KR20240036432A - Electronic device for displaying image and method for controlling the same - Google Patents

Abstract

A wearable electronic device is disclosed. A wearable electronic device of the present disclosure includes a camera, a display, at least one sensor, a memory, and at least one processor operatively connected to the camera, the display, the at least one sensor, and the memory, wherein the at least one processor is configured to: Obtain an image containing at least one object, perform preprocessing to compensate for the distortion area of the image generated based on the camera, based on information related to the type of camera, and perform preprocessing related to the distance of the object included in the image. At least one of a default matrix, first information input by the user, second information acquired through at least one sensor related to the user, or third information obtained through at least one sensor related to the surrounding environment is stored in the memory. Enter the stored deep learning model, obtain a matrix for adjusting the distance of at least one object included in the image from the deep learning model, and use the matrix to adjust the distance of at least one object included in the preprocessed image, An image with the distance of at least one object adjusted is displayed on the display, and the deep learning model adjusts the distance of the object corresponding to at least one of user information or surrounding environment information, and at least one of user information or surrounding environment information. It may be learned through the input of at least one matrix for

Description

Electronic device for displaying images and method for controlling the same {ELECTRONIC DEVICE FOR DISPLAYING IMAGE AND METHOD FOR CONTROLLING THE SAME}

Embodiments of the present disclosure relate to an electronic device that displays an image and a method of controlling the same.

As electronic and communication technologies develop, electronic devices can be miniaturized and lightweight to the point where they can be used without significant inconvenience even when worn on the user's body. For example, wearable electronic devices such as head-mounted devices (HMD), smart watches (or bands), contact lens-type devices, ring-type devices, glove-type devices, shoe-type devices, or clothing-type devices are commercialized. there is. Since wearable electronic devices are worn directly on the body, portability and user accessibility can be improved.

Visual see-through head-mounted display (VST-HMD) is a head-mounted electronic device that has a goggle-like shape. A head-mounted electronic device is a device worn on the user's head or face and can provide the user with information about objects in the form of images or text in at least a portion of the user's field of view.

After wearing a VST-HMD type wearable electronic device, the user is physically disconnected from the outside (closed-view), but can experience pure virtual reality (VR) rendered through the internal display. In addition, this type of electronic device transmits live video collected through a front-mounted camera to the internal display in real time, providing users with real-space-based augmented reality (AR) or mixed reality (AR). mixed reality (MR) experiences can also be provided.

The above information may be provided as background art for the purpose of aiding understanding of the present disclosure. No claim or determination is made as to whether any of the foregoing can be applied as prior art to the present disclosure.

According to one embodiment, a wearable electronic device may include a camera, a display, at least one sensor, a memory, and at least one processor operatively connected to the camera, the display, the at least one sensor, and the memory. .

According to one embodiment, the at least one processor may acquire an image including at least one object through the camera.

According to one embodiment, the at least one processor may perform preprocessing to compensate for a distortion area of the image generated based on the camera, based on information related to the type of the camera.

According to one embodiment, the at least one processor includes a default matrix related to the distance of an object included in an image, first information input by the user, and second information obtained through the at least one sensor in relation to the user. At least one of third information acquired through the at least one sensor related to information or the surrounding environment may be input to the deep learning model stored in the memory.

According to one embodiment, the at least one processor may obtain a matrix for adjusting the distance of the at least one object included in the image from the deep learning model.

According to one embodiment, the at least one processor may adjust the distance of the at least one object included in the preprocessed image using the matrix.

According to one embodiment, the at least one processor may display an image in which the distance of the at least one object is adjusted on the display.

According to one embodiment, the deep learning model inputs at least one of user information or surrounding environment information, and at least one matrix for adjusting the distance of an object respectively corresponding to at least one of the user information or the surrounding environment information. It may have been learned through .

According to one embodiment, the at least one processor may preprocess the image by compensating for a distortion area based on the type of lens included in the camera.

According to one embodiment, the deep learning model may include a plurality of sub-deep learning models each corresponding to a plurality of information.

According to one embodiment, the deep learning model each corresponds to at least one piece of information input to the deep learning model among the first information, the second information, and the third information among the plurality of sub-deep learning models. At least one sub deep learning model can be confirmed.

According to one embodiment, the deep learning model may obtain the matrix by sequentially using the at least one sub deep learning model.

According to one embodiment, the deep learning model includes a first sub deep learning model corresponding to the first information, a second sub deep learning model corresponding to the second information, and a third sub deep learning model corresponding to the third information. May include deep learning models.

According to one embodiment, the deep learning model, based on the first information being input to the deep learning model, output data of the first sub deep learning model to the second sub deep learning model or the third sub deep learning model. It can be used as input data for deep learning models.

According to one embodiment, the deep learning model uses the output data of the second sub-deep learning model as input data of the third sub-deep learning model, based on the second information being input to the deep learning model. You can.

According to one embodiment, the first information may include at least one of age, gender, height, vision, or BMI input by the user.

According to one embodiment, the deep learning model may include at least one first sub deep learning model each corresponding to at least one of age, gender, height, vision, or BMI input by the user.

According to one embodiment, the second information may include at least one of the inter-pupil distance or eye color acquired by the at least one sensor.

According to one embodiment, the deep learning model includes at least one second sub-deep learning model each corresponding to at least one of an inter-pupillary distance or an eye color acquired by the at least one sensor in relation to the user. can do.

According to one embodiment, the third information may include at least one of brightness, GPS, horizontal information, or inertial information acquired by the at least one sensor in relation to the surrounding environment.

According to one embodiment, the deep learning model includes at least one third sub-deep signal, each corresponding to at least one of brightness, GPS, horizontal information, or inertial information acquired by the at least one sensor in relation to the surrounding environment. May include a learning model.

According to one embodiment, the deep learning model may be learned using a default distance table related to the distance of at least one object included in the image.

According to one embodiment, the at least one processor may obtain the matrix by further inputting the default distance table to the deep learning model.

According to one embodiment, the default matrix may not change the default distance table.

According to one embodiment, the at least one processor may adjust the distance of the at least one object included in the pre-processed image by adjusting pixel values of the pre-processed image based on the matrix.

According to one embodiment, a method of controlling a wearable electronic device may include acquiring an image including at least one object through a camera.

According to one embodiment, a method of controlling a wearable electronic device may include preprocessing the image based on information related to the type of the camera.

According to one embodiment, a method of controlling a wearable electronic device includes a default matrix related to the distance of an object included in an image, first information input by a user, and second information obtained through at least one sensor related to the user. It may include inputting at least one of information or third information acquired through the at least one sensor related to the surrounding environment into a deep learning model stored in a memory.

According to one embodiment, a method of controlling a wearable electronic device may include obtaining a matrix for adjusting the distance of the at least one object included in the image from the deep learning model.

According to one embodiment, a method of controlling a wearable electronic device may include an operation of adjusting the distance of the at least one object included in the preprocessed image using the matrix.

According to one embodiment, a method of controlling a wearable electronic device may include displaying an image in which the distance of the at least one object is adjusted on a display.

According to one embodiment, the deep learning model inputs at least one of user information or surrounding environment information, and at least one matrix for adjusting the distance of an object respectively corresponding to at least one of the user information or the surrounding environment information. It may have been learned through .

According to one embodiment, the preprocessing operation may preprocess the image by compensating for a distortion area based on the type of lens included in the camera.

According to one embodiment, the deep learning model may include a plurality of sub-deep learning models each corresponding to a plurality of information.

According to one embodiment, the operation of acquiring the matrix is performed on at least one piece of information input to the deep learning model among the first information, the second information, and the third information among the plurality of sub-deep learning models. At least one corresponding sub deep learning model can be confirmed.

According to one embodiment, the operation of acquiring the matrix may be performed by sequentially using the at least one sub deep learning model.

According to one embodiment, the deep learning model includes a first sub deep learning model corresponding to the first information, a second sub deep learning model corresponding to the second information, and a third sub deep learning model corresponding to the third information. May include deep learning models.

According to one embodiment, the operation of acquiring the matrix is based on the first information being input to the deep learning model, and the output data of the first sub-deep learning model is divided into the second sub-deep learning model or the first sub-deep learning model. 3 It can be used as input data for a sub-deep learning model.

According to one embodiment, the operation of acquiring the matrix is based on the second information being input to the deep learning model, and the output data of the second sub-deep learning model is converted into input data of the third sub-deep learning model. It can be used as

According to one embodiment, the first information may include at least one of age, gender, height, vision, or BMI input by the user.

According to one embodiment, the deep learning model may include at least one first sub deep learning model each corresponding to at least one of age, gender, height, vision, or BMI input by the user.

According to one embodiment, the second information may include at least one of the inter-pupil distance or eye color acquired by the at least one sensor.

According to one embodiment, the deep learning model includes at least one second sub-deep learning model each corresponding to at least one of an inter-pupillary distance or an eye color acquired by the at least one sensor in relation to the user. can do.

According to one embodiment, the third information may include at least one of brightness, GPS, horizontal information, or inertial information acquired by the at least one sensor in relation to the surrounding environment.

According to one embodiment, the deep learning model includes at least one third sub-deep signal, each corresponding to at least one of brightness, GPS, horizontal information, or inertial information acquired by the at least one sensor in relation to the surrounding environment. May include a learning model.

According to one embodiment, the deep learning model may be learned using a default distance table related to the distance of at least one object included in the image.

According to one embodiment, the operation of acquiring the matrix may include obtaining the matrix by further inputting the default distance table to the deep learning model.

According to one embodiment, the default matrix may not change the default distance table.

According to one embodiment, the operation of adjusting the distance of the at least one object included in the preprocessed image includes adjusting the pixel value of the preprocessed image based on the matrix. The distance of the at least one object can be adjusted.

1 is a block diagram of an electronic device in a network environment according to an embodiment.
Figure 2 is a perspective view of an electronic device, according to one embodiment.
FIG. 3A is a first perspective view for explaining the internal configuration of an electronic device, according to an embodiment.
FIG. 3B is a second perspective view for explaining the internal configuration of an electronic device, according to an embodiment.
Figure 4 is an exploded perspective view of an electronic device, according to one embodiment.
5 is a perspective view of an electronic device according to embodiments of the present disclosure.
FIG. 6 is a flowchart illustrating an operation of adjusting the distance of an object included in a 3D image of a wearable electronic device according to an embodiment of the present disclosure.
FIG. 7 is a diagram for explaining a configuration based on the function of a wearable electronic device according to an embodiment of the present disclosure.
FIG. 8 is a diagram illustrating an operation of adjusting the distance of an object included in a 3D image based on the 3D image mode of a wearable electronic device according to an embodiment of the present disclosure.
Figure 9 is a diagram for explaining a deep learning model according to an embodiment of the present disclosure.
Figure 10 is a diagram for specifically explaining a deep learning model according to an embodiment of the present disclosure.
Figure 11 is a diagram for explaining a method of learning a deep learning model according to an embodiment of the present disclosure.
FIG. 12 is a diagram illustrating an operation of adjusting the distance of an object included in a 3D image of a wearable electronic device according to an embodiment of the present disclosure.
FIG. 13 is a diagram illustrating an operation of adjusting the distance of an object included in a 3D image based on a plurality of information of a wearable electronic device according to an embodiment of the present disclosure.

1 is a block diagram of an electronic device 101 in a network environment 100, according to one embodiment. Referring to FIG. 1, in the network environment 100, the electronic device 101 communicates with the electronic device 102 through a first network 198 (e.g., a short-range wireless communication network) or a second network 199. It is possible to communicate with at least one of the electronic device 104 or the server 108 through (e.g., a long-distance wireless communication network). According to one embodiment, the electronic device 101 may communicate with the electronic device 104 through the server 108. According to one embodiment, the electronic device 101 includes a processor 120, a memory 130, an input module 150, an audio output module 155, a display module 160, an audio module 170, and a sensor module ( 176), interface 177, connection terminal 178, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196 , or may include an antenna module 197. In some embodiments, at least one of these components (eg, the connection terminal 178) may be omitted or one or more other components may be added to the electronic device 101. In some embodiments, some of these components (e.g., sensor module 176, camera module 180, or antenna module 197) are integrated into one component (e.g., display module 160). It can be.

The processor 120, for example, executes software (e.g., program 140) to operate at least one other component (e.g., hardware or software component) of the electronic device 101 connected to the processor 120. It can be controlled and various data processing or calculations can be performed. According to one embodiment, as at least part of data processing or computation, the processor 120 stores commands or data received from another component (e.g., sensor module 176 or communication module 190) in volatile memory 132. The commands or data stored in the volatile memory 132 can be processed, and the resulting data can be stored in the non-volatile memory 134. According to one embodiment, the processor 120 includes a main processor 121 (e.g., a central processing unit or an application processor) or an auxiliary processor 123 that can operate independently or together (e.g., a graphics processing unit, a neural network processing unit ( It may include a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor). For example, if the electronic device 101 includes a main processor 121 and a secondary processor 123, the secondary processor 123 may be set to use lower power than the main processor 121 or be specialized for a designated function. You can. The auxiliary processor 123 may be implemented separately from the main processor 121 or as part of it.

The auxiliary processor 123 may, for example, act on behalf of the main processor 121 while the main processor 121 is in an inactive (e.g., sleep) state, or while the main processor 121 is in an active (e.g., application execution) state. ), together with the main processor 121, at least one of the components of the electronic device 101 (e.g., the display module 160, the sensor module 176, or the communication module 190) At least some of the functions or states related to can be controlled. According to one embodiment, co-processor 123 (e.g., image signal processor or communication processor) may be implemented as part of another functionally related component (e.g., camera module 180 or communication module 190). there is. According to one embodiment, the auxiliary processor 123 (eg, neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models. Artificial intelligence models can be created through machine learning. For example, such learning may be performed in the electronic device 101 itself on which the artificial intelligence model is performed, or may be performed through a separate server (e.g., server 108). Learning algorithms may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but It is not limited. An artificial intelligence model may include multiple artificial neural network layers. Artificial neural networks include deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), restricted boltzmann machine (RBM), belief deep network (DBN), bidirectional recurrent deep neural network (BRDNN), It may be one of deep Q-networks or a combination of two or more of the above, but is not limited to the examples described above. In addition to hardware structures, artificial intelligence models may additionally or alternatively include software structures.

The memory 130 may store various data used by at least one component (eg, the processor 120 or the sensor module 176) of the electronic device 101. Data may include, for example, input data or output data for software (e.g., program 140) and instructions related thereto. Memory 130 may include volatile memory 132 or non-volatile memory 134.

The program 140 may be stored as software in the memory 130 and may include, for example, an operating system 142, middleware 144, or application 146.

The input module 150 may receive commands or data to be used in a component of the electronic device 101 (e.g., the processor 120) from outside the electronic device 101 (e.g., a user). The input module 150 may include, for example, a microphone, mouse, keyboard, keys (eg, buttons), or digital pen (eg, stylus pen).

The sound output module 155 may output sound signals to the outside of the electronic device 101. The sound output module 155 may include, for example, a speaker or a receiver. Speakers can be used for general purposes such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. According to one embodiment, the receiver may be implemented separately from the speaker or as part of it.

The display module 160 can visually provide information to the outside of the electronic device 101 (eg, a user). The display module 160 may include, for example, a display, a hologram device, or a projector, and a control circuit for controlling the device. According to one embodiment, the display module 160 may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of force generated by the touch.

The audio module 170 can convert sound into an electrical signal or, conversely, convert an electrical signal into sound. According to one embodiment, the audio module 170 acquires sound through the input module 150, the sound output module 155, or an external electronic device (e.g., directly or wirelessly connected to the electronic device 101). Sound may be output through the electronic device 102 (e.g., speaker or headphone).

The sensor module 176 detects the operating state (e.g., power or temperature) of the electronic device 101 or the external environmental state (e.g., user state) and generates an electrical signal or data value corresponding to the detected state. can do. According to one embodiment, the sensor module 176 includes, for example, a gesture sensor, a gyro sensor, an air pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, It may include a temperature sensor, humidity sensor, or light sensor.

The interface 177 may support one or more designated protocols that can be used to connect the electronic device 101 directly or wirelessly with an external electronic device (eg, the electronic device 102). According to one embodiment, the interface 177 may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

The connection terminal 178 may include a connector through which the electronic device 101 can be physically connected to an external electronic device (eg, the electronic device 102). According to one embodiment, the connection terminal 178 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (eg, a headphone connector).

The haptic module 179 can convert electrical signals into mechanical stimulation (e.g., vibration or movement) or electrical stimulation that the user can perceive through tactile or kinesthetic senses. According to one embodiment, the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

The camera module 180 can capture still images and moving images. According to one embodiment, the camera module 180 may include one or more lenses, image sensors, image signal processors, or flashes.

The power management module 188 can manage power supplied to the electronic device 101. According to one embodiment, the power management module 188 may be implemented as at least a part of, for example, a power management integrated circuit (PMIC).

The battery 189 may supply power to at least one component of the electronic device 101. According to one embodiment, the battery 189 may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

Communication module 190 is configured to provide a direct (e.g., wired) communication channel or wireless communication channel between electronic device 101 and an external electronic device (e.g., electronic device 102, electronic device 104, or server 108). It can support establishment and communication through established communication channels. Communication module 190 operates independently of processor 120 (e.g., an application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module 190 is a wireless communication module 192 (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (e.g., : LAN (local area network) communication module, or power line communication module) may be included. Among these communication modules, the corresponding communication module is a first network 198 (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network 199 (e.g., legacy It may communicate with an external electronic device 104 through a telecommunication network such as a cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., LAN or WAN). These various types of communication modules may be integrated into one component (e.g., a single chip) or may be implemented as a plurality of separate components (e.g., multiple chips). The wireless communication module 192 uses subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module 196 within a communication network such as the first network 198 or the second network 199. The electronic device 101 can be confirmed or authenticated.

The wireless communication module 192 may support 5G networks after 4G networks and next-generation communication technologies, for example, NR access technology (new radio access technology). NR access technology provides high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and access to multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low latency). -latency communications)) can be supported. The wireless communication module 192 may support high frequency bands (eg, mmWave bands), for example, to achieve high data rates. The wireless communication module 192 uses various technologies to secure performance in high frequency bands, for example, beamforming, massive array multiple-input and multiple-output (MIMO), and full-dimensional multiplexing. It can support technologies such as input/output (FD-MIMO: full dimensional MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module 192 may support various requirements specified in the electronic device 101, an external electronic device (e.g., electronic device 104), or a network system (e.g., second network 199). According to one embodiment, the wireless communication module 192 supports Peak data rate (e.g., 20 Gbps or more) for realizing eMBB, loss coverage (e.g., 164 dB or less) for realizing mmTC, or U-plane latency (e.g., 164 dB or less) for realizing URLLC. Example: Downlink (DL) and uplink (UL) each of 0.5 ms or less, or round trip 1 ms or less) can be supported.

The antenna module 197 may transmit or receive signals or power to or from the outside (eg, an external electronic device). According to one embodiment, the antenna module 197 may include an antenna including a radiator made of a conductor or a conductive pattern formed on a substrate (eg, PCB). According to one embodiment, the antenna module 197 may include a plurality of antennas (eg, an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network such as the first network 198 or the second network 199 is connected to the plurality of antennas by, for example, the communication module 190. can be selected. Signals or power may be transmitted or received between the communication module 190 and an external electronic device through the at least one selected antenna. According to some embodiments, in addition to the radiator, other components (eg, radio frequency integrated circuit (RFIC)) may be additionally formed as part of the antenna module 197.

According to one embodiment, the antenna module 197 may form a mmWave antenna module. According to one embodiment, a mmWave antenna module includes: a printed circuit board, an RFIC disposed on or adjacent to a first side (e.g., bottom side) of the printed circuit board and capable of supporting a designated high frequency band (e.g., mmWave band); And a plurality of antennas (e.g., array antennas) disposed on or adjacent to the second side (e.g., top or side) of the printed circuit board and capable of transmitting or receiving signals in the designated high frequency band. can do.

At least some of the components are connected to each other through a communication method between peripheral devices (e.g., bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)) and signal ( (e.g. commands or data) can be exchanged with each other.

According to one embodiment, commands or data may be transmitted or received between the electronic device 101 and the external electronic device 104 through the server 108 connected to the second network 199. Each of the external electronic devices 102 or 104 may be of the same or different type as the electronic device 101. According to one embodiment, all or part of the operations performed in the electronic device 101 may be executed in one or more of the external electronic devices 102, 104, or 108. For example, when the electronic device 101 needs to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device 101 may perform the function or service instead of executing the function or service on its own. Alternatively, or additionally, one or more external electronic devices may be requested to perform at least part of the function or service. One or more external electronic devices that have received the request may execute at least part of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device 101. The electronic device 101 may process the result as is or additionally and provide it as at least part of a response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology can be used. The electronic device 101 may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device 104 may include an Internet of Things (IoT) device. Server 108 may be an intelligent server using machine learning and/or neural networks. According to one embodiment, the external electronic device 104 or server 108 may be included in the second network 199. The electronic device 101 may be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

FIG. 2 is a perspective view of an electronic device 200 (eg, the electronic device 101 of FIG. 1 ) according to an embodiment.

Referring to FIG. 2, the electronic device 200 is a wearable electronic device in the form of glasses, and a user can visually perceive surrounding objects or environments while wearing the electronic device 200. For example, the electronic device 200 may be a head-mounted device (HMD) or smart glasses that can provide images directly in front of the user's eyes. The configuration of the electronic device 200 of FIG. 2 may be completely or partially the same as the configuration of the electronic device 101 of FIG. 1 .

According to one embodiment, the electronic device 200 may include a housing 210 that forms the exterior of the electronic device 200. The housing 210 may provide a space where components of the electronic device 200 can be placed. For example, the housing 210 may include a lens frame 202 and at least one wearing member 203.

According to one embodiment, the electronic device 200 may include at least one display member 201 that can provide visual information to the user. For example, the display member 201 may include a module equipped with a lens, a display, a waveguide, and/or a touch circuit. According to one embodiment, the display member 201 may be formed to be transparent or translucent. According to one embodiment, the display member 201 may include a translucent glass material or a window member whose light transmittance can be adjusted by adjusting the coloring density. According to one embodiment, the display members 201 are provided as a pair and can be disposed to correspond to the user's left and right eyes, respectively, while the electronic device 200 is worn on the user's body.

According to one embodiment, the lens frame 202 may accommodate at least a portion of the display member 201. For example, the lens frame 202 may surround at least a portion of the edge of the display member 201. According to one embodiment, the lens frame 202 may position at least one of the display members 201 to correspond to the user's eye. According to one embodiment, the lens frame 202 may be a rim of a general eyeglass structure. According to one embodiment, the lens frame 202 may include at least one closed curve surrounding the display member 201.

According to one embodiment, the wearing member 203 may extend from the lens frame 202. For example, the wearing member 203 extends from an end of the lens frame 202 and, together with the lens frame 202, can be supported or positioned on the user's body (eg, ears). According to one embodiment, the wearing member 203 may be rotatably coupled to the lens frame 202 through a hinge structure 229. According to one embodiment, the wearing member 203 may include an inner side 231c configured to face the user's body and an outer side 231d opposite the inner side.

According to one embodiment, the electronic device 200 may include a hinge structure 229 configured to fold the wearing member 203 with respect to the lens frame 202. The hinge structure 229 may be disposed between the lens frame 202 and the wearing member 203. When not wearing the electronic device 200, the user can fold the wearing member 203 so that part of it overlaps the lens frame 202 and carry or store it.

FIG. 3A is a first perspective view for explaining the internal configuration of an electronic device according to various embodiments. FIG. 3B is a second perspective view for explaining the internal configuration of an electronic device according to various embodiments. 4 is an exploded perspective view of an electronic device according to various embodiments.

3A to 4, the electronic device 200 includes components (e.g., at least one circuit board 241 (e.g., printed circuit board (PCB)), printed board assembly (PBA)) accommodated in the housing 210. , flexible PCB (FPCB) or rigid-flexible PCB (RFPCB)), at least one battery 243, at least one speaker module 245, at least one power delivery structure 246, and camera module 250) may include. The configuration of the housing 210 in FIGS. 3A and 3B may be the same in whole or in part as the configuration of the display member 201, lens frame 202, wearing member 203, and hinge structure 229 in FIG. 2. there is.

According to one embodiment, the electronic device 200 uses the camera module 250 (e.g., the camera module 180 of FIG. 1) to detect the direction in which the user is looking or the electronic device 200 is oriented (e.g., - Acquire and/or recognize a visual image of an object or environment in the Y direction, and use an external electronic device (e.g., the first network 198 or the second network 199 in FIG. 1) through a network. Information about an object or environment may be provided from the electronic devices 102 and 104 or the server 108 of FIG. 1 . In one embodiment, the electronic device 200 may provide information about an object or environment to the user in audio or visual form. The electronic device 200 may provide information about the provided object or environment to the user through the display member 201 in a visual form using a display module (e.g., the display module 160 of FIG. 1). For example, the electronic device 200 can implement augmented reality by implementing information about objects or the environment in a visual form and combining it with actual images of the user's surrounding environment.

According to one embodiment, the display member 201 has a first surface F1 facing the direction in which external light is incident (e.g., -Y direction) and a first surface F1 facing the direction opposite to the first surface F1 (e.g., +Y direction). direction) may include a second surface (F2) facing the direction. When the user is wearing the electronic device 200, at least a portion of the light or image incident through the first side F1 is directed to the second side of the display member 201 disposed to face the left and/or right eye of the user. It may pass through the face F2 and enter the user's left and/or right eye.

According to one embodiment, the lens frame 202 may include at least two or more frames. For example, the lens frame 202 may include a first frame 202a and a second frame 202b. According to one embodiment, when a user wears the electronic device 200, the first frame 202a is a frame that faces the user's face, and the second frame 202b is a frame in the first frame 202a. It may be a part of the lens frame 202 spaced apart in the user's gaze direction (eg, -Y direction).

According to one embodiment, the optical output module 211 may provide images and/or videos to the user. For example, the light output module 211 includes a display panel (not shown) capable of outputting an image, and a lens (not shown) that corresponds to the user's eyes and guides the image to the display member 201. can do. For example, a user may obtain an image output from the display panel of the light output module 211 through the lens of the light output module 211. According to one embodiment, the optical output module 211 may include a device configured to display various information. For example, the light output module 211 may be a liquid crystal display (LCD), a digital mirror device (DMD), a liquid crystal on silicon (LCoS), or an organic light emitting diode. It may include at least one of an organic light emitting diode (OLED) or a micro LED (micro light emitting diode, micro LED). According to one embodiment, when the light output module 211 and/or the display member 201 includes one of a liquid crystal display device, a digital mirror display device, or a silicon liquid crystal display device, the electronic device 200 emits light. It may include a light source that irradiates light to the display area of the output module 211 and/or the display member 201. According to one embodiment, when the light output module 211 and/or the display member 201 includes one of an organic light emitting diode or a micro LED, the electronic device 200 does not include a separate light source and provides light to the user. Virtual images can be provided.

According to one embodiment, at least a portion of the light output module 211 may be disposed within the housing 210. For example, the light output module 211 may be disposed on the wearing member 203 or the lens frame 202 to correspond to the user's right eye and left eye, respectively. According to one embodiment, the light output module 211 is connected to the display member 201 and can provide an image to the user through the display member 201. For example, the image output from the light output module 211 is incident on the display member 210 through the input optical member located at one end of the display member 201, and is located on at least a portion of the display member 210. It may be radiated toward the user's eyes through a waveguide and output optical member. According to one embodiment, the waveguide may be made of glass, plastic, or polymer, and may include a nanopattern formed on one inner or outer surface, for example, a polygonal or curved grating structure. there is. According to one embodiment, the waveguide may include at least one of at least one diffractive element (e.g., a diffractive optical element (DOE), a holographic optical element (HOE)) or a reflective element (e.g., a reflective mirror). .

According to one embodiment, the circuit board 241 may include components for driving the electronic device 200. For example, the circuit board 241 may include at least one integrated circuit chip, such as the processor 120, memory 130, power management module 188, or communication module of FIG. 1. At least one of (190) may be provided in the integrated circuit chip. According to one embodiment, the circuit board 241 may be disposed within the wearing member 203 of the housing 210. According to one embodiment, the circuit board 241 may be electrically connected to the battery 243 through the power transmission structure 246. According to one embodiment, the circuit board 241 is connected to the flexible printed circuit board 205, and electronic components of the electronic device (e.g., the optical output module 211, An electrical signal may be transmitted to the camera module 250 and the light emitting unit. According to one embodiment, the circuit board 241 may be an interposer board.

According to various embodiments, flexible printed circuit board 205 may extend from circuit board 241 across hinge structure 229 and into the interior of lens frame 202. It may be disposed at least partially around the display member 201.

According to one embodiment, the battery 243 (e.g., the battery 189 in FIG. 1) is a component of the electronic device 200 (e.g., the optical output module 211, the circuit board 241, and the speaker module 245). , the microphone module 247, and/or the camera module 250), and may supply power to components of the electronic device 200.

According to one embodiment, at least a portion of the battery 243 may be disposed on the wearing member 203. According to one embodiment, the battery 243 may be disposed adjacent to the ends 203a and 203b of the wearing member 203. For example, the battery 243 may include a first battery 243a disposed at the first end 203a of the wearing member 203 and a second battery 243b disposed at the second end 203b. there is.

According to various embodiments, the speaker module 245 (e.g., the audio module 170 or the sound output module 155 of FIG. 1) may convert an electrical signal into sound. At least a portion of the speaker module 245 may be disposed within the wearing member 203 of the housing 210. According to one embodiment, the speaker module 245 may be located within the wearing member 203 to correspond to the user's ears. According to one embodiment (eg, FIG. 3A), the speaker module 245 may be disposed on the circuit board 241. For example, the speaker module 245 may be disposed between the circuit board 241 and the inner case (eg, the inner case 231 in FIG. 4). According to one embodiment (eg, FIG. 3B), the speaker module 245 may be placed next to the circuit board 241. For example, the speaker module 245 may be disposed between the circuit board 241 and the battery 243.

According to one embodiment, the electronic device 200 may include a speaker module 245 and a connection member 248 connected to the circuit board 241. The connection member 248 may transmit at least a portion of the sound and/or vibration generated by the speaker module 245 to the circuit board 241. According to one embodiment, the connection member 248 may be formed integrally with the speaker module 245. For example, a portion extending from the speaker frame of the speaker module 245 may be interpreted as the connection member 248. According to one embodiment (eg, FIG. 3A), the connecting member 248 may be omitted. For example, when the speaker module 245 is disposed on the circuit board 241, the connecting member 248 may be omitted.

According to one embodiment, the power transmission structure 246 may transmit power from the battery 243 to an electronic component (eg, the optical output module 211) of the electronic device 200. For example, the power transmission structure 246 is electrically connected to the battery 243 and/or the circuit board 241, and the circuit board 241 outputs power received through the power transmission structure 246 as light. It can be passed to module 211.

According to one embodiment, the power transmission structure 246 may be configured to transmit power. For example, power delivery structure 246 may include a flexible printed circuit board or wire. For example, a wire may include a plurality of cables (not shown). In various embodiments, the shape of the power transmission structure 246 may be varied in consideration of the number and/or type of cables.

According to one embodiment, the microphone module 247 (e.g., the input module 150 and/or the audio module 170 in FIG. 1) may convert sound into an electrical signal. According to one embodiment, the microphone module 247 may be disposed on at least a portion of the lens frame 202. For example, at least one microphone module 247 may be disposed at the bottom (eg, toward the -X axis) and/or top (eg, toward the X axis) of the electronic device 200. According to one embodiment, the electronic device 200 may recognize the user's voice more clearly using voice information (e.g., sound) acquired from at least one microphone module 247. For example, the electronic device 200 may distinguish between voice information and ambient noise based on the acquired voice information and/or additional information (eg, low-frequency vibration of the user's skin and bones). For example, the electronic device 200 can clearly recognize the user's voice and perform a function to reduce surrounding noise (eg, noise canceling).

According to one embodiment, the camera module 250 can capture still images and/or moving images. The camera module 250 may include at least one of a lens, at least one image sensor, an image signal processor, or a flash. According to one embodiment, the camera module 250 may be disposed within the lens frame 202 and around the display member 201.

According to one embodiment, the camera module 250 may include at least one first camera module 251. According to one embodiment, the first camera module 251 may photograph the user's eye (eg, pupil) or gaze trajectory. For example, the first camera module 251 may photograph a reflection pattern of light emitted by the light emitting unit to the user's eyes. For example, the light emitting unit may emit light in the infrared band for tracking the gaze trajectory using the first camera module 251. For example, the light emitting unit may include an IR LED. According to one embodiment, the processor (eg, processor 120 in FIG. 1) may adjust the position of the virtual image projected on the display member 201 so that it corresponds to the direction in which the user's pupils are gazing. According to one embodiment, the first camera module 251 may include a global shutter (GS) type camera, and the user can use a plurality of first camera modules 251 of the same standard and performance. The trajectory of one's eyes or gaze can be tracked.

According to various embodiments, the first camera module 251 periodically or aperiodically transmits information (e.g., trajectory information) related to the trajectory of the user's eyes or gaze to a processor (e.g., the processor 120 of FIG. 1). It can be sent to . According to one embodiment, when the first camera module 251 detects that the user's gaze has changed based on the trajectory information (e.g., the eyes move more than the reference value while the head is not moving), the first camera module 251 processes the trajectory information into a processor. It can be sent to .

According to one embodiment, the camera module 250 may include a second camera module 253. According to one embodiment, the second camera module 253 can capture external images. According to one embodiment, the second camera module 253 may be a global shutter type camera or a rolling shutter (RS) type camera. According to one embodiment, the second camera module 253 may capture an external image through the second optical hole 223 formed in the second frame 202b. For example, the second camera module 253 may include a high-resolution color camera and may be a high resolution (HR) or photo video (PV) camera. Additionally, the second camera module 253 may provide an auto focus function (AF) and an optical image stabilizer (OIS) function.

According to various embodiments (not shown), the electronic device 200 may include a flash (not shown) located adjacent to the second camera module 253. For example, a flash (not shown) may provide light to increase brightness (e.g., illuminance) around the electronic device 200 when acquiring an external image of the second camera module 253, in a dark environment, Difficulties in obtaining images due to mixing of various light sources and/or reflection of light can be reduced.

According to one embodiment, the camera module 250 may include at least one third camera module 255. According to one embodiment, the third camera module 255 may capture the user's movements through the first optical hole 221 formed in the lens frame 202. For example, the third camera module 255 may capture a user's gestures (eg, hand movements). The third camera module 255 and/or the first optical hole 221 are located at both sides of the lens frame 202 (e.g., the second frame 202b), for example, in the X direction. (For example, it may be disposed at both ends of the second frame 202b). According to one embodiment, the third camera module 255 may be a global shutter (GS) type camera. For example, the third camera module 255 is a camera that supports 3DoF (degrees of freedom) or 6DoF, which can provide 360-degree spatial (e.g. omnidirectional), position recognition, and/or movement recognition. You can. According to one embodiment, the third camera module 255 is a stereo camera that uses a plurality of global shutter cameras of the same standard and performance to perform a movement path tracking function (simultaneous localization and mapping, SLAM) and user movement recognition. It can perform its function. According to one embodiment, the third camera module 255 may include an infrared (IR) camera (eg, a time of flight (TOF) camera, or a structured light camera). For example, the IR camera may be operated as at least a part of a sensor module (eg, sensor module 176 in FIG. 1) for detecting the distance to the subject.

According to one embodiment, at least one of the first camera module 251 or the third camera module 255 may be replaced with a sensor module (eg, the sensor module 176 in FIG. 1). For example, the sensor module may include at least one of a vertical cavity surface emitting laser (VCSEL), an infrared sensor, and/or a photodiode. For example, the photo diode may include a positive intrinsic negative (PIN) photo diode, or an avalanche photo diode (APD). The photo diode may be referred to as a photo detector or photo sensor.

According to one embodiment, at least one of the first camera module 251, the second camera module 253, or the third camera module 255 may include a plurality of camera modules (not shown). For example, the second camera module 253 consists of a plurality of lenses (e.g., wide-angle and telephoto lenses) and image sensors and is disposed on one side (e.g., the side facing the -Y axis) of the electronic device 200. It can be. For example, the electronic device 200 may include a plurality of camera modules, each with different properties (e.g., angle of view) or function, and change the angle of view of the camera module based on the user's selection and/or trajectory information. You can control it to do so. For example, at least one of the plurality of camera modules may be a wide-angle camera, and at least another one may be a telephoto camera.

According to various embodiments, the processor (e.g., processor 120 of FIG. 1) acquires information using at least one of a gesture sensor, a gyro sensor, or an acceleration sensor of a sensor module (e.g., sensor module 176 of FIG. 1). Movement of the electronic device 200 using the information of one electronic device 200 and the user's motion (e.g., approach of the user's body to the electronic device 200) obtained using the third camera module 255. And/or the user's movement may be determined. According to one embodiment, in addition to the described sensor, the electronic device 200 includes a magnetic (geomagnetic) sensor capable of measuring orientation using a magnetic field and magnetoelectric force, and/or movement information (e.g., movement) using the strength of the magnetic field. It may include a Hall sensor capable of acquiring direction or movement distance. For example, the processor may determine the movement of the electronic device 200 and/or the user's movement based on information obtained from a magnetic (geomagnetic) sensor and/or a hall sensor.

According to various embodiments (not shown), the electronic device 200 may perform an input function (eg, touch and/or pressure sensing function) that allows interaction with the user. For example, components configured to perform touch and/or pressure sensing functions (e.g., touch sensors and/or pressure sensors) may be disposed on at least a portion of the wearing member 203 . The electronic device 200 may control a virtual image output through the display member 201 based on information acquired through the components. For example, sensors related to touch and/or pressure sensing functions may be resistive type, capacitive type, electro-magnetic type (EM), or optical type. ) can be configured in various ways, such as: According to one embodiment, components configured to perform the touch and/or pressure sensing function may be completely or partially identical to the configuration of the input module 150 of FIG. 1 .

According to one embodiment, the electronic device 200 may include a reinforcing member 260 disposed in the internal space of the lens frame 202 and formed to have a higher rigidity than the rigidity of the lens frame 202.

According to one embodiment, the electronic device 200 may include a lens structure 270. The lens structure 270 may refract at least a portion of light. For example, lens structure 270 may be a prescription lens with a specified refractive power. According to one embodiment, the housing 210 may include a hinge cover 227 that can conceal a portion of the hinge structure 229. Another part of the hinge structure 229 may be accommodated or hidden between the inner case 231 and the outer case 233, which will be described later.

According to various embodiments, the wearing member 203 may include an inner case 231 and an outer case 233. The inner case 231 is, for example, a case configured to face or directly contact the user's body, and may be made of a material with low thermal conductivity, for example, synthetic resin. According to one embodiment, the inner case 231 may include an inner side (eg, inner side 231c in FIG. 2 ) that faces the user's body. The outer case 233 includes, for example, a material capable of at least partially transferring heat (eg, a metal material), and may be coupled to face the inner case 231 . According to one embodiment, the outer case 233 may include an outer side opposite to the inner side 231c (eg, the outer side 231d in FIG. 2). In one embodiment, at least one of the circuit board 241 or the speaker module 245 may be accommodated in a space separate from the battery 243 within the wearing member 203. In the illustrated embodiment, the inner case 231 includes a first case 231a including a circuit board 241 and/or a speaker module 245, and a second case 231b containing the battery 243. It may include, and the outer case 233 may include a third case 233a coupled to face the first case 231a, and a fourth case 233b coupled to face the second case 231b. You can. For example, the first case 231a and the third case 233a are combined (hereinafter referred to as 'first case portions 231a, 233a') to accommodate the circuit board 241 and/or the speaker module 245. The battery 243 can be accommodated by combining the second case 231b and the fourth case 233b (hereinafter referred to as 'second case parts 231b, 233b').

According to one embodiment, the first case portions 231a and 233a are rotatably coupled to the lens frame 202 through a hinge structure 229, and the second case portions 231b and 233b are connected to the connection structure 235. It can be connected or mounted to the ends of the first case portions 231a and 233a. In one embodiment, the portion of the connection structure 235 that is in contact with the user's body may be made of a material with low thermal conductivity, for example, an elastomer material such as silicone, polyurethane, or rubber. , parts that are not in contact with the user's body may be made of a material with high thermal conductivity (e.g., a metal material). For example, when heat is generated from the circuit board 241 or the battery 243, the connection structure 235 blocks heat from being transferred to the part that is in contact with the user's body and dissipates heat through the part that is not in contact with the user's body. It can be dispersed or released. According to one embodiment, the part of the connection structure 235 configured to contact the user's body can be interpreted as part of the inner case 231, and the part of the connection structure 235 that does not contact the user's body can be interpreted as a part of the outer case ( 233). According to one embodiment (not shown), the first case 231a and the second case 231b are formed as one piece without the connection structure 235, and the third case 233a and the fourth case 233b are connected. It can be configured as an integral piece without the structure 235. According to various embodiments, other components (e.g., the antenna module 197 of FIG. 1) may be further included in addition to the components shown, and a network (e.g., the first antenna module 197 of FIG. 1) may be used using the communication module 190. Information about an object or environment can be provided from an external electronic device (e.g., the electronic devices 102 and 104 or the server 108 of FIG. 1) through the first network 198 or the second network 199. .

5 is another perspective view of an electronic device according to various embodiments of the present disclosure.

Referring to FIG. 5, the electronic device 400 may be a head-mounted device (HMD) that can provide an image in front of the user's eyes. The configuration of the electronic device 400 of FIG. 5 may be identical in whole or in part to the configuration of the electronic device 200 of FIG. 2 .

According to various embodiments, the electronic device 400 includes housings 410, 420, which can form the exterior of the electronic device 400 and provide a space where components of the electronic device 400 can be placed. 430) may include

According to various embodiments, the electronic device 400 may include a first housing 410 that can surround at least a portion of the user's head. According to one embodiment, the first housing 410 may include a first surface 400a facing the outside of the electronic device 400 (eg, -Y direction).

According to various embodiments, the first housing 410 may surround at least a portion of the internal space (I). For example, the first housing 410 may include a second surface 400b facing the internal space I of the electronic device 400 and a third surface 400c opposite the second surface 400b. You can. According to one embodiment, the first housing 410 may be combined with the third housing 430 to form a closed curve shape surrounding the internal space (I).

According to various embodiments, the first housing 410 may accommodate at least some of the components of the electronic device 400. For example, an optical output module (e.g., the optical output module 211 in FIG. 3A), a circuit board (e.g., the circuit board 241 in FIG. 3A, and the speaker module 245) are connected to the first housing 410. can be placed within.

According to various embodiments, the electronic device 400 may include one display member 440 corresponding to the left eye and the right eye. The display member 440 may be disposed in the first housing 410 . The configuration of the display member 440 of FIG. 5 may be completely or partially the same as the configuration of the display member 201 of FIG. 2 .

According to various embodiments, the electronic device 400 may include a second housing 420 that can be placed on the user's face. According to one embodiment, the second housing 420 may include a fourth surface 400d that may at least partially face the user's face. According to one embodiment, the fourth surface 400d may be a surface in a direction (eg, +Y direction) toward the internal space I of the electronic device 400. According to one embodiment, the second housing 420 may be combined with the first housing 410.

According to various embodiments, the electronic device 400 may include a third housing 430 that can be placed on the back of the user's head. According to one embodiment, the third housing 430 may be combined with the first housing 410. According to one embodiment, the third housing 430 may accommodate at least some of the components of the electronic device 400. For example, a battery (eg, battery 243 in FIG. 3A) may be disposed within the third housing 430.

In order to increase the user's overall experience, environment, and usability of head-mounted wearable electronic devices, the sensations felt and experienced by the user in VR (virtual reality), AR (augmented reality), and MR (mixed reality) spaces must be compared to those in the real world. It may be necessary to be as similar as possible.

According to one embodiment, when the distance between the user and the virtual object included in the 3D image recognized by the user is longer or shorter than the distance between the user and the real object in the real space, experience satisfaction is reduced due to symptoms such as motion sickness, dizziness, and vomiting. It can result in a large fall, which can lead to safety issues such as collisions and falls.

In addition, since the degree of misrecognition varies depending on the user's physical condition or surrounding environment, the following description describes an operation of adjusting the distance of a virtual object included in a 3D image by reflecting user information and surrounding environment information.

The technical challenges sought to be achieved in this document are not limited to the technical challenges mentioned above, and other technical challenges not mentioned can be clearly understood by those skilled in the art of this document from the description below. There will be.

FIG. 6 is a flowchart illustrating an operation of adjusting the distance of an object included in a 3D image of a wearable electronic device according to an embodiment of the present disclosure.

Referring to FIG. 6, a wearable electronic device (e.g., the electronic device 101 of FIG. 1, the processor 120 of FIG. 1, the electronic device 200 of FIG. 2, or the electronic device 400 of FIG. 5) has 610 In operation, an image including at least one object may be acquired. According to one embodiment, the image may be a 2D image and/or a 3D image. Below, for convenience of explanation, the description is made in terms of 3D images, but an embodiment of the present disclosure can also be applied to 2D images.

According to one embodiment, the electronic device acquires an image of the external environment through a camera (e.g., the camera module 180 of FIG. 1) and generates a 3D image including at least one object based on the acquired image. can do. According to one embodiment, at least one object included in the 3D image may include a virtual object corresponding to a real object placed in real space.

According to one embodiment, in operation 620, the wearable electronic device may perform preprocessing to compensate for the distortion area of the image generated based on the camera based on information related to the type of camera.

According to one embodiment, a wearable electronic device may preprocess a 3D image by compensating for a distortion area created by a lens based on the type of lens included in the camera.

In this way, by preprocessing the 3D image to normalize it regardless of the type of camera, misrecognition based on differences in the type of camera mounted on the wearable electronic device can be reduced.

According to one embodiment, the wearable electronic device may modify the default matrix before inputting the deep learning model to compensate for the distortion area based on the type of lens included in the camera.

According to one embodiment, in operation 630, the wearable electronic device displays a default matrix related to the distance of an object included in the 3D image, first information input by the user, and at least one sensor (e.g., in FIG. 1) related to the user. At least one of the second information acquired through the sensor module 176 or the third information acquired through at least one sensor in relation to the surrounding environment information may be input to the deep learning model stored in the memory.

According to one embodiment, the default matrix related to the distance of the object included in the 3D image may be a warping matrix for correcting the difference between the distance of the object included in the 3D image and the distance misperceived by the user.

According to one embodiment, the deep learning model learns through input of at least one of user information or surrounding environment information, and at least one matrix for adjusting the distance of an object respectively corresponding to at least one of user information or surrounding environment information. It may have happened. According to one embodiment, the matrix input when learning a deep learning model may be a matrix statistically obtained based on information of multiple users or information on the surrounding environment.

An example of learning a deep learning model according to an embodiment will be described with reference to FIG. 11.

According to one embodiment, a deep learning model may include a plurality of sub-deep learning models each corresponding to a plurality of pieces of information. According to one embodiment, the deep learning model includes at least one sub deep learning model each corresponding to at least one piece of information input to the deep learning model among the first information, second information, and third information among the plurality of sub deep learning models. You can check the model and obtain a matrix by sequentially using at least one sub deep learning model.

According to one embodiment, the matrix obtained as output data of the deep learning model may be a warping matrix obtained by adjusting the default matrix based on user information or surrounding environment information.

According to one embodiment, the deep learning model may be a multi-stage regression structure that sequentially uses a plurality of sub-deep learning models.

According to one embodiment, the deep learning model includes a first sub deep learning model corresponding to first information, a second sub deep learning model corresponding to second information, and a third sub deep learning model corresponding to third information. can do.

According to one embodiment, the deep learning model may obtain a matrix by first using a deep learning model corresponding to information that is easy to obtain by a wearable electronic device. According to one embodiment, the deep learning model is a sub-deep learning model corresponding to first information that a wearable electronic device can easily obtain, a sub-deep learning model corresponding to second information, and a sub-deep learning model corresponding to third information. You can obtain the matrix by using .

According to one embodiment, the deep learning model uses the output data of the first sub-deep learning model as input to the second sub-deep learning model or the third sub-deep learning model, based on the first information being input to the deep learning model. It can be used as data.

According to one embodiment, when the first information and the second information are input to the deep learning model, the deep learning model may use the output data obtained through the first sub deep learning model as input data of the second sub deep learning model. there is.

According to one embodiment, when the first information and the third information are input to the deep learning model, the deep learning model may use the output data obtained through the first sub deep learning model as input data of the third sub deep learning model. there is.

According to one embodiment, the deep learning model may use the output data of the second sub deep learning model as input data of the third sub deep learning model based on the second information being input to the deep learning model.

According to one embodiment, when the second information and the third information are input to the deep learning model, the deep learning model may use the output data obtained through the second sub deep learning model as input data of the third sub deep learning model. there is.

According to one embodiment, when the first to third information is input to the deep learning model, the deep learning model uses the output data obtained through the first sub deep learning model as input data of the second sub deep learning model, and , the output data of the second sub deep learning model can be used as input data of the third deep learning model.

According to one embodiment, the structure of a deep learning model will be described with reference to FIGS. 9 and 10.

According to one embodiment, the deep learning model orders the sub-deep learning model based on the order of experimentally obtained information that can improve the accuracy of distance correction (e.g., information that has a large influence on distance correction). You can decide.

In the above, the first information input by the user, the second information acquired through at least one sensor in relation to the user, or the third information obtained through at least one sensor in relation to the surrounding environment information are each one piece of information. , Although it has been explained that there is one sub deep learning model corresponding to each piece of information, the first to third pieces of information may each include a plurality of pieces of information. According to one embodiment, when there is a plurality of first to third information, there may also be a plurality of sub deep learning models corresponding to each piece of information.

According to one embodiment, the first information may include at least one of age, gender, height, vision, or body mass index (BMI) input by the user. According to one embodiment, the deep learning model may include at least one first sub deep learning model each corresponding to at least one of age, gender, height, vision, or BMI input by the user. According to one embodiment, each of the at least one first sub deep learning model inputs at least one of age, gender, height, vision, or BMI, and a matrix respectively corresponding to at least one of age, gender, height, vision, or BMI. It may be something learned.

According to one embodiment, the deep learning model may include at least one first sub deep learning model corresponding to at least two of age, gender, height, vision, or BMI.

According to one embodiment, the wearable electronic device may input user information through gesture input through the hand or an external device, or input through a linked external device (e.g., input through the touch screen of a smartphone).

According to one embodiment, the inter-pupil distance or eye color that can be obtained through a sensor may also be included in the first information when input by the user, and the degree of astigmatism and body weight input by the user may also be included.

According to one embodiment, the second information may include at least one of inter-pupil distance, pupil size, or eye color acquired by at least one sensor.

According to one embodiment, the deep learning model includes at least one second sub-deep learning model each corresponding to at least one of the inter-pupil distance, pupil size, or eye color acquired by at least one sensor in relation to the user. may include. According to one embodiment, each of the at least one second sub deep learning model corresponds to at least one of inter-pupil distance, pupil size, or eye color, and at least one of inter-pupil distance, pupil size, or eye color. It may have been learned by inputting a matrix.

According to one embodiment, the deep learning model may include at least one second sub-deep learning model corresponding to the inter-pupil distance, pupil size, and eye color.

According to one embodiment, the wearable electronic device may acquire at least one of the user's inter-pupil distance, pupil size, or eye color through an image sensor (e.g., a camera).

According to one embodiment, the third information may include at least one of brightness, GPS, horizontal information, or inertial information acquired by at least one sensor in relation to the surrounding environment.

According to one embodiment, the deep learning model includes at least one third sub deep learning model each corresponding to at least one of brightness, GPS, horizontal information, or inertial information acquired by at least one sensor in relation to the surrounding environment. It can be included. According to one embodiment, each of the at least one third sub deep learning model inputs a matrix respectively corresponding to at least one of brightness, GPS, horizontal information, or inertial information, and at least one of brightness, GPS, horizontal information, or inertial information. It may be something learned.

According to one embodiment, the deep learning model may include at least one third sub deep learning model corresponding to at least two of brightness, GPS, horizontal information, or inertial information.

According to one embodiment, the wearable electronic device may acquire at least one of brightness, GPS, horizontal information, or inertial information through an illuminance sensor, a GPS sensor, or a gyro sensor.

According to one embodiment, the deep learning model may be learned using a default distance table related to the distance of at least one object included in the 3D image. According to one embodiment, the default distance table may be a 1:1 mapping of real distances and virtual distances.

According to one embodiment, when the deep learning model is learned using the default distance table as input data, the electronic device may obtain a matrix by further inputting the default distance table to the deep learning model.

According to one embodiment, the default matrix may not change the default distance table. According to one embodiment, the default matrix is set by the manufacturer based on the camera type of the wearable electronic device, and lens distortion correction may be performed.

According to one embodiment, an example of learning a deep learning model will be described in more detail with reference to FIG. 11.

According to one embodiment, in operation 640, the wearable electronic device may obtain a matrix for adjusting the distance of at least one object included in the image from the deep learning model.

According to one embodiment, in operation 650, the wearable electronic device may adjust the distance of at least one object included in the preprocessed image using a matrix.

According to one embodiment, the wearable electronic device may adjust the distance of at least one object included in the preprocessed image by multiplying (e.g., overlapping) the preprocessed image with a matrix obtained as output data from a deep learning model.

According to one embodiment, the wearable electronic device may adjust the distance of the at least one object included in the pre-processed image by adjusting pixel values of the pre-processed image based on a matrix.

According to one embodiment, in operation 660, the wearable electronic device may display an image in which the distance of at least one object is adjusted on a display (eg, the display module 160 of FIG. 1).

According to one embodiment, the wearable electronic device may acquire user information as input from the user or through a sensor before using the wearable electronic device or whenever necessary and correct the warping matrix through a deep learning model. According to one embodiment, a wearable electronic device can automatically acquire surrounding environment information in real time through a sensor according to changes in the surrounding environment and correct the warping matrix through a deep learning model.

In this way, distance correction of objects included in different 3D images for each user can be performed based on user information and surrounding environment information to reduce user misperception of distance. This can improve the usability of wearable electronic devices and user satisfaction.

FIG. 7 is a diagram for explaining a configuration based on the function of a wearable electronic device according to an embodiment of the present disclosure.

Referring to FIG. 7, a wearable electronic device (e.g., the electronic device 101 of FIG. 1, the processor 120 of FIG. 1, the electronic device 200 of FIG. 2, or the electronic device 400 of FIG. 5) is used by a user. Information input module 710 (e.g., camera module 180 of FIG. 1, communication module 190 of FIG. 1, or sensor module 176 of FIG. 1), user information measurement module 711 (e.g., Internal data (e.g., user information) can be obtained through the sensor module 176. According to one embodiment, the wearable electronic device detects the sensor through the scene determination module 712 (e.g., the sensor module 176 in FIG. 1) and the inertial signal detection module 713 (e.g., the sensor module 176 in FIG. 1). External data (e.g., surrounding environment information) can be acquired.

According to one embodiment, the internal data may include at least one of age, gender, race, height, inter-pupillary distance, pupil size, visual acuity, eye color, or BMI index.

According to one embodiment, the external data may include at least one of ambient brightness, whether the wearable electronic device is used indoors or outdoors, GPS information, horizontal information of the wearable electronic device, or inertial information of the wearable electronic device.

According to one embodiment, the wearable electronic device provides user information (e.g., age, gender, race, height, inter-pupillary distance, pupil size, visual acuity) manually entered by the user through the user information input module 710 whenever necessary. , at least one of eye color or BMI index) can be acquired and the stored user information can be updated.

According to one embodiment, the wearable electronic device automatically acquires user information (e.g., at least one of inter-pupil distance, pupil size, and eye color) whenever necessary through the user information measurement module 711, and stores the stored user information. It can be updated.

According to one embodiment, the wearable electronic device automatically acquires surrounding environment information (e.g., at least one of brightness or indoor/outdoor) and updates the stored surrounding environment information whenever the user needs it through the scene determination module 712. can do.

According to one embodiment, the wearable electronic device automatically detects surrounding environment information (e.g., GPS information, horizontal level of the wearable electronic device) whenever necessary through the inertial signal detection module 713. information, inertial information of the wearable electronic device) can be acquired and the stored surrounding environment information can be updated.

According to one embodiment, the wearable electronic device processes user information acquired through the user information input module 710 and the user information measurement module 711 into the internal data processing module 720 (e.g., the processor 120 of FIG. 1). It can be passed on. According to one embodiment, the internal data processing module 720 may identify and store delivered user information by type.

According to one embodiment, the wearable electronic device processes user information acquired through the scene determination module 712 and the inertial signal detection module 713 into the external data processing module 721 (e.g., the processor 120 of FIG. 1). It can be delivered. According to one embodiment, the external data processing module 721 may identify and store the transmitted surrounding environment information by type.

According to one embodiment, the wearable electronic device uses the user information of the internal data processing module 720 and the surrounding environment information of the external data processing module 721 through a depth calibration module 730 (e.g., the processor of FIG. 1 (120)). The depth correction module 730 may adjust the distance of an object included in a 3D image using a warping matrix obtained using a deep learning model stored in a memory (e.g., memory 130 in FIG. 1).

According to one embodiment, a wearable electronic device may display a 3D image with the distance of an object adjusted on a head-mounted display module 740 (e.g., the display module 160 of FIG. 1).

FIG. 8 is a diagram illustrating an operation of adjusting the distance of an object included in a 3D image based on the 3D image mode of a wearable electronic device according to an embodiment of the present disclosure.

Referring to FIG. 8, a wearable electronic device (e.g., the electronic device 101 of FIG. 1, the processor 120 of FIG. 1, the electronic device 200 of FIG. 2, or the electronic device 400 of FIG. 5) uses VR. It is possible to determine (810) whether it is a mode, AR mode, or MR mode.

According to one embodiment, the VR mode may be a mode that displays a virtual space different from the real space. According to one embodiment, the AR mode is a mode that displays virtual objects in a real space visible through a transparent display or an image of a real space captured through a camera, but the real objects and virtual objects included in the real space do not interact. It may be a mode that does not work. According to one embodiment, the MR mode is a mode that displays virtual objects in a real space visible through a transparent display or an image of a real space captured through a camera, but is a mode in which real objects and virtual objects included in the real space interact with each other. It could be a mode.

According to one embodiment, the wearable electronic device may collect information for depth calibration (820). According to one embodiment, the wearable electronic device may collect internal data (eg, user information) through the user information input module 710 and the user information measurement module 711 of FIG. 7. According to one embodiment, the wearable electronic device may collect external data (eg, surrounding environment information) through the scene determination module 712 and the inertial signal detection module 713.

According to one embodiment, the wearable electronic device may derive a depth correction matrix (830). According to one embodiment, the wearable electronic device uses a deep learning model to create a depth correction matrix (e.g., warping matrix) to correct the difference between the distance of an object included in a 3D image and the distance misperceived by the user. It can be obtained. According to one embodiment, the operation of acquiring a depth correction matrix through a deep learning model will be described in more detail with reference to FIG. 10 below.

According to one embodiment, the operation of collecting depth correction information (820) and the operation of deriving the depth correction matrix (830) may be performed with personalization for each user.

According to one embodiment, in the AR mode or MR mode, the wearable electronic device uses an external camera sensor (e.g., the camera module 180 of FIG. 1) to capture an image in the raw domain (e.g., bayer). Can be obtained (840). According to one embodiment, the image may be a 2D image or a 3D image.

According to one embodiment, a wearable electronic device may perform distance warping (850) using a depth correction matrix (e.g., warping matrix). According to one embodiment, when the image is a 2D image, the wearable electronic device may generate a 3D image in which the distance of the object is adjusted using a depth correction matrix.

According to one embodiment, when the image is a 3D image, the wearable electronic device may adjust the distance of an object included in the 3D image using a depth correction matrix.

According to one embodiment, the wearable electronic device may perform preprocessing to correct distortion of a 2D image or 3D image based on the type of lens included in the external camera before performing distance correction using a depth correction matrix.

According to one embodiment, when the acquired 3D image is a raw domain, the wearable electronic device processes the 3D image through an image signal processor (ISP) 860 to display it on a head-mounted display (HMD) and then displays the 3D image on the HMD. It can be displayed (870).

According to one embodiment, in VR mode, the wearable electronic device acquires an image in the rgb domain using an external camera sensor (e.g., the camera module 180 of FIG. 1) ( 841) You can. According to one embodiment, the image may be a 2D image or a 3D image.

According to one embodiment, the wearable electronic device may perform distance correction (distance warping) 851 using a depth correction matrix. According to one embodiment, when the image is a 2D image, the wearable electronic device may generate a 3D image in which the distance of the object is adjusted using a depth correction matrix.

According to one embodiment, when the image is a 3D image, the wearable electronic device may adjust the distance of an object included in the 3D image using a depth correction matrix.

According to one embodiment, the wearable electronic device may perform preprocessing to correct distortion of a 2D image or 3D image based on the type of lens included in the external camera before performing distance correction using a depth correction matrix.

According to one embodiment, when the acquired 3D image is in the RGB domain, the wearable electronic device may display the 3D image on the HMD (870).

In this way, the depth correction matrix obtained based on the deep learning model can be used in the same way in both the raw domain and the rgb domain.

Figure 9 is a diagram for explaining a deep learning model according to an embodiment of the present disclosure.

Referring to FIG. 9, a wearable electronic device (e.g., the electronic device 101 of FIG. 1, the processor 120 of FIG. 1, the electronic device 200 of FIG. 2, or the electronic device 400 of FIG. 5) has a default A default distance table (910) and internal/external information (e.g., user information, surrounding environment information) (911) are used to create multi-stage regression networks for warping (e.g., deep learning models). , can be input to the depth correction module 730) 920 of FIG. 7. For convenience of explanation, the multi-stage regression network 920 for warping is hereinafter referred to as a deep learning model 920.

According to one embodiment, the default distance table 910 may be one in which real distances and virtual distances are mapped at a 1:1 ratio, such as 1m when rendering a real distance of 1m and 5m when rendering a real distance of 5m.

According to one embodiment, the wearable electronic device may obtain a fixed distance table 930 by multiplying the default distance table 910 with a warping matrix obtained through a deep learning model 920.

According to one embodiment, the structure of the deep learning model 920 will be described with reference to FIG. 10 below.

Figure 10 is a diagram for specifically explaining a deep learning model according to an embodiment of the present disclosure.

Referring to FIG. 10, a wearable electronic device (e.g., the electronic device 101 of FIG. 1, the processor 120 of FIG. 1, the electronic device 200 of FIG. 2, or the electronic device 400 of FIG. 5) uses a deep A default distance table 910 and internal/external information (e.g., user information, surrounding environment information) 911 may be input to the learning model 920.

According to one embodiment, the deep learning model 920 may be configured with a multi-stage regression structure that optimizes the warping matrix for each stage. According to one embodiment, performing optimization of the warping matrix step by step means using a plurality of sub deep learning models 921, 922, and 923 included in the deep learning model 920. This may mean obtaining an improved warping matrix by reflecting the information input to (921, 922, and 923), respectively.

According to one embodiment, the internal/external information 911 may include first information 1020, second information 1021, and third information 1022. According to one embodiment, the first information 1020 to the third information 1022 include age, gender, race, height, weight, inter-pupil distance, visual acuity, eye color, pupil size, BMI index, brightness, indoor/ It may include whether it is outdoors, GPS information, horizontal information of the wearable electronic device, or inertial information of the wearable electronic device. According to one embodiment, the first information 1020 to the third information 1022 include age, gender, race, height, weight, inter-pupil distance, visual acuity, eye color, pupil size, BMI index, brightness, indoor/ Each may include at least two of outdoor status, GPS information, horizontal information of the wearable electronic device, or inertial information of the wearable electronic device.

According to one embodiment, the deep learning model 920 inputs the default warping matrix 1010 and the first information 1020 into the first sub deep learning model 921 to generate the first warping matrix 1030. ) can be obtained.

According to one embodiment, the default warping matrix 1010 is a reference matrix (identity matrix).

According to one embodiment, the default warping matrix 1010 is provided by a camera manufacturer of a wearable electronic device, and may be a warping matrix that generates a distance table that corrects lens distortion of the camera, etc.

According to one embodiment, the default warping matrix 1010 may be a warping matrix statistically derived through large-scale data collection. For example, it can be obtained based on a large amount of distance misidentification data collected for learning a deep learning model. For example, the default warping matrix 1010 may be a warping matrix that adjusts the distance based on the average of the depth/distance misperception felt by users (the entire data collection group) when wearing a wearable electronic device. there is.

According to one embodiment, the deep learning model 920 inputs the first warping matrix 1030 and the second information 1021 into the second sub deep learning model 922 to obtain the second warping matrix 1031. You can.

According to one embodiment, the deep learning model 920 inputs the second warping matrix 1031 and the third information 1022 into the third sub deep learning model 923 to obtain the third warping matrix 1032. You can.

According to one embodiment, in FIG. 10, it is shown and explained that there are three sub-deep learning models (921, 922, and 923), but there may be two or less or four or more.

According to one embodiment, the wearable electronic device multiplies the finally obtained third warping matrix 1032 with the input default distance table 910 to obtain a fixed distance table 930 as output data. You can.

According to one embodiment, the deep learning model 920 is based on information that the user can easily provide and information that has a high degree of influence on the accuracy of distance recognition among the plurality of sub-deep learning models (921, 922, and 923). The corresponding sub deep learning model can be used first. For example, a wearable electronic device may use a sub-deep learning model (e.g., a first sub-deep learning model) corresponding to at least one of age, gender, race, height, weight, inter-pupillary distance, visual acuity, and BMI index that can be easily input from the user. The warping matrix can be obtained by first using the deep learning model (921).

According to one embodiment, the wearable electronic device collects the user's biometric information (e.g., inter-pupil distance, eye color, and pupil size) additionally required during distance correction through at least one sensor (e.g., in FIG. 1) without user intervention. It can be acquired through the sensor module 176 and input into the deep learning model 920.

According to one embodiment, the wearable electronic device may acquire surrounding environment information such as brightness and inertial information of the wearable electronic device through at least one sensor without user intervention and input them into the deep learning model 920.

According to one embodiment, when the user inputs additional user information or a change in the surrounding environment is detected, the wearable electronic device may input the input user information or measured surrounding environment information into the deep learning model 920. .

According to one embodiment, the deep learning model 920 is based on the order of the existing input information and the newly input information, and adds the warping matrix of the stage before the newly input information to the sub deep learning model corresponding to the newly input information. You can enter it.

According to one embodiment, the deep learning model 920 inputs the warping matrix output from the sub-deep learning model corresponding to the newly input information into the sub-deep learning model at a stage after the newly input information to reflect the newly input information. The final warping matrix can be obtained.

In this way, by using each piece of information sequentially, the warping matrix can be finely adjusted to obtain a warping matrix optimized for the user.

Because of this, the wearable electronic device of the present disclosure can provide an improved distance perception experience to the user.

Figure 11 is a diagram for explaining a method of learning a deep learning model according to an embodiment of the present disclosure.

Referring to FIG. 11, a wearable electronic device (e.g., the electronic device 101 of FIG. 1, the processor 120 of FIG. 1, the electronic device 200 of FIG. 2, or the electronic device 400 of FIG. 5) is used by a user. The deep learning model 1120 can be trained by inputting information and/or surrounding environment information 1110 and a matrix 1111 corresponding to user information and/or surrounding environment information into the deep learning model 1120.

According to one embodiment, the wearable electronic device stores information (e.g., at least one of age, gender, race, height, weight, inter-pupillary distance, visual acuity, eye color, pupil size, or BMI index) of the first user and the first user. A matrix for correcting the user's degree of misrecognition can be input into the deep learning model (1120). According to one embodiment, the wearable electronic device may collect surrounding environment information (e.g., brightness, whether indoor/outdoor, GPS information, horizontal information of the wearable electronic device, or inertial information of the wearable electronic device) when the first user uses the wearable electronic device. at least one of) can be input into the deep learning model 1120.

According to one embodiment, the electronic device may input a matrix manually adjusting the information of multiple users, surrounding environment information, and the degree of distance misrecognition of the user into the deep learning model 1120.

According to one embodiment, the wearable electronic device may further use the default warping matrix as input data for training the deep learning model 1120.

According to one embodiment, the wearable electronic device may further use the default distance table as input data for training the deep learning model 1120.

According to one embodiment, the wearable electronic device may further use information about the type of camera as input data for training the deep learning model 1120.

FIG. 12 is a diagram illustrating an operation of adjusting the distance of an object included in a 3D image of a wearable electronic device according to an embodiment of the present disclosure.

Referring to FIG. 12, a wearable electronic device (e.g., the electronic device 101 of FIG. 1, the processor 120 of FIG. 1, the electronic device 200 of FIG. 2, or the electronic device 400 of FIG. 5) has 1210 In motion, wearing a visual see-through (VST) head-mounted display (HMD) can be detected.

According to one embodiment, in operation 1211, the wearable electronic device may check whether calibration is requested. According to one embodiment, if the user requests correction or if the user's motion is detected to be out of sync with the displayed 3D image, the wearable electronic device may confirm that correction is requested.

According to one embodiment, if it is confirmed that correction has been requested (operation 1211 - Yes), in operation 1212, the wearable electronic device may check whether there is a calibration matrix. According to one embodiment, if there is a stored correction matrix, the wearable electronic device can confirm that the correction matrix exists. According to one embodiment, the stored correction matrix may be a default warping matrix.

According to one embodiment, if the correction matrix does not exist (operation 1212 - No), in operation 1213, the wearable electronic device may check whether manual information is updated. According to one embodiment, passive information may be user information entered by the user.

According to one embodiment, when the manual information is updated (operation 1213 - Yes), in operation 1214, the wearable electronic device may perform internal data processing. According to one embodiment, internal data may include user information input by the user or user information measured through a sensor.

According to one embodiment, a wearable electronic device may identify user information input by the user by type.

According to one embodiment, in operation 1215, the wearable electronic device may check changes in the surrounding environment. According to one embodiment, a wearable electronic device can check changes in the surrounding environment through a sensor.

According to one embodiment, if the manual information has not been updated (operation 1213 - No), the wearable electronic device proceeds to operation 1215 to check changes in the surrounding environment.

According to one embodiment, when the surrounding environment changes (operation 1215 - Yes), the wearable electronic device may perform external data processing in operation 1216. According to one embodiment, external data may include information on the surrounding environment measured through a sensor.

According to one embodiment, a wearable electronic device may identify surrounding environment information measured by a sensor by type.

According to one embodiment, if the surrounding environment has not changed (operation 1215 - No), the wearable electronic device may return to operation 1215 and continue to check for changes in the surrounding environment.

According to one embodiment, in operation 1217, the wearable electronic device may derive and store a calibration matrix. According to one embodiment, the wearable electronic device stores internal data and The correction matrix can be obtained by inputting external data.

According to one embodiment, in operation 1218, the wearable electronic device may perform distance correction (depth warping). According to one embodiment, a wearable electronic device may perform distance correction of a 3D image based on a correction matrix.

According to one embodiment, the 3D image on which distance correction has been performed may have input frame normalization performed in operation 1219. According to one embodiment, input frame normalization may be a preprocessing operation for 3D images. According to one embodiment, the wearable electronic device stores optics information (e.g., type of lens included in the camera, assembly) and factory calibration information (e.g., information in which lens distortion included in the camera has been corrected). You can use this to perform preprocessing of 3D images acquired through a camera.

According to one embodiment, in operation 1220, the electronic device may display a 3D image with adjusted distance information on a display (eg, the display module 160 of FIG. 1).

According to one embodiment, the wearable electronic device may return to operation 1215 after operation 1220 and monitor changes in the surrounding environment.

According to one embodiment, the wearable electronic device may measure user information through a sensor in operations 1220 and 1221. According to one embodiment, the wearable electronic device may proceed to operation 1214 to classify and store the measured user information by type.

According to one embodiment, if a correction matrix exists (operation 1212 - Yes), in operation 1222, the wearable electronic device may check whether calibration is necessary. According to one embodiment, if the wearable electronic device detects that the user has changed or that the user's movements are out of sync with the displayed 3D image, the wearable electronic device may determine that correction is necessary.

According to one embodiment, if correction is not necessary (operation 1222 - No), the wearable electronic device may proceed to operation 1218 and perform a distance correction operation using the stored warping matrix (e.g., default warping matrix).

According to one embodiment, if correction is necessary (operation 1222 - Yes), the wearable electronic device may proceed to operation 1213 to check whether user information has been manually input.

According to one embodiment, if correction is not requested (operation 1211 - No), the wearable electronic device may proceed to operation 1220 and display a 3D image.

According to one embodiment, in operation 1223, the wearable electronic device may detect that the VST HMD is not worn. According to one embodiment, when the wearable electronic device detects that the VST HMD is removed while displaying an image, it may end the process.

FIG. 13 is a diagram illustrating an operation of adjusting the distance of an object included in a 3D image based on a plurality of information of a wearable electronic device according to an embodiment of the present disclosure. For example, Figure 13 illustrates an operation of adjusting the distance of an object included in a 3D image in two dimensions based on age and inter-pupil distance among a plurality of pieces of information.

Referring to FIG. 13, a wearable electronic device (e.g., the electronic device 101 of FIG. 1, the processor 120 of FIG. 1, the electronic device 200 of FIG. 2, the electronic device 400 of FIG. 5, or the electronic device 400 of FIG. 9 The deep learning model 920) may perform correction based on the default warping matrix 1310 obtained based on previously collected data (black circle) for distance correction.

According to one embodiment, the wearable electronic device may perform correction of the warping matrix by using age information that is easy to obtain first, and then sequentially using the inter-pupillary distance that is difficult to obtain.

According to one embodiment, the wearable electronic device may obtain the first warping matrix 1330 by inputting the default warping matrix 1310 and the age information of user A into the sub-deep learning model corresponding to the age information. there is. According to one embodiment, the wearable electronic device obtains a second warping matrix 1331 by inputting the first warping matrix 1330 and the inter-pupil distance information of user A into a sub-deep learning model corresponding to the inter-pupil distance information. You can.

Accordingly, it can be confirmed that the second warping matrix 1331 is similar to the warping matrix 1320 corresponding to user A.

According to one embodiment, the wearable electronic device may obtain the third warping matrix 1350 by inputting the default warping matrix 1310 and the age information of user B into the sub-deep learning model corresponding to the age information. According to one embodiment, the wearable electronic device obtains the fourth warping matrix 1351 by inputting the third warping matrix 1350 and the inter-pupil distance information of user B into the sub-deep learning model corresponding to the inter-pupil distance information. You can.

Accordingly, it can be confirmed that the fourth warping matrix 1351 has a reduced error compared to the warping matrix 1340 corresponding to user B and the default warping matrix 1310.

According to one embodiment, if the wearable electronic device performs regression in multiple dimensions using more information, the error in the user's perception of the distance to the object in the 3D image may be reduced. According to one embodiment, the error in the user's perception of the distance to the object of the 3D image may be reduced even if the user, like user B, deviates greatly from the distribution of the majority of users.

According to one embodiment, a wearable electronic device (e.g., the electronic device 101 of FIG. 1, the processor 120 of FIG. 1, the electronic device 200 of FIG. 2, or the electronic device 400 of FIG. 5) uses a camera. (e.g., camera module 180 of FIG. 1, display (e.g., display module 160 of FIG. 1), at least one sensor (e.g., sensor module 176 of FIG. 1), memory (e.g., It may include a memory 130) and at least one processor (eg, processor 120 of FIG. 1) operatively connected to the camera, the display, the at least one sensor, and the memory.

According to one embodiment, the at least one processor may acquire an image including at least one object through the camera.

According to one embodiment, the at least one processor may perform preprocessing to compensate for a distortion area of the image generated based on the camera based on information related to the type of the camera.

According to one embodiment, the at least one processor includes a default matrix related to the distance of an object included in an image (e.g., the default warping matrix 1010 of FIG. 10), first information input by the user, and the user and In relation to this, at least one of second information acquired through the at least one sensor or third information obtained through the at least one sensor related to the surrounding environment may be input to the deep learning model stored in the memory.

According to one embodiment, the at least one processor may obtain a matrix for adjusting the distance of the at least one object included in the image from the deep learning model.

According to one embodiment, the at least one processor may adjust the distance of the at least one object included in the preprocessed image using the matrix.

According to one embodiment, the at least one processor may display an image in which the distance of the at least one object is adjusted on the display.

According to one embodiment, the deep learning model inputs at least one of user information or surrounding environment information, and at least one matrix for adjusting the distance of an object respectively corresponding to at least one of the user information or the surrounding environment information. It may have been learned through .

According to one embodiment, the at least one processor may preprocess the image by compensating for a distortion area based on the type of lens included in the camera.

According to one embodiment, the deep learning model may include a plurality of sub-deep learning models each corresponding to a plurality of information.

According to one embodiment, the deep learning model each corresponds to at least one piece of information input to the deep learning model among the first information, the second information, and the third information among the plurality of sub-deep learning models. At least one sub deep learning model can be confirmed.

According to one embodiment, the deep learning model may obtain the matrix by sequentially using the at least one sub deep learning model.

According to one embodiment, the deep learning model includes a first sub deep learning model corresponding to the first information, a second sub deep learning model corresponding to the second information, and a third sub deep learning model corresponding to the third information. May include deep learning models.

According to one embodiment, the deep learning model, based on the first information being input to the deep learning model, output data of the first sub deep learning model to the second sub deep learning model or the third sub deep learning model. It can be used as input data for deep learning models.

According to one embodiment, the deep learning model uses the output data of the second sub-deep learning model as input data of the third sub-deep learning model, based on the second information being input to the deep learning model. You can.

According to one embodiment, the first information may include at least one of age, gender, height, vision, or BMI input by the user.

According to one embodiment, the deep learning model may include at least one first sub deep learning model each corresponding to at least one of age, gender, height, vision, or BMI input by the user.

According to one embodiment, the second information may include at least one of the inter-pupil distance or eye color acquired by the at least one sensor.

According to one embodiment, the deep learning model includes at least one second sub-deep learning model each corresponding to at least one of an inter-pupillary distance or an eye color acquired by the at least one sensor in relation to the user. can do.

According to one embodiment, the third information may include at least one of brightness, GPS, horizontal information, or inertial information acquired by the at least one sensor in relation to the surrounding environment.

According to one embodiment, the deep learning model includes at least one third sub-deep signal, each corresponding to at least one of brightness, GPS, horizontal information, or inertial information acquired by the at least one sensor in relation to the surrounding environment. May include a learning model.

According to one embodiment, the deep learning model may be learned using a default distance table related to the distance of at least one object included in the image.

According to one embodiment, the at least one processor may obtain the matrix by further inputting the default distance table to the deep learning model.

According to one embodiment, the default matrix may not change the default distance table.

According to one embodiment, the at least one processor may adjust the distance of the at least one object included in the pre-processed image by adjusting pixel values of the pre-processed image based on the matrix.

According to one embodiment, a method of controlling a wearable electronic device may include acquiring an image including at least one object through a camera.

According to one embodiment, a method of controlling a wearable electronic device may include preprocessing the image based on information related to the type of the camera.

According to one embodiment, a method of controlling a wearable electronic device includes a default matrix related to the distance of an object included in an image, first information input by a user, and second information obtained through at least one sensor related to the user. It may include inputting at least one of information or third information acquired through the at least one sensor related to the surrounding environment into a deep learning model stored in a memory.

According to one embodiment, a method of controlling a wearable electronic device may include obtaining a matrix for adjusting the distance of the at least one object included in the image from the deep learning model.

According to one embodiment, a method of controlling a wearable electronic device may include adjusting the distance of the at least one object included in the preprocessed image using the matrix.

According to one embodiment, a method of controlling a wearable electronic device may include displaying an image in which the distance of the at least one object is adjusted on a display.

According to one embodiment, the deep learning model inputs at least one of user information or surrounding environment information, and at least one matrix for adjusting the distance of an object respectively corresponding to at least one of the user information or the surrounding environment information. It may have been learned through .

According to one embodiment, the preprocessing operation may preprocess the image by compensating for a distortion area based on the type of lens included in the camera.

According to one embodiment, the deep learning model may include a plurality of sub-deep learning models each corresponding to a plurality of information.

According to one embodiment, the operation of acquiring the matrix is performed on at least one piece of information input to the deep learning model among the first information, the second information, and the third information among the plurality of sub-deep learning models. At least one corresponding sub deep learning model can be confirmed.

According to one embodiment, the operation of acquiring the matrix may be performed by sequentially using the at least one sub deep learning model.

According to one embodiment, the deep learning model includes a first sub deep learning model corresponding to the first information, a second sub deep learning model corresponding to the second information, and a third sub deep learning model corresponding to the third information. May include deep learning models.

According to one embodiment, the operation of acquiring the matrix is based on the first information being input to the deep learning model, and the output data of the first sub-deep learning model is divided into the second sub-deep learning model or the first sub-deep learning model. 3 It can be used as input data for a sub-deep learning model.

According to one embodiment, the operation of acquiring the matrix is based on the second information being input to the deep learning model, and the output data of the second sub-deep learning model is converted into input data of the third sub-deep learning model. It can be used as

According to one embodiment, the first information may include at least one of age, gender, height, vision, or BMI input by the user.

According to one embodiment, the deep learning model may include at least one first sub deep learning model each corresponding to at least one of age, gender, height, vision, or BMI input by the user.

According to one embodiment, the second information may include at least one of the inter-pupil distance or eye color acquired by the at least one sensor.

According to one embodiment, the deep learning model includes at least one second sub-deep learning model each corresponding to at least one of an inter-pupillary distance or an eye color acquired by the at least one sensor in relation to the user. can do.

According to one embodiment, the third information may include at least one of brightness, GPS, horizontal information, or inertial information acquired by the at least one sensor in relation to the surrounding environment.

According to one embodiment, the deep learning model includes at least one third sub-deep signal, each corresponding to at least one of brightness, GPS, horizontal information, or inertial information acquired by the at least one sensor in relation to the surrounding environment. May include a learning model.

According to one embodiment, the deep learning model may be learned using a default distance table related to the distance of at least one object included in the image.

According to one embodiment, the operation of acquiring the matrix may include obtaining the matrix by further inputting the default distance table to the deep learning model.

According to one embodiment, the default matrix may not change the default distance table.

According to one embodiment, the operation of adjusting the distance of the at least one object included in the preprocessed image includes adjusting the pixel value of the preprocessed image based on the matrix. The distance of the at least one object can be adjusted.

Electronic devices according to embodiments disclosed in this document may be of various types. Electronic devices may include, for example, portable communication devices (e.g., smartphones), computer devices, portable multimedia devices, portable medical devices, cameras, wearable devices, or home appliances. Electronic devices according to embodiments of this document are not limited to the above-described devices.

The embodiments of this document and the terms used herein are not intended to limit the technical features described in this document to specific embodiments, and should be understood to include various changes, equivalents, or replacements of the embodiments. In connection with the description of the drawings, similar reference numbers may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of the above items, unless the relevant context clearly indicates otherwise. As used herein, “A or B”, “at least one of A and B”, “at least one of A or B”, “A, B or C”, “at least one of A, B and C”, and “A Each of phrases such as “at least one of , B, or C” may include any one of the items listed together in the corresponding phrase, or any possible combination thereof. Terms such as "first", "second", or "first" or "second" may be used simply to distinguish one component from another, and to refer to that component in other respects (e.g., importance or order) is not limited. One (e.g., first) component is said to be “coupled” or “connected” to another (e.g., second) component, with or without the terms “functionally” or “communicatively.” When mentioned, it means that any of the components can be connected to the other components directly (e.g. wired), wirelessly, or through a third component.

The term "module" used in embodiments of this document may include a unit implemented in hardware, software, or firmware, and is interchangeable with terms such as logic, logic block, component, or circuit, for example. It can be used as A module may be an integrated part or a minimum unit of the parts or a part thereof that performs one or more functions. For example, according to one embodiment, the module may be implemented in the form of an application-specific integrated circuit (ASIC).

One embodiment of the present document is one or more instructions stored in a storage medium (e.g., built-in memory 136 or external memory 138) that can be read by a machine (e.g., electronic device 101). It may be implemented as software (e.g., program 140) including these. For example, a processor (e.g., processor 120) of a device (e.g., electronic device 101) may call at least one command among one or more commands stored from a storage medium and execute it. This allows the device to be operated to perform at least one function according to the at least one instruction called. The one or more instructions may include code generated by a compiler or code that can be executed by an interpreter. A storage medium that can be read by a device may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' only means that the storage medium is a tangible device and does not contain signals (e.g. electromagnetic waves), and this term refers to cases where data is semi-permanently stored in the storage medium. There is no distinction between temporary storage cases.

According to one embodiment, the method according to the embodiments disclosed in this document may be provided and included in a computer program product. Computer program products are commodities and can be traded between sellers and buyers. The computer program product may be distributed in the form of a machine-readable storage medium (e.g. compact disc read only memory (CD-ROM)) or through an application store (e.g. Play StoreTM) or on two user devices (e.g. It can be distributed (e.g. downloaded or uploaded) directly between smart phones) or online. In the case of online distribution, at least a portion of the computer program product may be at least temporarily stored or temporarily created in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

According to one embodiment, each component (e.g., module or program) of the above-described components may include a single or plural entity, and some of the plurality of entities may be separately placed in other components. there is. According to one embodiment, one or more of the components or operations described above may be omitted, or one or more other components or operations may be added. Alternatively or additionally, multiple components (eg, modules or programs) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each component of the plurality of components in the same or similar manner as those performed by the corresponding component of the plurality of components prior to the integration. . According to embodiments, operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, or omitted. Alternatively, one or more other operations may be added.

Claims (20)

In wearable electronic devices,
camera;
display;
at least one sensor;
Memory; and
At least one processor operatively connected to the camera, the display, the at least one sensor, and the memory,
The at least one processor,
Obtaining an image including at least one object through the camera,
Based on information related to the type of the camera, preprocessing is performed to compensate for the distortion area of the image generated based on the camera,
A default matrix related to the distance of an object included in the image, first information input by the user, second information acquired through the at least one sensor in relation to the user, or the at least one sensor in relation to the surrounding environment. Input at least one of the third information obtained through the deep learning model stored in the memory,
Obtaining a matrix for adjusting the distance of the at least one object included in the image from the deep learning model,
Adjusting the distance of the at least one object included in the preprocessed image using the matrix,
Displaying an image with the distance of the at least one object adjusted on the display,
The deep learning model is learned through input of at least one of user information or surrounding environment information, and at least one matrix for adjusting the distance of an object respectively corresponding to at least one of the user information or the surrounding environment information. , wearable electronic devices.
According to paragraph 1,
The at least one processor,
A wearable electronic device that preprocesses the image by compensating for a distortion area based on the type of lens included in the camera.
According to claim 1 or 2,
The deep learning model is,
Contains a plurality of sub-deep learning models each corresponding to a plurality of information,
Confirming at least one sub deep learning model among the plurality of sub deep learning models, each corresponding to at least one piece of information input to the deep learning model among the first information, the second information, and the third information,
A wearable electronic device that obtains the matrix by sequentially using the at least one sub-deep learning model.
According to paragraph 3,
The deep learning model is,
Comprising a first sub deep learning model corresponding to the first information, a second sub deep learning model corresponding to the second information, and a third sub deep learning model corresponding to the third information,
Based on the first information being input to the deep learning model, output data of the first sub deep learning model is used as input data of the second sub deep learning model or the third sub deep learning model,
A wearable electronic device that uses output data of the second sub-deep learning model as input data of the third sub-deep learning model, based on the second information being input to the deep learning model.
According to paragraph 3,
The first information is,
Contains at least one of age, gender, height, vision, or BMI entered by the user,
The deep learning model is,
A wearable electronic device comprising at least one first sub-deep learning model each corresponding to at least one of age, gender, height, vision, or BMI input by the user.
According to paragraph 3,
The second information is,
Contains at least one of an inter-pupillary distance or an eye color obtained by the at least one sensor,
The deep learning model is,
A wearable electronic device comprising at least one second sub-deep learning model, each corresponding to at least one of an inter-pupillary distance or an eye color obtained by the at least one sensor in relation to the user.
According to paragraph 3,
The third information is,
Contains at least one of brightness, GPS, horizontal information, or inertial information obtained by the at least one sensor in relation to the surrounding environment,
The deep learning model is,
A wearable electronic device comprising at least one third sub deep learning model each corresponding to at least one of brightness, GPS, horizontal information, or inertial information acquired by the at least one sensor in relation to the surrounding environment.
According to any one of claims 1 to 7,
The deep learning model is,
It is further learned using a default distance table related to the distance of at least one object included in the image,
The at least one processor,
A wearable electronic device that obtains the matrix by further inputting the default distance table to the deep learning model.
According to clause 8,
The wearable electronic device, wherein the default matrix does not change the default distance table.
According to any one of claims 1 to 9,
The at least one processor,
A wearable electronic device that adjusts the distance of the at least one object included in the pre-processed image by adjusting pixel values of the pre-processed image based on the matrix.
In a method of controlling a wearable electronic device,
An operation of acquiring an image including at least one object through a camera;
preprocessing the image based on information related to the type of the camera;
A default matrix related to the distance of an object included in an image, first information input by the user, second information acquired through at least one sensor in relation to the user, or through the at least one sensor in relation to the surrounding environment. An operation of inputting at least one of the obtained third information into a deep learning model stored in a memory;
Obtaining a matrix for adjusting the distance of the at least one object included in the image from the deep learning model;
adjusting the distance of the at least one object included in the preprocessed image using the matrix; and
An operation of displaying an image with the distance of the at least one object adjusted on a display,
The deep learning model is learned through input of at least one of user information or surrounding environment information, and at least one matrix for adjusting the distance of an object respectively corresponding to at least one of the user information or the surrounding environment information. ,Control method of wearable electronic devices.
According to clause 11,
The preprocessing operation is,
A control method for a wearable electronic device that preprocesses the image by compensating for a distortion area based on the type of lens included in the camera.
According to claim 11 or 12,
The deep learning model is,
Contains a plurality of sub-deep learning models each corresponding to a plurality of information,
The operation of obtaining the matrix is,
Confirming at least one sub deep learning model among the plurality of sub deep learning models, each corresponding to at least one piece of information input to the deep learning model among the first information, the second information, and the third information,
A control method for a wearable electronic device that obtains the matrix by sequentially using the at least one sub-deep learning model.
According to clause 13,
The deep learning model is,
Comprising a first sub deep learning model corresponding to the first information, a second sub deep learning model corresponding to the second information, and a third sub deep learning model corresponding to the third information,
The operation of obtaining the matrix is,
Based on the first information being input to the deep learning model, output data of the first sub deep learning model is used as input data of the second sub deep learning model or the third sub deep learning model,
A control method of a wearable electronic device, based on the second information being input to the deep learning model, using the output data of the second sub-deep learning model as input data of the third sub-deep learning model.
According to clause 13,
The first information is,
Contains at least one of age, gender, height, vision, or BMI entered by the user,
The deep learning model is,
A method of controlling a wearable electronic device, comprising at least one first sub-deep learning model each corresponding to at least one of age, gender, height, vision, or BMI input by the user.
According to clause 13,
The second information is,
Contains at least one of an inter-pupillary distance or an eye color obtained by the at least one sensor,
The deep learning model is,
A control method for a wearable electronic device, comprising at least one second sub-deep learning model each corresponding to at least one of an inter-pupillary distance or an eye color obtained by the at least one sensor in relation to the user.
According to clause 13,
The third information is,
Contains at least one of brightness, GPS, horizontal information, or inertial information obtained by the at least one sensor in relation to the surrounding environment,
The deep learning model is,
A control method of a wearable electronic device, including at least one third sub-deep learning model each corresponding to at least one of brightness, GPS, horizontal information, or inertial information acquired by the at least one sensor in relation to the surrounding environment. .
According to any one of claims 11 to 17,
The deep learning model is,
It is further learned using a default distance table related to the distance of at least one object included in the image,
The operation of obtaining the matrix is,
A control method of a wearable electronic device that obtains the matrix by further inputting the default distance table to the deep learning model.
According to clause 18,
The default matrix does not change the default distance table.
According to any one of claims 11 to 19,
The operation of adjusting the distance of the at least one object included in the preprocessed image includes:
A control method for a wearable electronic device that adjusts the distance of the at least one object included in the pre-processed image by adjusting pixel values of the pre-processed image based on the matrix.

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