WO2021096339A1 - Procédé de transformation d'image - Google Patents

Procédé de transformation d'image Download PDF

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Publication number
WO2021096339A1
WO2021096339A1 PCT/KR2020/095147 KR2020095147W WO2021096339A1 WO 2021096339 A1 WO2021096339 A1 WO 2021096339A1 KR 2020095147 W KR2020095147 W KR 2020095147W WO 2021096339 A1 WO2021096339 A1 WO 2021096339A1
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Prior art keywords
image
images
area
processor
received
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PCT/KR2020/095147
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English (en)
Korean (ko)
Inventor
이용수
진희경
Original Assignee
주식회사 날비컴퍼니
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Publication of WO2021096339A1 publication Critical patent/WO2021096339A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/73—Deblurring; Sharpening
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/77—Retouching; Inpainting; Scratch removal
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10004—Still image; Photographic image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]

Definitions

  • the present disclosure relates to a method for transforming an image, and more particularly, to provide a method for transforming an image by extracting an object from one or more images and transforming at least one of the extracted object or a background region.
  • the surrounding area may be distorted as a person or object included in an image is corrected, or an image/image may appear unnatural due to the correction.
  • the present disclosure provides an image modification method and a computer program stored in a recording medium for solving the above problems.
  • the present disclosure may be implemented in a variety of ways, including a method or a computer program stored in a readable storage medium.
  • an image modification method performed by at least one processor includes receiving one or more images, extracting a first object included in the received one or more images, and a first object Or transforming at least one of the first background regions, generating an image including at least one of the transformed first object or the first background region, and outputting the generated image, the first background region Is an area other than the first object in the received one or more images.
  • the transforming at least one of the first object or the first background area includes performing out-of-focus (OUT-OF-FOCUS) for the first background area and adjusting the intensity of out-focus. Includes steps.
  • the step of generating an image including at least one of the deformed first object or the first background area may include, when the received one or more images are moving pictures, the first background area according to the movement of the first object. And generating the out-of-focus video.
  • the received one or more images are moving pictures
  • generating a moving picture in which the first background area is out of focus according to the movement of the first object is performed.
  • the transforming at least one of the first object or the first background area includes deleting the first background area from the received one or more images, and thus at least one first image including the first object is Including the step of generating, the step of generating the image including at least one of the transformed first object or the first background area, receiving at least one second image and generating at least one second image Compositing to the first image.
  • the transforming at least one of the first object or the first background region includes deleting a first object from one or more received images, a productive hostile neural network model for image inpainting ( Generating an image for a region in which a first object has been deleted from one or more images through a Generative Adversarial Network; GAN), and applying an image for a region in which the first object has been deleted to the received one or more images.
  • a productive hostile neural network model for image inpainting Generating an image for a region in which a first object has been deleted from one or more images through a Generative Adversarial Network; GAN
  • GAN Generative Adversarial Network
  • the step of generating an image including at least one of the deformed first object or the first background area is, when the received one or more images is a video, and when the first object is moved in the video, the image is moved. And generating a video from which the first object has been deleted.
  • the transforming at least one of the first object or the first background area includes receiving a user input for the first object, transforming the first object based on the received user input, and an image Through a productive adversarial neural network model for inpainting, generating an image of a blank area or a distortion area of one or more images formed according to the deformation of a first object, and receiving an image of a blank area or a distortion area of one or more images And applying to one or more images.
  • the transforming at least one of the first object or the first background region includes receiving a user input for some of the first objects and using a productive adversarial neural network model for image inpainting, And transforming some of the first objects.
  • a computer program stored in a computer-readable recording medium is provided to execute an image modification method according to an embodiment of the present disclosure on a computer.
  • an object selected or automatically recognized by a user may be focused, and a video in which a background area is out of focus may be provided.
  • a video in which a focused object is changed according to a user's input may be provided.
  • a video in which an object selected by a user or automatically recognized is deleted may be provided. Accordingly, it is possible to prevent an invasion of privacy caused by photographing an unintended object, and to allow viewers to focus on the main object.
  • a user may extract an object from one or more images and store the object image in a storage device such as a memory in advance. After that, the user can load the stored object image and combine it with another image.
  • an image similar to a multi-exposure shot may be generated by extracting and combining an object from each of a plurality of images continuously photographed in a specific time unit or a frame image extracted in a specific time unit from a moving image.
  • the position of each object can be modified and synthesized through a user input such as a touch input.
  • the background due to correction unlike the existing Photoshop or correction application Distortion may not appear.
  • some areas, such as eyes, nose, mouth, ears, hair, clothes, etc. of an object extracted from an image may be transformed into another natural shape using an artificial neural network model.
  • FIG. 1 is a diagram illustrating an example in which a user transforms a video output through a screen of a user terminal according to an embodiment of the present disclosure.
  • FIG. 2 is a schematic diagram illustrating a configuration in which an information processing system is connected to enable communication with a plurality of user terminals in order to provide an image modification service according to an embodiment of the present disclosure.
  • FIG. 3 is a block diagram illustrating an internal configuration of a user terminal and an information processing system according to an embodiment of the present disclosure.
  • FIG. 4 is a block diagram illustrating an internal configuration of a processor according to an embodiment of the present disclosure.
  • FIG. 5 is a flowchart illustrating a method of transforming an image according to an exemplary embodiment of the present disclosure.
  • FIG. 6 is a diagram illustrating an example of out-focusing a background area that is an area other than some objects among a plurality of objects included in a video according to an exemplary embodiment of the present disclosure.
  • FIG. 7 is a diagram illustrating an example of combining an image including an object extracted from one or more images into another image according to an embodiment of the present disclosure.
  • FIG. 8 is a diagram illustrating an example of generating an image similar to a multiple exposure shot by combining an image including an object extracted from one or more images with another image according to an embodiment of the present disclosure.
  • FIG. 9 is a diagram illustrating an example of deleting an object extracted from one or more images according to an embodiment of the present disclosure.
  • FIG. 10 is a diagram illustrating an example of transforming an object extracted from one or more images according to an embodiment of the present disclosure.
  • 11 is a diagram illustrating an example of deforming some of objects extracted from one or more images according to an embodiment of the present disclosure.
  • module or “unit” used in the specification mean software or hardware components, and “module” or “unit” performs certain roles.
  • The'module' or'unit' is not meant to be limited to software or hardware.
  • The'module' or'unit' may be configured to be in an addressable storage medium, or may be configured to reproduce one or more processors.
  • the'module' or'unit' refers to components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, It may include at least one of procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, or variables.
  • Components and functions provided within the'module' or'unit' may be combined into a smaller number of components and'module' or'unit', or additional components and'module' or'unit'. Can be further separated.
  • a'module' or'unit' may be implemented with a processor and a memory.
  • 'Processor' should be interpreted broadly to include general purpose processors, central processing units (CPUs), microprocessors, digital signal processors (DSPs), controllers, microcontrollers, state machines, etc.
  • a'processor' may refer to an application specific semiconductor (ASIC), programmable logic device (PLD), field programmable gate array (FPGA), and the like.
  • ASIC application specific semiconductor
  • PLD programmable logic device
  • FPGA field programmable gate array
  • 'Processor' refers to a combination of processing devices such as, for example, a combination of a DSP and a microprocessor, a combination of a plurality of microprocessors, a combination of one or more microprocessors in combination with a DSP core, or any other such combination of configurations. You may.
  • the'memory' should be broadly interpreted to include any electronic component capable of storing electronic information.
  • 'Memory' refers to random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erase-programmable read-only memory (EPROM), It may refer to various types of processor-readable media such as electrically erasable PROM (EEPROM), flash memory, magnetic or optical data storage, registers, and the like.
  • RAM random access memory
  • ROM read-only memory
  • NVRAM non-volatile random access memory
  • PROM programmable read-only memory
  • EPROM erase-programmable read-only memory
  • the memory is said to be in electronic communication with the processor if the processor can read information from and/or write information to the memory.
  • the memory integrated in the processor is in electronic communication with the processor.
  • the'image' may include one or more images, photos, pictures and/or videos.
  • an image may refer to the moving picture itself and/or each frame image constituting the moving picture, and conversely, the moving picture may refer to an image.
  • 'object' may refer to each object included in an image, such as people, animals and plants, and objects.
  • the'object' may be a person, animals, plants, or objects that are the most central in an image.
  • 'object' may refer to an object image and/or an image including an object.
  • 'deformation' may refer to all or part of an image being in a form different from an existing form.
  • the transformation may include making some changes from the existing form based on the existing form.
  • the modification may include applying a new shape completely different from the existing shape.
  • the transformation may include creating a new one that does not already exist.
  • the modification may include deleting an existing one.
  • 'out focus' may refer to any aesthetic or aesthetic effect applied to at least a portion of an image. It may refer to processing so that an area other than the main object corresponding to the focus (ie, focus) in the image during and/or after the image is captured is not clear.
  • 'out focus' may include a bokeh effect and/or a blur effect in which all or a part of the image is blurred.
  • the main object corresponding to the focus (ie, focus) of the image may be processed to be emphasized, highlighted, or sharpened.
  • FIG. 1 is a diagram illustrating an example in which a user 110 transforms a video output through a screen 130 of a user terminal 120 according to an embodiment of the present disclosure.
  • At least one processor of the user terminal 120 may receive one or more images.
  • the image may include a video.
  • the processor of the user terminal 120 may receive one or more images input through an input device such as a camera in real time. For example, based on the input of the user 110 to start recording, the processor may capture one or more images through an internal photographing device included in the user terminal 120 and receive one or more images photographed in real time. I can.
  • the processor captures one or more images through an external photographing device that communicates with the user terminal 120, and receives one or more images photographed in real time.
  • the processor of the user terminal 120 may receive a pre-stored image and/or a recorded image from an internal storage device and/or an external storage device of the user terminal 120.
  • the processor receives at least one image selected from an internal storage device and/or an external storage device of the user terminal 120 can do.
  • the processor of the user terminal 120 may extract one or more objects 140 included in the received one or more images, and set an area other than the extracted one or more objects 140 as a background area.
  • the processor may transform at least one of the one or more objects 140 or the background region, and generate an image including at least one of the one or more transformed objects or the background region.
  • the processor performs out-of-focus (OUT-OF-FOCUS) on a background area on one or more received images, adjusts the intensity of out-focus, and extracts one or more objects 140 and a modified background area. It is possible to create an image including.
  • the processor may generate a moving image in which a background area is out of focus according to movement of the object.
  • the processor extracts an object from each of a plurality of frames included in the received video and performs out-focus on the background area, thereby generating a video in which the background area is out-focused according to the movement of the object.
  • the processor may output and store one or more images and/or videos generated in this way through the screen 130 of the user terminal 120 or to an internal storage device and/or an external storage device of the user terminal 120.
  • the user 110 may capture a video including the female object 140 and the vehicle object in real time using the camera of the user terminal 120.
  • the user 110 may perform a touch input for selecting the female object 140 on the screen 130 on which the video being photographed is output.
  • the processor extracts the female object 140 included in the video, sets an area other than the female object 140 as a background area, and makes the background area out of focus.
  • the processor may adjust the intensity of out-focus based on a user input for out-focus.
  • the processor may generate a video in which the background area is out of focus according to the movement of the female object 140 in the received video.
  • the out-of-focus effect can be applied to a background area that varies according to movement of an object.
  • the processor may generate a video in which a background area other than an object is out of focus, and output the generated video through the screen 130 of the user terminal 120. Also, the video generated in this way may be stored in a storage device.
  • FIG. 1 illustrates an example in which a processor transforms and outputs a video received in real time while capturing a video, but is not limited thereto.
  • the processor may transform and output a pre-photographed and pre-stored video.
  • the information processing system 230 may include system(s) capable of providing image modification services.
  • the information processing system 230 is one or more server devices and/or databases capable of storing, providing and executing computer-executable programs (eg, downloadable applications) and data related to image modification services, or It may include one or more distributed computing devices and/or distributed databases based on cloud computing services.
  • the information processing system 230 may include separate systems (eg, servers) for providing an image modification service.
  • the image modification service provided by the information processing system 230 may be provided to a user through an application for image modification service installed in each of the plurality of user terminals 210_1, 210_2, and 210_3.
  • the user terminals 210_1, 210_2, and 210_3 may process tasks such as out-focusing, image synthesis, and image inpainting using an image modification program/algorithm stored therein.
  • the user terminals 210_1, 210_2, and 210_3 may directly process tasks such as out-focus, image synthesis, and image inpainting without communicating with the information processing system 230.
  • the plurality of user terminals 210_1, 210_2, 210_3 may communicate with the information processing system 230 through the network 220.
  • the network 220 may be configured to enable communication between the plurality of user terminals 210_1, 210_2, and 210_3 and the information processing system 230.
  • the network 220 is, for example, a wired network such as Ethernet, a wired home network (Power Line Communication), a telephone line communication device and RS-serial communication, a mobile communication network, a WLAN (Wireless LAN), It can be composed of wireless networks such as Wi-Fi, Bluetooth and ZigBee, or a combination thereof.
  • the communication method is not limited, and user terminals 210_1, 210_2, 210_3 as well as a communication method utilizing a communication network (for example, a mobile communication network, wired Internet, wireless Internet, broadcasting network, satellite network, etc.) that the network 220 may include. ), short-range wireless communication may also be included.
  • a communication network for example, a mobile communication network, wired Internet, wireless Internet, broadcasting network, satellite network, etc.
  • short-range wireless communication may also be included.
  • the mobile phone terminal 210_1, the tablet terminal 210_2, and the PC terminal 210_3 are illustrated as examples of user terminals, but are not limited thereto, and the user terminals 210_1, 210_2, 210_3 are wired and/or wireless communication It could be any computing device capable of this.
  • the user terminal may include a smart phone, a mobile phone, a computer, a notebook computer, a personal digital assistant (PDA), a portable multimedia player (PMP), a tablet PC, and the like. Also, in FIG.
  • three user terminals 210_1, 210_2, 210_3 are shown to communicate with the information processing system 230 through the network 220, but are not limited thereto, and different numbers of user terminals may be connected to the network ( It may be configured to communicate with the information processing system 230 via 220.
  • the information processing system 230 is data from the user terminals 210_1, 210_2, 210_3 through an application for image modification service operating in the user terminals 210_1, 210_2, 210_3 (for example, transformation Target images, user inputs for images, transformed images, etc.) can be received.
  • the information processing system 230 transmits data (for example, an image to be transformed, to the user terminals 210_1, 210_2, 210_3) through an application for an image transformation service operating in the user terminals 210_1, 210_2, and 210_3.
  • User input for images, transformed images, etc. can be provided.
  • the user terminal 210 may refer to an arbitrary computing device capable of executing a camera application, a photo editing application, a web browser, etc. and capable of wired/wireless communication.
  • the mobile phone terminal 210_1 of FIG. 2, a tablet It may include a terminal 210_2, a PC terminal 210_3, and the like.
  • the user terminal 210 may include a memory 312, a processor 314, a communication module 316, and an input/output interface 318.
  • the information processing system 230 may include a memory 332, a processor 334, a communication module 336, and an input/output interface 338.
  • the user terminal 210 and the information processing system 230 are configured to communicate information and/or data through the network 220 using respective communication modules 316 and 336. Can be.
  • the input/output device 320 may be configured to input information and/or data to the user terminal 210 through the input/output interface 318 or to output information and/or data generated from the user terminal 210.
  • the memories 312 and 332 may include any non-transitory computer-readable recording medium.
  • the memories 312 and 332 are non-destructive mass storage devices such as random access memory (RAM), read only memory (ROM), disk drive, solid state drive (SSD), flash memory, etc. (Permanent mass storage device) may be included.
  • a non-destructive mass storage device such as a ROM, SSD, flash memory, disk drive, etc. may be included in the user terminal 210 or the information processing system 230 as a separate permanent storage device that is separate from the memory.
  • the memory 312 and 332 may store an operating system and at least one program code (eg, a code for a camera application installed and driven in the user terminal 210, a photo editing application, etc.).
  • These software components may be loaded from a computer-readable recording medium separate from the memories 312 and 332.
  • a separate computer-readable recording medium may include a recording medium directly connectable to the user terminal 210 and the information processing system 230, for example, a floppy drive, a disk, a tape, a DVD/CD- It may include a computer-readable recording medium such as a ROM drive and a memory card.
  • software components may be loaded into the memories 312 and 332 through a communication module other than a computer-readable recording medium. For example, at least one program is loaded into the memories 312 and 332 based on a computer program installed by files provided by the file distribution system for distributing the installation files of the developers or applications through the network 220 Can be.
  • the processors 314 and 334 may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input/output operations. Instructions may be provided to the processors 314 and 334 by the memories 312 and 332 or the communication modules 316 and 336. For example, the processors 314 and 334 may be configured to execute an instruction received according to a program code stored in a recording device such as the memories 312 and 332.
  • the communication modules 316 and 336 may provide a configuration or function for the user terminal 210 and the information processing system 230 to communicate with each other through the network 220, and the user terminal 210 and/or information processing
  • the system 230 may provide a configuration or function for communicating with other user terminals or other systems (eg, a separate cloud system). For example, a request or data (eg, an image modification request, etc.) generated by the processor 314 of the user terminal 210 according to a program code stored in a recording device such as a memory 312 It may be transmitted to the information processing system 230 through the network 220 according to the control.
  • a control signal or command provided under the control of the processor 334 of the information processing system 230 is transmitted through the communication module 316 of the user terminal 210 through the communication module 336 and the network 220. It may be received by the user terminal 210. For example, the user terminal 210 may receive a modified image or the like from the information processing system 230 through the communication module 316.
  • the input/output interface 318 may be a means for an interface with the input/output device 320.
  • the input device may include a device such as a camera, keyboard, microphone, and mouse including an audio sensor and/or an image sensor
  • the output device may include a device such as a display, a speaker, a haptic feedback device, and the like. I can.
  • the input/output interface 318 may be a means for an interface with a device in which a configuration or function for performing input and output is integrated into one, such as a touch screen.
  • the processor 314 of the user terminal 210 uses information and/or data provided by the information processing system 230 or other user terminals in processing instructions of a computer program loaded in the memory 312.
  • the configured service screen and the like may be displayed on the display through the input/output interface 318.
  • the input/output device 320 is illustrated not to be included in the user terminal 210, but is not limited thereto, and may be configured with the user terminal 210 and one device.
  • the input/output interface 338 of the information processing system 230 is a means for interface with a device (not shown) for input or output that is connected to the information processing system 230 or that the information processing system 230 may include. Can be In FIG.
  • the input/output interfaces 318 and 338 are illustrated as elements configured separately from the processors 314 and 334, but are not limited thereto, and the input/output interfaces 318 and 338 may be configured to be included in the processors 314 and 334. have.
  • the user terminal 210 and the information processing system 230 may include more components than those of FIG. 3. However, there is no need to clearly show most of the prior art components. According to an embodiment, the user terminal 210 may be implemented to include at least some of the input/output devices 320 described above. In addition, the user terminal 210 may further include other components such as a transceiver, a global positioning system (GPS) module, a camera, various sensors, and a database.
  • GPS global positioning system
  • the user terminal 210 when the user terminal 210 is a smartphone, it may generally include components included in the smartphone, for example, an acceleration sensor, a gyro sensor, a camera module, various physical buttons, and touch Various components such as a button using a panel, an input/output port, and a vibrator for vibration may be implemented to be further included in the user terminal 210.
  • the processor 314 of the user terminal 210 may be configured to operate an application or the like that provides an image modification service. In this case, a code associated with the application and/or program may be loaded into the memory 312 of the user terminal 210.
  • the processor 314 connects the input/output interface 318 to an input device such as a touch screen, a keyboard, a camera including an audio sensor and/or an image sensor, a microphone, etc.
  • an input device such as a touch screen, a keyboard, a camera including an audio sensor and/or an image sensor, a microphone, etc.
  • the received text, image, video, audio and/or action, etc. can be stored in the memory 312 or the communication module 316 and It may be provided to the information processing system 230 through the network 220.
  • the processor 314 may receive at least one selected image in response to a user input for selecting at least one image from among a plurality of images, and transmit the received image to the communication module 316 and the network 220 ) Can be provided to the information processing system 230.
  • the processor may receive one or more images from an input device, such as a camera, in response to a user input for capturing an image and/or video, and transmit the received image to the communication module 316 and the network 220. It can be provided to the information processing system 230 through the.
  • the processor 314 may receive a user input for an image on an application providing an image modification service or a web browser through an input device 320 such as a touch screen, a mouse, and a keyboard.
  • the processor may directly process a request corresponding to such a user input or may provide it to the information processing system 230 through the network 220 and the communication module 316.
  • the user input for the image may include a user input for selecting an object included in the image, a user input for some of the objects, an input for object transformation, an input for out-focus control, and the like.
  • the processor 314 of the user terminal 210 manages, processes, and/or stores information and/or data received from the input device 320, other user terminals, the information processing system 230 and/or a plurality of external systems. Can be configured to Information and/or data processed by the processor 314 may be provided to the information processing system 230 through the communication module 316 and the network 220.
  • the processor 314 of the user terminal 210 may transmit and output information and/or data to the input/output device 320 through the input/output interface 318.
  • the processor 314 may display the received information and/or data on the screen of the user terminal. For example, the processor 314 may display the received image to be transformed, the transformed image, and/or the generated image on the screen.
  • the processor 334 of the information processing system 230 may be configured to manage, process, and/or store information and/or data received from a plurality of user terminals 210 and/or a plurality of external systems. Information and/or data processed by the processor 334 may be provided to the user terminal 210 through the communication module 336 and the network 220. For example, the processor 334 may provide the received image to be transformed, the transformed image, and/or the generated image to the user terminal 210 through the communication module 336 and the network 220.
  • the processor 334 of the information processing system 230 is through an output device 320 such as a display output capable device (eg, a touch screen, a display, etc.) of the user terminal 210 and a voice output capable device (eg, a speaker). It may be configured to output processed information and/or data. For example, the processor 334 may output the received image to be transformed, the transformed image, and/or the generated image through the screen of the user terminal.
  • the processors 314 and 334 may extract one or more objects included in one or more received images.
  • the processors 314 and 334 may transform at least one of the extracted one or more objects or background regions, and generate an image including at least one of the transformed one or more objects or background regions.
  • the background area may refer to an area other than one or more objects extracted from one or more received images.
  • the processors 314 and 334 may perform out-of-focus (OUT-OF-FOCUS) on the background area and adjust the intensity of the out-focus to generate an image in which focus is focused on one or more extracted objects. .
  • OUT-OF-FOCUS out-of-focus
  • the processors 314 and 334 may generate a moving image in which the background area is out of focus according to the movement of the object.
  • the processors 314 and 334 in response to a user response selecting another object included in the received one or more images, the processors 314 and 334 set an area other than the selected other object as a background area, and move the selected other object. Accordingly, a video in which the background area is out of focus may be generated.
  • the processors 314 and 334 delete the background region from the received one or more images to generate at least one first image including one or more objects, receive at least one second image, and at least One second image may be combined with the generated first image.
  • the processors 314 and 334 delete one or more objects extracted from the received one or more images, and through a Generative Adversarial Network (GAN) for image inpainting, in one or more images. An image of an area in which one or more extracted objects have been deleted may be generated. Thereafter, the processor may apply the image of the region in which the one or more objects have been deleted to the received one or more images.
  • GAN Generative Adversarial Network
  • the processors 314 and 334 may generate a moving picture in which one or more objects to be moved are deleted.
  • the processor 314, 334 transforms one or more objects extracted based on the received user input, and is formed according to the deformation of the extracted one or more objects through a productive hostile neural network model for image inpainting.
  • An image may be generated for a blank area or a distortion area of one or more images.
  • the processor may apply an image of a blank area or a distortion area of the one or more images to the received one or more images.
  • the processors 314 and 334 may transform some of the extracted one or more objects using a productive hostile neural network model for image inpainting.
  • the information processing system 230 is illustrated as an element configured separately from the user terminal 210, but is not limited thereto, and the information processing system 230 may be configured to be included in the user terminal 210.
  • the processor 314 may include an object extracting unit 410, an object/background transforming unit 420, and an image generating unit 430.
  • the object extraction unit 410 may receive one or more images.
  • the object extraction unit 410 may extract one or more objects included in one or more received images.
  • the object extracting unit 410 may extract one or more objects included in one or more received images by using an artificial neural network model learned to extract an object from an image.
  • the object extraction unit 410 may extract one or more objects included in one or more received images using a segmentation artificial neural network model and/or a detection artificial neural network model.
  • the segmentation artificial neural network model may apply various segmentation algorithms, and may be, for example, a semantic segmentation and/or an instance segmentation artificial neural network model.
  • the object/background transforming unit 420 may transform at least one of an object or a background region extracted by the object extracting unit 410.
  • the background area may refer to an area other than an object extracted from one or more images.
  • the object/background transforming unit 420 may perform out-focus on the background area. Additionally, the object/background transforming unit 420 may adjust the intensity of out-focus.
  • the object/background transforming unit 420 may generate at least one first image including the extracted object by deleting a background region from one or more images.
  • the object/background transforming unit 420 deletes an object extracted from one or more images, and supplements an area in which an object is deleted from one or more images using a productive hostile neural network model for image inpainting.
  • the object/background transforming unit 420 generates an image for a region in which an object has been deleted from one or more images through a productive hostile neural network model for image inpainting, and creates an image for the region in which the object has been deleted. Can be applied to more than one image.
  • the object/background transforming unit 420 receives a user input for the extracted object, transforms the object based on the received user input, and converts an empty area of one or more images resulting from the transformation into an image. It can be supplemented by using a productive adversarial neural network model for painting.
  • the object/background transforming unit 420 generates an image for an empty area or a distortion area of one or more images formed according to the deformation of the object through the hostile neural network model for image inpainting, and An image of a blank area or a distortion area can be applied to one or more images.
  • the object/background transforming unit 420 may receive a user input for some of the extracted objects and transform some of the extracted objects using a productive hostile neural network model for image inpainting. have.
  • the image generator 430 may generate an image including at least one of a transformed object or a background area. Additionally, the image generator 430 may output the generated image. For example, the image generator 430 may output the generated image through the screen of the user terminal. In an embodiment, when one or more images are moving images, the image generator 430 may generate a moving image in which a background area is out of focus according to movement of an object. Additionally, the image generator 430 selects another object included in one or more images, sets an area other than the selected other object as a background area, and generates a video in which the set background area is out of focus according to the movement of the selected other object. Can be generated.
  • the image generating unit 430 receives at least one second image, receives the first image generated by the object/background transform unit 420, and receives at least one second image to the received first image. You can create an image that combines 2 images.
  • the image generator 430 may generate a video in which the moving object is deleted when one or more images are videos and an object is moved in the video.
  • the image modification method 500 may be performed by a processor (eg, at least one processor of a user terminal or an information processing system). As shown, the image modification method 500 may be initiated by the processor receiving one or more images (S510). The processor may extract a first object included in one or more received images (S520).
  • a processor eg, at least one processor of a user terminal or an information processing system.
  • the image modification method 500 may be initiated by the processor receiving one or more images (S510).
  • the processor may extract a first object included in one or more received images (S520).
  • the processor may transform at least one of the first object and the first background area (S530).
  • the first background area may refer to an area other than the first object in one or more images.
  • the processor may perform out-focus on the first background area. Additionally, the processor can adjust the intensity of out focus.
  • the processor may generate at least one first image including the first object by deleting the first background area from the received one or more images.
  • the processor deletes the first object from the received one or more images, and generates an image for the region in which the first object has been deleted from the one or more images through a productive adversarial neural network model for image inpainting. I can.
  • the processor may apply the image of the area in which the first object has been deleted to one or more received images.
  • the processor receives a user input for the first object, transforms the first object based on the received user input, and transforms the first object through a productive adversarial neural network model for image inpainting.
  • An image of a blank area or a distortion area of one or more images formed according to the method may be generated.
  • the processor may apply an image of a blank area or a distortion area of the one or more images to the received one or more images.
  • the processor may receive a user input for some of the first objects, and transform some of the first objects using a productive adversarial neural network model for image inpainting.
  • the processor may generate an image including at least one of the transformed first object or the first background area (S540).
  • the processor may generate a moving image in which the first background area is out of focus according to the movement of the first object.
  • the processor selects a second object included in the received one or more images, sets an area other than the selected second object as a second background area, and causes the second background area to be out-focused according to the movement of the second object. You can create a video.
  • the processor may receive at least one second image and synthesize at least one second image with the first image generated in step S530.
  • the processor may generate a moving picture from which the moving first object has been deleted. After that, the processor may output the generated image (S550). It can be displayed on the screen of the user terminal. Or it can be stored in memory.
  • the processor may receive one or more videos and extract an object included in the received video.
  • the processor may receive a video captured in real time from an input device such as a camera.
  • the processor may receive a pre-stored video and/or a recorded video from a storage device such as a memory.
  • the video may include one or more frame images.
  • the processor may extract the first object from the received video and transform at least one of the extracted first object or the first background region.
  • the first object may be selected in response to a user input.
  • the first object may be automatically extracted by an object extraction algorithm or the like.
  • the processor may generate a video including at least one of the transformed first object or the first background area.
  • the processor may extract a first object from some of a plurality of frames included in the video, and transform at least one of the extracted first object or the first background region.
  • the processor may generate one or more frames including at least one of the transformed first object or the first background area, and generate a video including the generated one or more frames.
  • the processor may generate a video in which the first background area is out of focus by out-focusing the first background area and adjusting the intensity of the out focus.
  • the processor may generate a video in which the first background area is out of focus according to the movement of the first object.
  • the processor extracts a first object from each frame image corresponding to a partial section of the received video, and out-focuss a background area other than the first object, so that the background area is out according to the movement of the first object. It is possible to generate a video in which the focus is performed and the focus for the first object is maintained.
  • the processor selects a second object included in the received video and sets an area other than the selected second object as the second background area, so that the second background area is out of focus according to the movement of the second object.
  • the second object may be selected in response to a user input.
  • the second object may be automatically selected by an object extraction algorithm or the like.
  • the processor selects a second object from some of a plurality of frames included in the video, generates one or more frames in which a second background region other than the selected second object is out of focus, and generates one or more frames. You can create a video including frames.
  • the processor may extract the first male object 612 from each frame image corresponding to the first section from the received video, and out-focus a background region other than the first male object 612. Accordingly, the processor may generate one or more frames 610 in which the background area is out of focus and focus for the first male object 612 is maintained according to the movement of the first male object 612. Thereafter, the processor extracts the second male object 622 from each frame image corresponding to the second section in the received video based on a user input for selecting the second male object 622 in the video, and 2 A background area other than the male object 622 may be out of focus. Accordingly, the processor may generate one or more frames 620 in which the background area is out of focus and focus on the second male object 622 is maintained according to the movement of the second male object 622.
  • the processor may generate a video including the frames 610 and 620 thus generated.
  • the first section and the second section may be continuous sections.
  • the processor may generate a video in which focus moves from the first male object 612 to the second male object 622.
  • the processor extracts one or more objects included in the received one or more images, deletes a background region other than the object extracted from the one or more images, and deletes at least one first image (e.g., an object Extract image) can be created. Thereafter, the processor may receive at least one second image and synthesize the received second image with the generated first image. For example, the processor stores the generated first image in an internal and/or external storage device of the user terminal, and then, receives the first image from the storage device according to the user input, and submits the generated first image to the received first image. 2 You can composite images.
  • the first image and/or the second image may be a moving picture.
  • the first image may be that the extracted object is enlarged, reduced, duplicated, vertical/left-right symmetry, tilt adjustment, sharpness adjustment, etc., or various effects such as a black and white effect and an emphasis effect are applied to the extracted object. have.
  • the processor may generate at least one video including the extracted object by extracting an object included in the received video and deleting a background area other than the extracted object from the video. Thereafter, the processor may receive at least one image and combine the generated video with the received image. Specifically, the processor extracts an object from each frame image corresponding to a certain section from the received video, deletes a background region other than the object, generates one or more frames including only the extracted object, and generates one or more frames.
  • An object video eg, a GIF image
  • the processor extracts an object included in one or more images, deletes a background area that is an area other than the object extracted from the one or more images, and deletes at least one image (e.g., a sticker image) including the extracted object. ) Can be created. Thereafter, the processor may receive at least one video and combine the generated image with the received video. For example, the processor may synthesize an image generated in each frame image corresponding to a predetermined section from the received video.
  • the processor extracts a male object from the received image 710 and deletes a background region other than the male object, thereby generating a first image including only the extracted male object. Thereafter, the processor may generate the image 720 including the first image and the second image by receiving the second image and synthesizing the first image to the area around the object included in the second image.
  • the first image may be the extracted male object being reduced, duplicated, and/or symmetrical left and right.
  • the object included in the first image and the object included in the second image are shown to be the same, but the present invention is not limited thereto.
  • the processor synthesizes the first image into a region around the object included in the second image, but is not limited thereto.
  • the processor may synthesize the first image into a region in the second image where the object exists.
  • the processor may receive a video including a plurality of frame images.
  • the processor may extract one or more objects from a first frame image among a plurality of frame images included in the received moving picture, delete a background area other than the extracted object, and generate a first image including the extracted object.
  • the processor may generate an image similar to the multiple exposure shot or a moving picture in which the movement trace of the object is displayed by synthesizing the second frame image and the first image among the plurality of frame images.
  • the first frame image, the second frame image, and/or the first image may be one or more images.
  • the first frame image and/or the second frame image may be a frame image extracted in a specific time unit among a plurality of frames.
  • the processor extracts n from a received video in a specific time unit, where n is Of the (a natural number) frame images, m (where m is One or more objects are extracted from each of the remaining n-1 frame images excluding the (natural number)-th frame, and n-1 first images may be generated by deleting a background area that is an area other than the extracted object. Thereafter, the processor may generate an image similar to the multi-exposure shot or a moving picture in which the movement trace of the object is displayed by combining the m-th frame image and the n-1 frame image.
  • the processor may receive a plurality of images continuously photographed in a specific time unit.
  • the processor may generate a first image including the extracted object by extracting one or more objects from the first continuous shot image among the plurality of images and deleting a background area that is an area other than the extracted object. Thereafter, the processor may generate an image similar to the multiple exposure shot or a moving picture in which the movement trace of the object is displayed by synthesizing the second continuous shot image and the first image among the plurality of images.
  • the first continuous shot image, the second continuous shot image, and/or the first image may be one or more images.
  • the processor extracts a female object from each of eight images out of nine images successively photographed in a specific time unit, and deletes a background region other than the extracted female object, thereby 1 You can create an image. Thereafter, the processor combines the remaining one image (i.e., the image including the ice rink background) and the eight first images out of nine consecutively photographed images, thereby creating an image 800 similar to the multiple exposure shot image. Can be generated.
  • the image 800 generated according to the exemplary embodiment of the present disclosure extracts only an object from some images of a plurality of consecutively photographed images, unlike a conventional multiple exposure shot image, and an image in which a background region exists among the plurality of consecutively photographed images. Since it is synthesized, the position of each object to be synthesized can be modified through user input such as touch input.
  • the processor synthesizes the object images extracted from each of the continuously photographed images (or frame images) as it is, but is not limited thereto.
  • the processor may combine the extracted object image by applying a black and white effect.
  • the processor may modify and synthesize the color sharpness, saturation, brightness, etc. according to the photographing sequence of the continuous photographed image (or frame image) from which each object image is extracted.
  • the processor may extract and delete one or more objects from one or more received images, and supplement a region in which an object has been deleted from one or more images using a productive hostile neural network model for image inpainting.
  • the processor may extract and delete one or more objects from one or more received images, and generate an image for a region in which an object has been deleted from one or more images through a productive adversarial neural network model for image inpainting.
  • the processor may generate an image of the area in which the object has been deleted based on the area around the area in which the object has been deleted.
  • the processor may generate an image for an area in which an object has been deleted based on the before/after frame images. After that, the processor may apply the image of the area in which the object has been deleted to one or more images.
  • the processor may generate a moving image in which the object is deleted according to the movement of the extracted object. For example, when one or more received images are videos and an object is moved in the video, a video in which the moved object is deleted may be generated. Specifically, by extracting and deleting an object from each of a plurality of frames corresponding to a predetermined section from the video, a video in which the object does not appear even if the object moves may be generated.
  • the object extracted and deleted by the processor may be an object selected through a user input such as a touch, an object that is automatically recognized as not the main object, and/or an object not included in a preset object DB.
  • the processor may extract and delete a person object standing on the beach background from the first image 710.
  • the area from which the human object has been deleted can be naturally filled using a productive hostile neural network model for image inpainting.
  • the processor can generate an image for the area in which the human object has been deleted, and apply the image for the area in which the human object has been deleted to the first image 710 through a productive hostile neural network model for image inpainting. have.
  • the processor may generate and/or output the filled second image 720 so that the human object is deleted and the deleted area naturally connects with the surrounding background area. According to an embodiment of the present disclosure, invasion of privacy caused by photographing an unintended object can be prevented, and users can focus on the main object.
  • the processor may receive a user input for the object and may transform the object based on the received user input.
  • the processor may compensate for blank areas or distorted areas of one or more images due to such deformation using a productive hostile neural network model for image inpainting.
  • the processor generates an image of a blank area or a distortion area of one or more images formed according to deformation of an object through a productive hostile neural network model for image inpainting, and the blank area or distortion area of the one or more images
  • the image for can be applied to one or more received images.
  • the processor may naturally supplement through image inpainting based on the surrounding area of an empty area that appears as an object transformation in the image.
  • the processor may naturally supplement the blank area of the image through image inpainting based on the frames before and after the corresponding frame. Blank areas may appear in the image.
  • the user can increase the height of the human object and change the body slimly through a touch input, a click input, and/or a drag input for an object in the output image 1010.
  • a touch input As the body of the human object extracted from the image is thinly deformed, some of the area where the object existed before the transformation may appear as an empty area or distorted in the image.
  • the processor generates an image of a blank area or a distortion area formed by thinly deforming the body of a human object through a productive adversarial neural network model for image inpainting, and the image of the blank area or distortion area is imaged (1010). Can be applied to.
  • the processor supplements the blank area or the distorted area using a productive hostile neural network model for image inpainting, so that the blank area or the distorted area that occurs as the extracted object is transformed is naturally filled. Can be generated. In this case, unlike existing Photoshop or correction applications, distortion of the background due to correction may not appear.
  • the processor may receive a user input for some of the objects extracted from one or more images, and may transform or generate some of the extracted objects using a productive adversarial neural network model for image inpainting.
  • the productive hostile neural network model for image inpainting may be a model trained to determine a color, partial shape, eyes, nose, mouth, clothes, hair, etc. that match the object included in the input image.
  • the processor uses a productive hostile neural network model for image inpainting to transform the eye area of the object into an eye shape that matches the object or You can create a matching eye shape and apply it to your image.
  • the processor transforms the eye area of the object into an eye shape that matches the object or matches the object by using a productive hostile neural network model for image inpainting. You can create an eye shape and apply it to an image.
  • the user may perform a touch input on the eye area of the human object.
  • the processor transforms the eye area into an eye shape that matches the corresponding human object, thereby generating and/or outputting a natural image including an opened human object such as the second image 1120.
  • the processor may recommend a plurality of eye shapes matching the object, receive a user input for selecting one of the recommended plurality of eye shapes, and transform the eye area of the object into the selected eye shape. .
  • the processor transforms the eye area of the object into an open eye shape when the object is closed, but is not limited thereto.
  • the processor may transform the ear area of the object into an ear shape that matches the object or into a shape covered with hair.
  • the processor may transform the front portion of the object into a different style of the front portion.
  • the processor in response to a user input on the area under the nose of the object, the processor may transform the area under the nose of the object into a shape of a mustache or a shape without a mustache.
  • the processor may transform the lip region of the object into various lip shapes, such as a laughing lip shape.
  • the image modification method described above may be provided as a computer program stored in a computer-readable recording medium for execution on a computer.
  • the medium may be one that continuously stores a program executable by a computer, or temporarily stores a program for execution or download.
  • the medium may be a variety of recording means or storage means in a form in which a single piece of hardware or several pieces of hardware are combined.
  • the medium is not limited to a medium directly connected to a computer system, but may be distributed on a network.
  • Examples of media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, And there may be ones configured to store program instructions, including ROM, RAM, flash memory, and the like.
  • examples of other media include an app store that distributes applications, a site that supplies or distributes various software, and a recording medium or a storage medium managed by a server.
  • the image modification method described above may be operated through a browser (eg, a web browser) to which the user terminal accesses without installing a separate program. That is, the user of the user terminal may be provided with an image modification service through a web browser.
  • the processing units used to perform the techniques include one or more ASICs, DSPs, digital signal processing devices (DSPDs), programmable logic devices (PLDs). ), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, other electronic units designed to perform the functions described in this disclosure. , Computer, or a combination thereof.
  • various exemplary logic blocks, modules, and circuits described in connection with the present disclosure may include general purpose processors, DSPs, ASICs, FPGAs or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or It may be implemented or performed in any combination of those designed to perform the functions described in.
  • a general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine.
  • the processor may also be implemented as a combination of computing devices, eg, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in connection with the DSP core, or any other configuration.
  • the techniques include random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), PROM (on a computer-readable medium such as programmable read-only memory), erasable programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, compact disc (CD), magnetic or optical data storage device, etc. It can also be implemented as stored instructions.
  • the instructions may be executable by one or more processors, and may cause the processor(s) to perform certain aspects of the functionality described in this disclosure.
  • exemplary implementations may refer to utilizing aspects of the currently disclosed subject matter in the context of one or more standalone computer systems, the subject matter is not so limited, but rather is associated with any computing environment such as a network or distributed computing environment. It can also be implemented. Furthermore, aspects of the presently disclosed subject matter may be implemented in or across multiple processing chips or devices, and storage may be similarly affected across multiple devices. Such devices may include PCs, network servers, and handheld devices.

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  • General Physics & Mathematics (AREA)
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Abstract

Un mode de réalisation de la présente invention concerne un procédé de transformation d'une image, ledit procédé étant réalisé par au moins un processeur. Le procédé comprend les étapes consistant à : recevoir une ou plusieurs images ; extraire un premier objet inclus dans la ou les images reçues ; transformer le premier objet et/ou une première zone d'arrière-plan ; générer une image incluant le premier objet transformé et/ou la première zone d'arrière-plan transformée ; et délivrer en sortie l'image générée, la première zone d'arrière-plan étant une zone excluant le premier objet dans la ou les images reçues.
PCT/KR2020/095147 2019-11-11 2020-11-11 Procédé de transformation d'image WO2021096339A1 (fr)

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