Classifications

• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
  • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F18/00—Pattern recognition
  • G06F18/20—Analysing
  • G06F18/24—Classification techniques
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
  • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
  • G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V10/00—Arrangements for image or video recognition or understanding
  • G06V10/20—Image preprocessing
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V10/00—Arrangements for image or video recognition or understanding
  • G06V10/40—Extraction of image or video features
  • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
  • G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
  • G06V10/449—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V10/00—Arrangements for image or video recognition or understanding
  • G06V10/94—Hardware or software architectures specially adapted for image or video understanding
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V10/00—Arrangements for image or video recognition or understanding
  • G06V10/94—Hardware or software architectures specially adapted for image or video understanding
  • G06V10/955—Hardware or software architectures specially adapted for image or video understanding using specific electronic processors
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V20/00—Scenes; Scene-specific elements
  • G06V20/10—Terrestrial scenes
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
  • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
  • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
  • G06V40/161—Detection; Localisation; Normalisation
  • G06V40/166—Detection; Localisation; Normalisation using acquisition arrangements
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
  • G06V40/20—Movements or behaviour, e.g. gesture recognition
  • G06V40/23—Recognition of whole body movements, e.g. for sport training
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
  • G06V40/20—Movements or behaviour, e.g. gesture recognition
  • G06V40/28—Recognition of hand or arm movements, e.g. recognition of deaf sign language
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
  • H04N23/60—Control of cameras or camera modules
  • H04N23/61—Control of cameras or camera modules based on recognised objects
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
  • H04N23/60—Control of cameras or camera modules
  • H04N23/61—Control of cameras or camera modules based on recognised objects
  • H04N23/611—Control of cameras or camera modules based on recognised objects where the recognised objects include parts of the human body
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
  • H04N23/60—Control of cameras or camera modules
  • H04N23/65—Control of camera operation in relation to power supply
  • H04N23/651—Control of camera operation in relation to power supply for reducing power consumption by affecting camera operations, e.g. sleep mode, hibernation mode or power off of selective parts of the camera
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
  • H04N23/70—Circuitry for compensating brightness variation in the scene
  • H04N23/741—Circuitry for compensating brightness variation in the scene by increasing the dynamic range of the image compared to the dynamic range of the electronic image sensors
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N7/00—Television systems
  • H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
  • H04N7/183—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a single remote source
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V10/00—Arrangements for image or video recognition or understanding
  • G06V10/40—Extraction of image or video features
  • G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
  • G06V10/467—Encoded features or binary features, e.g. local binary patterns [LBP]
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
  • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
  • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
  • G06V40/161—Detection; Localisation; Normalisation
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
  • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Definitions

• a smart sensor for sensing dynamic scene-based occurrences can comprise dedicated computer vision (CV) computation hardware configured to receive sensor data from a sensor array comprising more than one sensor pixel and capable of computing one or more CV features using readings from neighboring sensor pixels of the sensor array, and a first processing unit
• CV computer vision
• the apparatus may comprise peripheral circuitry configured to provide at least one of a timing operation, a focusing operation, an auto-exposure correction operation, object detection, object recognition, storing a scanning window, an event-queuing and/or processing operation, analog processing, analog-to-digital conversion, an integration operation, CV feature computation, a cascade-classifier-based classification, a histogram-based classification, or memory buffering, or any combination thereof.
• the apparatus may comprise the second processing unit, and the first processing devis may be further configured to communicaie the face-detected event to the second processing unit while the second processing unit is operating in a low-power mode.
• FIG. 2B is a block diagram of a sensor system with a sensor array unit, microprocessor, and example peripheral circuitry 214, according to one embodiment.
• FIG. 6 is a simplified illustration of an example configurations of the sensor array of FIG. 5,
• the sensor system processes the information retrieved from the camera using the included embedded processor and sends "events" (or indications that one or more reference occurrences have occurred) for the main processor only when needed or as defined and configured by the application.
• This allows the general-purpose microprocessor (which is typically relatively high-speed and high-power to support a variety of applications) to stay in a low-power (e.g., sleep mode) most of the time as conventional, while becoming active only when events are received from the sensor system.
• a smart sensor capable of performing object detection, recognition, etc., can be useful in a variety of applications including internet of things (loT) applications.
• FIG. 2A is a block diagram that illustrates how a sensor system 210 (also referred to herein as a "smart sensor”) can be configured to enable high-level sensing operations while a main processor 220 can be operating in a low-power (e.g., "sleep" or "stand-by") mode, according to one embodiment.
• a sensor system 210 also referred to herein as a "smart sensor”
• main processor 220 can be operating in a low-power (e.g., "sleep" or "stand-by") mode, according to one embodiment.
• Components of FIG . 2A can be incorporated into a larger electronic device.
• An example of a mobile device in which an sensor system 210 may be incorporated is described below, with regard to FIG. 5.
• CV features can then be computed or extracted by the dedicated CV computation hardware using readings from neighboring sensor pixels of the sensor array, providing outputs such as a computed HSO and/or an LBP feature, label, or descriptor.
• no image signal processing circuitry may be disposed between the sensor array unit 212 and the dedicated CV computation hardware.
• dedicated CV computation hardware may receive raw sensor data from the sensor array unit 212 before any image signal processing is performed on the raw sensor data.
• Other CV computations are also possible based on other CV computation algorithms including edge detection, corner detection, scale-invariant feature transform (or SIFT), speeded up robust features (SURF), histogram of oriented gradients (HOG), local ternary patterns ( I . ! ' ;' : ⁇ . etc., as well as extensions of any of the above.
• an event can be an indication that one or more reference occurrences have occurred.
• events can include data related to a reference occurrence.
• the data included in an event can be indicative of a detected reference object, location information related to the reference object, number of reference objects, movement associated with detected reference object, and the like. This data may be conveyed in any of a variety of ways. For example, in the case of object detection, an event can be a simply binary output where "0" means the reference object has not been detected, and "1" means the reference objeci has been detected.
• the signals received by the CV computation hardware 242 from the sensor array unit 212 have not undergone ISP, for example, the signals have not undergone one or more of defect correction, white balancing, color balancing, auto focus, lens roll off, demosaicing, debayering, or image sharpening, or any combination thereof.
• some processing may occur, such as focusing or auto-exposure correction.
• Such signals that have not undergone ISP may be referred to as raw signals or raw sensor readings or raw sensor data.
• the block of one or more subject sensor elements can include a block of m by n sensor elements, for example, 1 1 by 1 1 sensor elements, it is also understood that a pixel-level LBP computation may also be made where the block of one or more subject sensor elements for which the localized C V feature is computed is a single subject sensor element.
• CV computation hardware 312 as separate from the dedicated microprocessor 320, it is understood that in some implementations, dedicated CV computation hardware 312 may be implemented in hardware within the dedicated microprocessor 320.
• microprocessor 216 can provide control signals to the line buffer(s) 230, ADC 234, two dimensional integration hardware 2.36, hardware scanning window array 238, and CV computation hardware 242.
• the microprocessor 216 may use a cascade classifier algorithm to perform object-class detection, for example face detection.
• further power savings are possible by implementing the cascade classifier in hardware, to further reduce the computational burden on the microprocessor 216.
• the optional cascade classifier hardware 244 includes a hardware
• each LBP feature, (LBPn, LBP] / ) will be multiplied by a given weight, (wi i , w i / ), each of which can be different.
• the first weighted scalar sum value is then compared to a first threshold.
• the cascade classifier hardware 244 moves to the next stage.
• the cascade classifier hardware again requests the CV computation hardware 242 to provide LBP features for m subject sensor elements at locations ⁇ (x2i, y'2;) > ⁇ ⁇ ⁇ Jim) ⁇ stored in the hardware scanning window array 2.38.
• the cascade classifier hardware 2.44 performs another summation of a dot product of each of the LBP features with one or more weights, (w 2 i, ..., 3 ⁇ 4 » ), to generate a second weighted scalar sum vakte.
• the second weighted scalar sum value is then compared to a second threshold.
• the cascade classifier hardware 244 can then indicate to the microprocessor 216 that the reference object has been detected, and may further optionally indicate the location of the portion of the image in which the reference object, or portion of reference object, was detected.
• the cascade classifier hardware 244 can be configured to send an indication to the microprocessor 216 that the reference object was detected along with data associated with the reference object, such as the all or some of the CV features computed in the process of detecting the reference object, the location within the image of those CV features, or any other data associated with the computations or operations performed by the CV computation hardware 242 and/or the cascade classifier hardware 2.44.
• the microprocessor 216 and the main processor 220 of FIG. 2A. As illustrated in FIG, 2B, the microprocessor 2.16 includes an interface 2.46 for communications with the second microprocessor. Additionally or alternatively, the microprocessor 216 might track a position of the detected reference object over time (e.g., over multiple images) to determine movement for gesture recognition, risk of collision, danger, and/or other events, for example.
• microprocessor 216 can, in some implementations, still provide control signals to sensor array unit 212, line buffer(s) 230, etc., or, alternatively or additionally, such control signals may be provided by- lower power control logic.
• a cascade classifier may be run as a software algorithm on the microprocessor 216.
• other software algorithms may be run on the microprocessor in the place of the cascade classifier. For example, reference object detection may be performed using histograms, as described in FIG. 11C.
• one or more of the line buffer(s) 230, the ADC 234, the two dimensional integration hardware 236, the hardware scanning window array 238, the CV computation hardware 242, the cascade classifier hardware 2.44, or any combination thereof may be considered peripheral circuitry, that is circuitry that is peripheral to the sensor array unit 212 and may correspond to peripheral circuitry 214 of FIG, 2A, It is also understood that the various components just listed, or any combination thereof, may be implemented instead as in-pixeJ circuitry within the sensor array unit 212.
• peripheral circuitry 214 is coupled with a plurality of sensor ceil outputs of a sensor array unit 212.
• the sensor array unit 212 and/or peripheral circuitry 214 include dedicated CV computation hardware to perform a feature detection computation using at least a subset of the plurality of sensor cell outputs, where the subset of the plurality of sensor cell outputs correspond to a region of the sensor array unit 212 (e.g., an image array) comprising neighboring sensor ceils or pixels.
• the output of the peripheral circuitry 214 is based (at least partially) on the feature detection computation.
• the first processing unit 217 processes signal s received from the one or more outputs of the smart image array to detect a reference occurrence.
• the first processing unit 217 then generates an event, indicating the reference occurrence, to be received by a second processing unit (e.g., the main processor 220 of FIG. 2A),
• an event for a second processing unit is generated, where the event is indicative of the reference occurrence.
• the term "event" describes information provided to a processing unit, indicative of a reference occurrence.
• the event is provided to a second processing unit.
• the event may simply include an indication that a reference occurrence has happened.
• the event may further include an indication of the type of reference occurrence that was detected.
• the even t may be generated by the first processing unit and sent to the second processing unit. In some embodiments, there may be intervening circuitry between the first and second processing units.
• FIG. 5 shows a simplified illustration of the sensor array unit 212 of FIG, 2A.
• pixels 510 are arranged in rows and columns and placed in the focal plane of a receiving optics to provide image capture. (For clarity, only a few pixels 510 in FIG. 5 have numerical labels.)
• features of the sensor array unit such as pixel size, aspect ratio, resolution, and the like can vary depending on desired functionality.
• the simplified illustration of FIG. 5 shows a 10x10 pixel array, but embodiments may have hundreds, thousands, or millions of pixels (or more).
• the sensor system 210 may be configured to detect one or reference occurrences and generate one or more corresponding events while it is performing in a lower-power operation.
• the sensor system 210 may be incorporated into a mobile phone and configured to detect a reference occurrence when a sensed value for the single pixel 310 indicates a significant increase in an amount of light detected by the sensor system 210. Such a change in the amount of detected light may indicate that the mobile phone has been retrieved from a user's pocket or has been picked up from a table or nightstand.
• the sensor system 210 can determine, while in lower-power operation, that this reference occurrence happened and generate an event indicative of the reference occurrence for the main processor 220.
• the sensor system 2.10 can further activate dedicated CV computation hardware to enable higher-power operation to perform different types of CV operations, such as face detection and face recognition.
• the sensor system 210 while performing the lower-power operation, the sensor system 210 detects a reference occurrence.
• the sensor system 210 In one example in which the sensor system 210 is configured to perform an ALS function, the sensor system 210 generates at least one lower-power optical sensor reading, which may be used to detect the reference occurrence.
• a lower-power optical sensor reading may indicate a change in an amount of ambient fight, and the sensor system 210 may detect a reference occurrence based on the lower-power optical sensor reading when a sensed level of light changes at a rate above a reference threshold, or changes color at a rate above a reference threshold.
• the sensor system 210 can be configured to perform a MD function.
• the sensor system 210 configures the sensor array unit 212 to have a reduced resolution greater than a 2x2 pixel resolution, but less than a maximum resolution of pixels in the sensor array unit 212.
• the sensor system 210 is configured to detect relative changes in sensed light at different effective pixels.
• the sensor system 210 analyzes an amount of light sensed at each of the effective pixels (e.g., subgroups 610 as shown in FIG. 6), determines a first set of differences between the amount of fight sensed at each effective pixel relative to at least one other effective pixel.
• the sensor system 210 provides a parameter for lower-power operation, ⁇ one embodiment, a higher-power operation may detect an object near the sensor system 210, and in some example systems may also determine an estimated distance to the object.
• the sensor system 210 may provide an event comprising a parameter to the lower-power operation indicating the presence of the object, or may also (or instead) provide a parameter indicating a distance to the object.
• a parameter may be employed by the lower-power operation to assist with or enhance a PD function.
• the PD function may be able to more accurately detect an object near the sensor based on the parameter, such as by establishing or adjusting a threshold intensit '- level.
• the sensor system 210 initiates a lower-power operation using the parameter.
• the sensor system 210 may initiate a lower-power operation as described above with respect to FIGS. 7 and 8.
• the lower- power operation after initiation, is configured to use the parameter.
• a PD function may be able to more accurately detect an object near the sensor based on the parameter, such as by establishing or adjusting a threshold intensity- level.
• the parameter may assist or enhance the lower-power operation, such as by assisting with an ALS function by pro viding information associated with a threshold for detecting changes in ambient lighting.
• Some embodiments may repeatedly execute the method 900. For example, after performing a higher-power operation, the sensor system 210 may restart the method 900 and initiate a lower-power operation at block 910.
• FIG. 10A shows an example state diagram for computer-vision computations and lower-power optical sensor readings, which may be performed by the sensor system 210.
• FIG. 10A includes two states, a lower-power operation(s) state 1010 and a higher- power operation(s) state 1020.
• the sensor system 210 In a lower-power operation(s) state 1010, the sensor system 210 is configured to perform one or more lower-power operations and may obtain one or more sensor readings.
• a higher-power operation(s) state 1020 the sensor system 2.10 is configured to perform one or more higher-power operations, such as computer- vision computations and operations, and may obtain one or more sensor readings.
• FIG. 12 illustrates an embodiment of a mobile device 105, which can utilize the sensor system 210 as described above. It should be noted that FIG. 12 is meant only to provide a generalized illustration of various components, any or all of which may be utilized as appropriate. It can be noted that, in some instances, components illustrated by FIG. 12 can be localized to a single physical device and/or distributed among various networked devices, which may be disposed at different physical locations.
• the mobile device 105 can further include sensor(s) 1240.
• sensors can include, without limitation, one or more aceeierometer(s), gyroscope(s), carnera(s), magnetometer(s), altimeter(s), microphone(s), proximity sensor(s), light sensor(s), and the like.
• the sensor(s) 1240 may include the sensor system 210 of FIGS. 2A or 2B and/or similar electronic sensors.
• a first processor e.g., microprocessor 216 in FIGS. 2A or 2B
• a first processing unit of sensor(s) 1240 can determine, from one or more signals received from the one or more outputs of an image array (e.g., sensor array unit 212 of FIGS. 2A or 2B), that a face has been detected, and in response to the determination, generate a face-detection event, for a second processing unit (e.g., processing unit(s) 1210 of FIG. 12).
• an image array e.g., sensor array unit 212 of FIGS. 2A or 2B
• a second processing unit e.g., processing unit(s) 1210 of FIG. 12
• Embodiments of the mobile device may also include a Satellite Positioning System (SPS) receiver 1280 capable of receiving signals 1284 from one or more SPS satellites using an SPS antenna 1282.
• the SPS receiver 1280 can extract a position of the mobile device, using conventional techniques, from satellites of an SPS system, such as a global navigation satellite system (GNSS) (e.g.. Global Positioning System (GPS)), Galileo, Glonass, Compass, Quasi-Zenith Satellite System (QZSS) over Japan, Indian Regional Navigational Satellite System (IRNSS) over India, Beidou over China, and/or the like.
• GNSS global navigation satellite system
• GPS Global Positioning System
• Galileo Galileo
• Glonass Galileo
• Glonass Galileo
• Glonass Compass
• QZSS Quasi-Zenith Satellite System
• IRNSS Indian Regional Navigational Satellite System
• Beidou Beidou over China
• the mobile de vice 105 may further include and/or be in communication with a memory 1260.
• the memory 1260 can include, without limitation, local and/or network accessible storage, a disk drive, a drive array, an optical storage device, a solid-state storage device, such as a random access memory (“RAM”), and/or a read-only memory (“ROM”), which can be programmable, flash-updateable, and/or the like.
• RAM random access memory
• ROM read-only memory
• Such storage devices may be configured to implement any appropriate data stores, including without limitation, various file systems, database structures, arid'Or the like.
• the memory 218 of FIG, 2A which can include any of the memory types pre viously listed, may be included in the memor j 2.60 or may be distinct fro memory 1260, depending on desired functionality,

Landscapes

• Engineering & Computer Science (AREA)
• Multimedia (AREA)
• Theoretical Computer Science (AREA)
• Physics & Mathematics (AREA)
• General Physics & Mathematics (AREA)
• Signal Processing (AREA)
• Human Computer Interaction (AREA)
• Computer Vision & Pattern Recognition (AREA)
• General Health & Medical Sciences (AREA)
• Health & Medical Sciences (AREA)
• General Engineering & Computer Science (AREA)
• Life Sciences & Earth Sciences (AREA)
• Software Systems (AREA)
• Biomedical Technology (AREA)
• Molecular Biology (AREA)
• Biodiversity & Conservation Biology (AREA)
• Social Psychology (AREA)
• Psychiatry (AREA)
• Data Mining & Analysis (AREA)
• Oral & Maxillofacial Surgery (AREA)
• Evolutionary Computation (AREA)
• Evolutionary Biology (AREA)
• Bioinformatics & Computational Biology (AREA)
• Bioinformatics & Cheminformatics (AREA)
• Artificial Intelligence (AREA)
• Image Analysis (AREA)
• Studio Devices (AREA)
• Image Processing (AREA)
• Length Measuring Devices By Optical Means (AREA)
• Geophysics And Detection Of Objects (AREA)
• Burglar Alarm Systems (AREA)

Priority Applications (8)

Applications Claiming Priority (8)

Publications (1)

Family

ID=55585883

Family Applications (1)

Country Status (7)

Cited By (3)

Families Citing this family (71)

Citations (1)

Family Cites Families (83)

• 2015
  • 2015-09-25 US US14/866,549 patent/US9554100B2/en active Active
  • 2015-09-28 CA CA2959549A patent/CA2959549C/en active Active
  • 2015-09-28 KR KR1020227042166A patent/KR102633876B1/ko active Active
  • 2015-09-28 WO PCT/US2015/052684 patent/WO2016053886A1/en not_active Ceased
  • 2015-09-28 JP JP2017516988A patent/JP6737777B2/ja active Active
  • 2015-09-28 EP EP15781806.3A patent/EP3201831B1/en active Active
  • 2015-09-28 KR KR1020177008531A patent/KR102474008B1/ko active Active
  • 2015-09-28 CN CN202511988764.0A patent/CN121617133A/zh active Pending
  • 2015-09-28 CN CN201580052384.4A patent/CN107077601A/zh active Pending
• 2017
  • 2017-01-03 US US15/397,566 patent/US9870506B2/en active Active
  • 2017-01-06 US US15/400,914 patent/US9986211B2/en active Active
• 2020
  • 2020-05-14 JP JP2020085260A patent/JP7009553B2/ja active Active

Patent Citations (1)

Non-Patent Citations (9)

Also Published As

Similar Documents

Legal Events