Abstract: The disclosure is inputting a first image captured an image of an authentication target; inputting a second image captured an image of a right eye or a left eye of the target; determining whether the second image is of a left eye or a right eye of the target based on information including the first image, and outputting a determination result as left/right information in association with the second image; detecting an overlap between a region including the second image and a predetermined region in the first image; calculating a verification score by comparing characteristic information that are related to the left/right information with iris characteristic information calculated from the second image, and calculating a first weighted verification score obtained by weighting the verification score with a detection result; and authenticating a target in the second image based on the first weighted verification score, and outputting an authentication result.

Abstract: An image processing device 100 of the present invention includes an image generation means 121 for generating a difference image representing a difference between an object image that is an image including an area from which a mobile body is to be detected and a corresponding image that is another image including an area corresponding to the area of the object image, and a detection means 122 for detecting the mobile body from the object image on the basis of the object image, the corresponding image, and the difference image.

Abstract: The learning device learns a tracking model used for a radar device. A first acquisition unit acquires, from a radar device, a target detection information indicating a position of a target detected based on a received wave, and a track of the target calculated based on the target detection information. A second acquisition unit acquires a target position information indicating a position of the target. A learning data generation unit generates learning data using the target detection information, the track, and the target position information. A learning processing unit learns a tracking model for performing tracking processing of the target based on the target detection information, using the learning data.

Abstract: According to the present disclosure, it is an object to provide, in particular, an image processing apparatus, an image processing system, an image processing method, and a non-transitory computer-readable medium storing an image processing program therein, capable of tracking an object in a video with high accuracy. An image processing apparatus according to the present disclosure includes: image feature calculation means for outputting an image feature of an object using a time-series image of the object; object identification means for outputting an identification result obtained by identifying an object using the image feature and a tracking result of the object; kinetic model selection means for selecting an appropriate kinetic model from a plurality of kinetic models based on the identification result and the tracking result; and object tracking means for tracking the object in time series and calculating the tracking result, from the identified object.

Abstract: An image processing apparatus according to the present invention includes: an extracting unit configured to extract a candidate image, which is an image of a candidate region specified in accordance with a preset criterion, from a target image to be a target for an annotation process, and also extract a corresponding image, which is an image of a corresponding region corresponding to the candidate region, from a reference image that is an image corresponding to the target image; a displaying unit configured to display the candidate image and the corresponding image so as to be able to compare the images with each other; and an input accepting unit configured to accept input of input information for the annotation process for the candidate image.

Abstract: The learning device learns the target detection model used in the radar device. The learning device includes an acquisition unit, a learning data generation unit, and a learning processing unit. The acquisition unit acquires a reception signal generated based on the received wave and a tracking signal generated based on the reception signal from the radar device. The learning data generation unit generates learning data using the reception signal and the tracking signal. The learning processing unit learns a target detection model that detects a target from the reception signal, using the learning data.

Abstract: To provide a system, a method and the like for acquiring more images or more quickly focused images. A control device acquires information regarding a situation of a range captured by an imaging device, determines a mode according to an assumed situation of the range captured by the imaging device among a plurality of focusing modes, and transmits designation information specifying the determined mode to the imaging device. The imaging device receives the designation information from the control device, and captures an image using the mode specified by the received designation information.

Abstract: The image recognition apparatus includes an image selection unit and a recognition unit. The image selection unit selects a feature image representing a feature portion of an object from among captured images of a time series in which the object is photographed. For example, the feature image corresponds to an image showing an abnormal portion. The recognition unit performs a recognition process of the object using the feature image. By the recognition process, an abnormality of the object is detected.

Abstract: The learning device includes an acquisition unit, a learning data generation unit, and a learning processing unit. The acquisition unit acquires operation data generated during an operation of a radar device and the operation history data indicating operations performed by an operator on the radar device from the radar device. The learning data generation unit generates the learning data using the operation data and the operation history data. The learning processing unit learns an operation determination model that determines an operation to be performed on the radar device based on the operation data, using the learning data.

Abstract: An authentication system according to one aspect of the present disclosure includes: at least one memory storing a set of instructions; and at least one processor configured to execute the set of instructions to: track an object included in a video captured by a first capture device; detect a candidate for biometric authentication in the object being tracked; determine whether biometric authentication has been performed for the candidate based on a record of biometric authentication performed for the object being tracked; and perform the biometric authentication for the candidate based on a video of an authentication part of the candidate when the biometric authentication has not been performed for the candidate, the video of the authentication part being captured by a second capture device having a capture range including a part of a capture range of the first capture device.

Abstract: The disclosure is inputting a first image obtained by capturing an object of authentication moving in a specific direction; inputting a second image at least for one eye obtained by capturing a right eye or a left eye of the object; determining whether the second image is of the left eye or the right eye of the object, based on information including the first image, and outputting a determination result associated with the second image as left/right information; comparing characteristic information relevant to the left/right information, the characteristic information being acquired from a memory that stores the characteristic information of a right eye and a left eye pertaining to object to be authenticated, with characteristic information associated with the left/right information, and calculating a verification score; and authenticating the object captured in the first image and the second image, based on the verification score, and outputting an authentication result.

Abstract: A learning device makes an object detection device learn how to detect an object from an input image. A feature extraction unit performs feature extraction from input images including real images and pseudo images to generate feature maps, and the object detection unit detects objects included in the input images based on the feature maps. The domain identification unit identifies the domains forming the input images and generates domain identifiability information. Then, the feature extraction unit and the object detection unit learn common features that do not depend on the difference in domains, based on the domain identifiability information.

Abstract: To provide a system, a method and the like for acquiring more images or more quickly focused images. A control device acquires information regarding a situation of a range captured by an imaging device, determines a mode according to an assumed situation of the range captured by the imaging device among a plurality of focusing modes, and transmits designation information specifying the determined mode to the imaging device. The imaging device receives the designation information from the control device, and captures an image using the mode specified by the received designation information.

Abstract: The disclosure is inputting a first image obtained by capturing an object of authentication moving in a specific direction; inputting a second image at least for one eye obtained by capturing a right eye or a left eye of the object; determining whether the second image is of the left eye or the right eye of the object, based on information including the first image, and outputting a determination result associated with the second image as left/right information; comparing characteristic information relevant to the left/right information, the characteristic information being acquired from a memory that stores the characteristic information of a right eye and a left eye pertaining to object to be authenticated, with characteristic information associated with the left/right information, and calculating a verification score; and authenticating the object captured in the first image and the second image, based on the verification score, and outputting an authentication result.

Abstract: A visualized image display device includes an acquisition means for acquiring time-series feature data in which time-series data of a feature of an object in a video and an object ID are associated with each other, and classification data in which the object ID and a class ID labeled to the object are associated with each other; a selection means for selecting a class ID to be visualized, and a display control means for acquiring the object ID associated with the selected class ID from the classification data, acquiring time-series data associated with the acquired object ID from the time-series feature data, and on the basis of the acquired time-series data, displaying a feature image representing a temporal change of the feature of the object having the selected class ID, on a display unit.

Abstract: An authentication system according to one aspect of the present disclosure includes: at least one memory storing a set of instructions; and at least one processor configured to execute the set of instructions to: track an object included in a video captured by a first capture device; detect a candidate for biometric authentication in the object being tracked; determine whether biometric authentication has been performed for the candidate based on a record of biometric authentication performed for the object being tracked; and perform the biometric authentication for the candidate based on a video of an authentication part of the candidate when the biometric authentication has not been performed for the candidate, the video of the authentication part being captured by a second capture device having a capture range including a part of a capture range of the first capture device.

Abstract: In order to improve the learning performance of a neural network model, this image recognition model generating device is provided with an input image patch determining unit, a similar patch searching unit, a pixel value generating unit, and a convolution processing unit. The input image patch determining unit determines an input image patch containing a border region in contact with the outside of a boundary line of an input image. The similar patch searching unit searches for a similar patch that is similar to the input image patch. The pixel value generating unit generates a pixel value complementing the border region, on the basis of the similar patch. The convolution processing unit performs a convolution process from the generated pixel value and pixel values of the input image.

Abstract: A system includes: a sequential image string input unit configured to input a sequential image string having sequentiality; a reference image selection unit configured to select one or more images from the sequential image string as reference images; a variation calculation unit configured to select an adjacent reference image adjacent to the reference image from the sequential image string and calculate a variation between the reference image and the adjacent reference image; an image information regression unit configured to calculate class confidence by regression processing with the reference image as an input; a difference image information regression unit configured to calculate class confidence by regression processing with the variation as an input; a confidence integration unit configured to integrate class confidence calculated by the image information regression unit and class confidence calculated by the difference image information regression unit; and an output unit configured to output the inte

Abstract: The learning apparatus classifies target domain data into (N-c) classes based on unique features of the target domain data, classifies source domain data into N classes based on unique features of the source domain data, and classifies the target domain data and the source domain data into the N classes based on common features of the target domain data and the source domain data. Also, the learning apparatus calculates a first distance between the common features of the target domain data and the source domain data, and calculates a second distance between the unique features of the target domain data and the source domain data. Next, the learning apparatus updates parameters of a common feature extraction unit based on the first distance, and updates parameters of a target domain feature extraction unit and a source domain feature extraction unit based on the second distance.

Abstract: A learning apparatus includes a metric space learning unit and a case example storage unit. The metric space learning unit learns a metric space including feature vectors extracted from sets of attribute-attached image data for each combination of different attributes by using the sets of attribute-attached image data to which pieces of attribute information are added. The case example storage unit calculates feature vectors from sets of case example image data, and stores the feature vectors as case examples associated with the metric space.