Abstract: A system for high definition (HD) image recognition of criminals is disclosed. The system includes a plurality of cameras, an image recognition server, investigator user devices, a computing device, a database, and a network. At least one processor of the image recognition server is configured to receive a plurality of photographs of a first individual, perform image processing of the plurality of photographs to extract a first set of physical features, store feature data regarding the first set of physical features in the database, receive suspect data regarding a suspected individual from a first investigator user device, match the suspect data with the feature data stored in the database, and transmit an alert to the computing device in the prison, wherein the alert activates a mobile application on each investigator user device to display match data identifying the suspected individual as the first individual based on the feature data.

Abstract: The present disclosure relates to the field of artificial intelligence technology, and to a method of estimating a pose of a device and a related device. The method of estimating a pose of a device includes identifying a similar key frame that is similar to a current frame collected by the device from a key frame set, based on a vector distance between the similar key frame and the current frame; obtaining data-related information between image frames based on a feature matching relationship between the current frame and the similar key frame; and obtaining the pose of the device based on the data-related information.

Abstract: A problem of imbalanced big data is solved by decoupling a classifier into a neural network for generation of representation vectors and into a classification model for operating on the representation vectors. The neural network and the classification model act as a mapper classifier. The neural network is trained with an unsupervised algorithm and the classification model is trained with a supervised active learning loop. An acquisition function is used in the supervised active learning loop to speed arrival at an accurate classification performance, improving data efficiency. The accuracy of the hybrid classifier is similar to or exceeds the accuracy of comparative classifiers in all aspects. In some embodiments, big data includes an imbalance of more than 10:1 in image classes. The hybrid classifier reduces labor and improves efficiency needed to arrive at an accurate classification performance, and improves recognition of previously-unrecognized images.

Abstract: A vehicle monitoring apparatus includes a hardware processor functioning as an image acquisition unit, an abnormality information acquisition unit, a damage determination unit, and a damage reporting unit. The image acquisition unit serves to acquire a captured image including an image representation of an exterior of a vehicle body of a vehicle. The abnormality information acquisition unit serves to acquire vehicle-body abnormality information indicating an abnormality in the vehicle body. The damage determination unit serves to determine presence or absence of damage to the vehicle body on the basis of the captured image and the vehicle-body abnormality information. The damage reporting unit serves to report, to an information processing apparatus, a damage report including the captured image or an image generated on the basis of the captured image. The damage report is reported in accordance with on a result of the determination on the damage.

Abstract: The present disclosure includes a method, apparatus, and non-transitory computer-readable medium for adaptive neural image compression by meta-learning. The method may include generating a substitute input image and a substitute target quality control parameter using an original input image and a target quality control parameter, wherein the substitute input image is a modified version of the original input image and the substitute target quality control parameter is a modified version of the target quality control parameter. The method may further include encoding the substitute input image, based on the substitute input image and the substitute target quality control parameter, using an encoding neural network, to generate a compressed representation of the substitute input image.

Abstract: This application provides an image defect detection method. The method includes obtaining a first image and a second image of a flawless image. A third image is obtained from the second image and the first image, and a fourth image is obtained according to the second image and an image to be detected. A fifth image is obtained based on the third image and the second image. A sixth image is obtained based on the third image and the fourth image. A seventh image is obtained from the fifth image and the sixth image. A defect value of the fourth image is obtained according to the third image and the seventh image. A detection result of the fourth image is determined based on the defect value.

Abstract: Systems and methods are disclosed for identifying a diagnostic feature of a digitized pathology image, including receiving one or more digitized images of a pathology specimen, and medical metadata comprising at least one of image metadata, specimen metadata, clinical information, and/or patient information, applying a machine learning model to predict a plurality of relevant diagnostic features based on medical metadata, the machine learning model having been developed using an archive of processed images and prospective patient data, and determining at least one relevant diagnostic feature of the relevant diagnostic features for output to a display.

Abstract: Techniques are provided for improving image data quality, such as in functional imaging follow-up studies, using reconstruction, post-processing, and/or deep-learning enhancement approaches in a way that automatically improves analysis fidelity, such as lesion tracking fidelity. The disclosed approaches may be useful in improving the performance of automatic analysis methods as well as in facilitating reviews performed by clinician.

Abstract: A geometric calibration apparatus detects points from projection regions onto which markers disposed on a phantom are projected, and calculates an output vector representing a probability distribution that gives a probability with which each point is a projection of each marker, by inputting data corresponding to each point to a learning model. The geometric calibration apparatus extracts a predetermined number of samples based on the probability distribution, obtains a candidate projection matrix by transforming correspondences between markers determined based on the samples among the markers and points determined based on the samples among the points, calculates points into which the markers are transformed by the candidate projection matrix, calculates a difference between a set of the transformed points and a set of the detected points, and designates the candidate projection matrix as a projection matrix when the difference is less than or equal to a threshold.

Abstract: An expression recognition method is described that includes acquiring a face image to be recognized, and inputting the face image into N different recognition models arranged in sequence for expression recognition and outputting an actual expression recognition result, the N different recognition models being configured to recognize different target expression types, wherein N is an integer greater than 1.

Abstract: A discrete attribute value dataset is obtained that is associated with a plurality of probe spots each assigned a different probe spot barcode. The dataset comprises spatial projections, each comprising images of a biological sample. Each image includes a corresponding plurality of discrete attribute values for the probe spots. Each such value is associated with a probe spot in the plurality of probes spots based on the probe spot barcodes. The dataset is clustered using the discrete attribute values, or dimension reduction components thereof, for a plurality of loci at each respective probe spot across the images of the projections thereby assigning each probe spot to a cluster in a plurality of clusters. Morphological patterns are identified from the spatial arrangement of the probe spots in the various clusters.

Abstract: An apparatus for detecting mounting behavior of an animal object includes: a memory that stores a program; and a processor that executes the program. The program extracts animal detection information about an animal object detected from the image by inputting the received image into an animal detection model. Also, the program extracts bounding boxes of which a distance between coordinates of central points is smaller than a first set value, bounding boxes of which a difference in rotational angle is smaller than a second set value, and bounding boxes of which a difference between a vector connecting the central points of the extracted bounding boxes and an orientation of each bounding box is smaller than a third set value. If activity information of the animal object is extracted based on an MHI of the image, it is determined that mounting behavior occurs.

Abstract: A method, apparatus and computer readable storage to implement an automated system for video surveillance in a casino or other controlled environment. Players in the casino can be automatically scanned and analyzed for whether they are under the legal gambling age or not. When an underage gambler is detected, a casino security employee (or other casino personnel) is notified so they can take the appropriate action. Similarly, players who are excluded from the casino can also be automatically detected and would be ejected when detected.

Abstract: There is provided a method for generating a learning model, the method including: acquiring an endoscopic image captured by an endoscope and manipulation information regarding a manipulation of an endoscope operator in each stage of operation of the endoscope by the endoscope operator operating the endoscope; and generating a learning model learned so as to output the manipulation information of a next stage in a case where the endoscopic image and the manipulation information are input, based on training data including the acquired endoscopic image and manipulation information, and the manipulation information of the next stage.

Abstract: An adaptive image shading correction method and an adaptive image shading system are provided. The method includes: configuring an image capturing device to obtain a current frame; and configuring a processing unit to: divide the current frame into blocks; select block pairs from the blocks, in which each of the block pairs includes an inner block and an outer block; perform a filtering process for each of the block pairs to determine whether a brightness condition, a saturation condition, a hue similarity condition, and a sharpness similarity condition are met; in response to obtaining filtered block pairs, calculate a sum similarity threshold based on hue statistical data, a saturation difference, and a brightness difference; and use filtered blocks with individual thresholds less than the sum similarity threshold to calculate a shadow compensation value to adjust the current frame.

Abstract: The present disclosure provides an apparatus for cylindrical convolutional neural network operation, and an apparatus and method for object recognition and viewpoint estimation using the same. The present disclosure uses a cylindrical convolutional network to recognize objects in the image and to determine the viewpoint, which cylindrical convolutional network performs a convolution operation while sliding the input image in the direction of rotation of the cylindrical kernel and extracts a plurality of view-specific feature vectors according to angular unit intervals, to identify and recognize objects, as well as to determine the viewpoint from which the objects were photographed.

Abstract: An object is to provide a travel environment analysis apparatus that accurately estimates an event occurring outside of a vehicle. A travel environment analysis apparatus includes a gaze point concentration area detector, a gaze point concentration area event estimation unit, and an information output unit. The gaze point concentration area detector sequentially detects a gaze point concentration area that is an area outside of a plurality of vehicles and gazed by occupants of the plurality of vehicles, based on line-of-sight information related to lines of sight of the occupants. The gaze point concentration area event estimation unit estimates, when a new gaze point concentration area which is a gaze point concentration area newly coming up is detected, an event occurring in the new gaze point concentration area. The information output unit outputs information of the new gaze point concentration area and information of the event.

Abstract: Disclosed is a system for analysis of microscopic image data acquired from biological cells. The system includes a data processing system which is configured to read the image data and determine a plurality of vertices, wherein each of the vertices represents a location of an entity of interest within a region of interest of the image data. The data processing system generates a plurality of graphs, wherein for each of the graphs, the generation of the respective graph includes generating a plurality of edges, wherein each of the edges has two of the plurality of vertices associated therewith. For each of the graphs one or more vertex sets are identified, each of which consisting of one or more of the plurality of vertices. The data processing system further determines, for each of the graphs, a number of the identified vertex sets.

Abstract: A classification model for identifying or classifying biological structures depicted in a base image can be generated by training a label generation model and then using the label generation model to train the classification model. The label generation model can be configured to accept co-registered base and informer images, while the classification model can be configured to accept base images. The classification model can output an indication when a biological structure is identified or classified in a base image.

Abstract: An AI-enhanced data labeling tool assists a human operator in annotating image data. The tool may use a segmentation model to identify portions to be labeled. Initially, the operator manually annotates portions and once the operator has labeled a sufficient number of portions, a classifier is trained to predict labels for other portions. The predictions generated by the classifier are presented to the operator for approval or modification. The tool may also include an active learning model that recommends portions of the image data for the operator to annotate next. The active learning model may suggest one or more batches of portions based on the extent to which, once labeled, those batches will increase the diversity of the total set of labeled portions.