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: 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: 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: 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: 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: 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: 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 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: 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 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.

Abstract: A data noise reduction method and apparatus are provided. In some embodiments, the method includes: obtaining to-be-processed point cloud data; for each of points in the to-be-processed point cloud data, determining a point feature corresponding to the point, the point feature comprising at least one of a spatial distribution difference feature between the point and other points adjacent to the point or a point group distribution feature corresponding to a point group comprising all points in a local space in which the point is located; recognizing a noise point from the to-be-processed point cloud data according to point features corresponding to the points in the to-be-processed point cloud data; and performing noise reduction on the to-be-processed point cloud data according to the recognized noise point.

Abstract: A method for providing a viral infection status of at least one cell in a cell sample comprising receiving holographic information (304) of the cell sample obtained by digital holographic microscopy, and determining from the holographic information, the viral infection status (306) of the at least one cell.

Abstract: Systems, methods and computer program products are provided to collect ultrasound (US) data. A processor is configured to acquire the US data along one or more acquisition scan planes. The US data defines a plurality of image frames that have a first image quality. The processor is further configured to apply a generative model to at least one of the US data or plurality of image frames to generate a synthetic scan plane image along a synthetic scan plane. The generative model is defined based on one or more training ultrasound data sets. The synthetic scan plane image has an image quality that is common with the first image quality of the plurality of image frames. The system further comprises a display configured to display the synthetic scan plane image.

Abstract: A distributed systems and methods for generating composite media including receiving a media context that defines media that is to be generated, the media context including: a definition of a sequence of media segment specifications and, an identification of a set of remote devices. For each media segment specification, a reference segment may be generated and transmitted to at least one remote device. A media segment may be received from each of the remote device, the media segment having been recorded by a camera. Verified media sequences may replace the corresponding reference segment. The media segments may be aggregated and an updated sequence of media segments may be defined. An instance of the media context that includes a subset of the updated sequence of media segments may then be generated.

Abstract: The present technology relates to an image processing apparatus and method that are capable of enhancing encoding efficiency while suppressing a decrease in the efficiency of encoding processing. The image processing apparatus includes an encoding mode setter that sets, in units of coding units having a hierarchical structure, whether a non-compression mode is to be selected as an encoding mode for encoding image data, the non-compression mode being an encoding mode in which the image data is output as encoded data, and an encoder that encodes the image data in units of the coding units in accordance with a mode set by the encoding mode setter. The present disclosure can be applied to, for example, an image processing apparatus.

Abstract: An illustrative example embodiment of a detector device, which may be useful on an automated vehicle, includes an array of detectors arranged in one dimension. The array includes a plurality of first detectors and a plurality of second detectors. The first detectors respectively have one of the second detectors between the first detector and an adjacent one of the first detectors. The first detectors respectively are spaced from the one of the second detectors by a first distance. The one of the second detectors are respectively spaced from the adjacent one of the first detectors by a second distance that is larger than the first distance. The first detectors are spaced from each other by a third distance that is a sum of the first and second distance. The second detectors are also spaced from each other by the third distance.