Abstract: One or more computer processors creating a plurality of k-hop neighborhood contextual subgraphs utilizing extracted labelled nodes from an input graph; compute an eigenvector centrality score for each node contained in each created subgraph in the plurality of k-hop neighborhood contextual subgraphs; propagate a label for each node in each subgraph in the plurality of k-hop neighborhood contextual subgraphs leveraging an aggregated mathematical decay function, preserving a topical context of the label; calculate an attributable prestige vector for each node in each subgraph in the plurality of k-hop neighborhood contextual subgraphs based on the propagated label and the computed eigenvector centrality score associated with each node in each subgraph in the plurality of k-hop neighborhood contextual subgraph; and unsupervised predict a subsequent label for one or more subsequent nodes, subgraphs, or graphs utilizing the calculated attributable prestige vectors for each node in each subgraph.

Abstract: An image processing apparatus includes a hardware processor configured to: acquire a plurality of pieces of image information including image-capturing regions overlapping partially; to set first and second correction values with a predetermined value, the first correction value being used for correction of luminance of a first region of interest included in a first overlap region in which a first image-capturing region overlaps an adjacent second image-capturing region, the second correction value being used for correction of luminance of a second region of interest included in a second overlap region in which the first image-capturing region overlaps an adjacent third image-capturing region; and to set with the first and second correction values an individual correction value used for correction of luminance of a region between the first region of interest and the second region of interest in the first image-capturing region.

Abstract: Disclosed is a detection target positioning method and device. The method comprises: acquiring an original image and pre-processing the original image to obtain a gradation of each pixel in a target gradation image corresponding to a target region including a detection target; calculating first gradation sets corresponding to rows of pixels of the target gradation image and second gradation sets corresponding to columns of pixels of the target gradation image; and determining rows of two ends of the detection target in a column direction according to the first gradation sets, determining columns of two ends of the detection target in a row direction according to the second gradation sets, and determining a center of the detection target according to the row of two ends of the detection target in the column direction and the columns of two ends of the detection target in the row direction.

Abstract: According to one or more embodiments, operations may comprise obtaining a first point cloud. The operations also comprise performing segmentation of the first point cloud, the segmentation generating one or more clusters of points of the point cloud. The operations also comprise determining, for each respective cluster of the plurality of clusters, a respective geometric feature of a corresponding object that corresponds to the respective cluster. The operations also comprise obtaining a second point cloud. The operations also comprise assigning a plurality of weights that comprises assigning a respective weight to each respective cluster based on the respective geometric feature that corresponds to the respective cluster. The operations also comprise obtaining a second point cloud and aligning the first point cloud with the second point cloud based on the plurality of weights.

Abstract: Systems and methods for quality control in image-guided radiotherapy are provided. In some aspects, a method includes acquiring treatment images from a patient using an imaging system, and performing a registration using the treatment images and simulation images acquired during a simulation process. The method also includes computing at least one similarity metric based on the registration performed, and determining a conformance of the at least one similarity metric to predetermined limits. The method further includes generating a report indicative of the conformance.

Abstract: A device may obtain video data associated with a driving event involving a first vehicle. The device may determine a vanishing point associated with the video data and may construct a cone of impact of the first vehicle based on the vanishing point. The device may detect a second vehicle within the cone of impact and may analyze the subset of video frames to determine a distance between the first vehicle and the second vehicle. The device may determine a speed of the first vehicle during a time period associated with a subset of video frames. The device may determine a headway score, representative of a severity associated with the first vehicle being within a proximity threshold of the second vehicle during the time period, based on the distance and the speed. The device may determine an occurrence of a tailgating event based on the headway score.

Abstract: There is provided systems and methods for training a classifier. The method comprises: obtaining a classifier for classifying data into one of a plurality of classes; retrieving training data comprising a set of observations and a set of corresponding labels, each label representing an assigned class for a corresponding observation; and applying an agent trained by a reinforcement learning system to generate labeled data from unlabeled observations and train the classifier using the training data and the labeled data according to a policy determined by the reinforcement learning system.

Abstract: An image processing device includes: a reception unit that receives at least one first image provided from at least one first camera capturing an image of a region in which an object exists and a plurality of second images provided from a plurality of second cameras capturing images of a region including a dead region hidden by the object and invisible from a position of the first camera; and an image processing unit that generates a complementary image, as an image of a mask region in the at least one first image corresponding to the object, from the plurality of second images and generates a synthetic display image by combining the at least one first image and the complementary image.

Abstract: An image processing system has one or more memories and image processing circuitry coupled to the one or more memories. The image processing circuitry, in operation, compares a first image to feature data in a comparison image space using a matching model. The comparing includes: unwarping keypoints in keypoint data of the first image; and comparing the unwarped keypoints and descriptor data associated with the first image to the feature data of the comparison image. The image processing circuitry determines whether the first image matches the comparison image based on the comparing.

Abstract: The disclosure provides a pedestrian re-identification method based on a spatio-temporal joint model of a residual attention mechanism and a device thereof. The method includes: performing feature extraction for an input pedestrian with a pre-trained ResNet-50 model; constructing a residual attention mechanism network including a residual attention mechanism module, a feature sampling layer, a global average pooling layer and a local feature connection layer; calculating a feature distance by using a cosine distance and denoting the feature distance as a visual probability according to the trained residual attention mechanism network; performing modeling for a spatio-temporal probability according to camera ID and frame number information in a pedestrian tag of a training sample, and performing Laplace smoothing for a probability model; and calculating a final spatio-temporal joint probability by using the visual probability and the spatio-temporal probability to obtain a pedestrian re-identification result.

Abstract: Free space machine interface and control can be facilitated by predictive entities useful in interpreting a control object's position and/or motion (including objects having one or more articulating members, i.e., humans and/or animals and/or machines). Predictive entities can be driven using motion information captured using image information or the equivalents. Predictive information can be improved applying techniques for correlating with information from observations.

Abstract: A traffic signal information management system executes a region-of-interest calculation processing to calculate, based on a position information of a vehicle and a traffic signal information indicating the position of a traffic signal, a region of interest in which a traffic signal is presumed to be present in an image imaged by the camera, and also execute a traffic signal image detection processing to detect a traffic signal image included in the region of interest. The traffic signal information management system then execute an evaluation processing to perform a comparison between the position of the region of interest calculated by the region-of-interest calculation processing and the position of the traffic signal image detected by the traffic signal image detection processing and evaluate the certainty of the traffic signal information based on the comparison.

Abstract: A dynamic image analysis system includes a modality and a hardware processor. The modality generates first and second dynamic images each composed of frame images indicating dynamics of a subject. The processor detects a first signal value of a pixel in a first region in each of the frame images of the first dynamic image. The hardware processor detects a second signal value of a pixel in a second region in which an identical site to that of the first region is imaged, the second region being in each of the frame images of the second dynamic image. The processor determines an image processing condition for making the first signal value close to the second signal value based on the first and second signal values detected. The hardware processor performs image processing on the first dynamic image under the determined image processing condition to generate a processed dynamic image.

Abstract: A processor for an electronic endoscope includes: a region detection unit configured to detect an enhancement processing target region to be enhanced from pixel information of a captured image of a living tissue; and an enhancement processing unit configured to perform enhancement processing on the enhancement processing target region detected by the region detection unit. The region detection unit is configured to repeat a candidate extraction process of extracting a focused pixel as a candidate for an enhancement processing target region when a signal level value of the focused pixel is smaller than signal level values of two farthest pixels located on both sides farthest from the focused pixel in any one of a plurality of pixel array directions in a region surrounded by a frame surrounding a region with the focused pixel as a center while changing a size of the frame.

Abstract: Systems and methods are described herein for analyzing the application of adhesives. An adhesive comprising a fluorescent property is applied to a component. The component is illuminated using one or more of wavelengths of light from a light source, wherein the adhesive is configured to absorb the one or more wavelengths of light and fluoresce in response. An image of the component is captured using a camera while the component is illuminated using the light source. One or more fluorescence characteristics from the image are determined and a state of the applied adhesive is determined based on the one or more fluorescence characteristics.

Abstract: There is provided a method of defect detection on a specimen and a system thereof. The method includes: obtaining a runtime image representative of at least a portion of the specimen; processing the runtime image using a supervised model to obtain a first output indicative of the estimated presence of first defects on the runtime image; processing the runtime image using an unsupervised model component to obtain a second output indicative of the estimated presence of second defects on the runtime image; and combining the first output and the second output using one or more optimized parameters to obtain a defect detection result of the specimen.

Abstract: Methods and systems for determining and classifying a number of repetitive motions in a video are described, and include the steps of first determining a plurality of images from a video, where the images are segmented from at least one video frame of the video. Next, performing a pose detection process on a feature of the images to generate one or more landmarks. Next, determining one or more principle component axes on points associated with a given landmark. Finally, determining at least one repetitive motion based on a pattern associated with a projection of the points onto the one or more principle components. In some embodiments, the disclosed methods can classify the repetitive motions to respective types. The present invention can be implemented for convenient use on a mobile computing device, such as a smartphone, for tracking exercises and similar repetitive motions.

Abstract: An information processing apparatus, comprising: one or more processing devices; and one or more storage devices storing instructions for causing the one or more processing devices to: acquire observation information obtained through observation of a target region from a flying object flying in outer space; classify the target object by inputting the observation information acquired to a classifier so trained as to output a classification result obtained by classifying a target object present in the target region if the observation information is input; accept designation input for designating the target object; and output the observation information including a classification result of the target object designated.

Abstract: The information processing device includes a storage section storing a learnt model, a reception section, and a processor. The learnt model is obtained by mechanically learning the relationship between a sectional image obtained by dividing a voucher image and a type of a character string included in the sectional image based on a data set in which the sectional image is associated with type information indicating the type. The reception section receives an input of the voucher image to be subjected to a recognition process. The processing section generates a sectional image by dividing the voucher image received as an input and determines a type of the generated sectional image based on the learnt model.

Abstract: A retail store including a server having a processor and a memory; a communication network; and a database are disclosed. The server includes an electronic product recognizer that receives a video stream including a plurality of frames from a camera of an electronic mobile device. At least one of the plurality of frames is selected as a captured image. A plurality of products in the captured image is segmented into a plurality of segments. Image recognition is performed using each of the plurality of segments to identify the product in each of the plurality of segments. One or more recognized products identified in the image recognition are output. The one or more recognized products identified in the image recognition are configured to be sent to a user device communicable with the server via the communication network, the server configured to cause one or more stickers to be displayed on the user device.