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.

Abstract: Methods and systems for computed tomography provide advances in efficiency. The methods operating within the parallel-beam geometry, rather than a divergent beam geometry, for the majority of the operations. In back projection methods perform digital image coordinate transformations on selected intermediate-images, the parameters of one or more coordinate transformations being chosen such that Fourier spectral support of the intermediate-image is modified so that a region of interest has an increased extent along one or more coordinate direction and the aggregates of the intermediate-images can be represented with a desired accuracy by sparse samples.

Abstract: An unmarked body motion tracking system based on multi-Kinect V2 is provided and includes: Kinect V2s, configured to acquire depth data and joint data; analysis modules, configured to analyze data collection characteristics of single Kinect V2 and transmission device assembly requirements; clients, configured to receive and process data collected by Kinect V2s. A process of the data processing includes: based on the data collection characteristics of the single Kinect V2, a client-server model is built to track a human body from different directions; output modules, configured to output layout modes and tracking results of the Kinect V2s. According to the data collection characteristics of the single Kinect V2 and the transmission device assembly requirements, the disclosure minimizes the mutual interference between the opposite Kinect V2s, tracks the human body from different directions, and ensures that the joints in the self-occlusion state are stably tracked by sensors at other angles.

Abstract: Systems and methods for object detection. Object detection may be used to control autonomous vehicle(s). For example, the methods comprise: obtaining, by a computing device, a LiDAR dataset generated by a LiDAR system of autonomous vehicle; and using, by the computing device, the LiDAR dataset and image(s) to detect an object that is in proximity to the autonomous vehicle. The object is detected by performing the following operations: computing a distribution of object detections that each point of the LiDAR dataset is likely to be in; creating a plurality of segments of LiDAR data points using the distribution of object detections; merging the plurality of segments of LiDAR data points to generate merged segments; and detecting the object in a point cloud defined by the LiDAR dataset based on the merged segments. The object detection may be used by the computing device to facilitate at least one autonomous driving operation.

Abstract: In implementations of the subject matter described herein, a solution for object detection is proposed. First, a feature(s) is extracted from an image and used to identify a candidate object region in the image. Then another feature(s) is extracted from the identified candidate object region. Based on the features extracted in these two stages, a target object region in the image and a confidence for the target object region are determined. In this way, the features that characterize the image from the whole scale and a local scale are both taken into consideration in object recognition, thereby improving accuracy of the object detection.

Abstract: Aspects of the present disclosure include methods, systems, and non-transitory computer readable media that perform the steps of receiving a first plurality of snapshots, generating a first plurality of descriptors each associated with the first plurality of snapshots, grouping the first plurality of snapshots into at least one cluster based on the plurality of descriptors, selecting a representative snapshot for each of the at least one cluster, generating at least one second descriptor for the representative snapshot for each of the at least one cluster, wherein the at least one second descriptor is more complex than the first plurality of descriptors, and identifying a target by applying the at least second descriptor to a second plurality of snapshots.

Abstract: A method for tracking movement of a presenter during a videoconference comprises tracking the location of subject of interest; quickly detecting error conditions—such as can occur when a subject of interest moves too quickly or when a subject of interest walks past another person—and quickly reinitiating the tracking sequence to help ensure a positive videoconferencing experience.

Abstract: Noise reduction in images is provided by performing a noise reduction step on blocks of pixels within a video-processing pipeline. The noise reduction step consists of applying a discrete cosine transform (DCT) to the block of pixels, quantizing the resulting DCT coefficients, and performing an inverse of the DCT to the quantized coefficients. The output of that noise reduction step is a block of image pixels similar to the input pixels, but with significantly less image noise. Because the noise reduction step can be performed quickly on small blocks of pixels, the noise reduction can be performed in real-time in a video processing pipeline.

Abstract: Embodiments of the present disclosure disclose an anomaly detector for detecting an anomaly in a sequence of poses of a human performing an activity. The anomaly detector includes an input interface configured to accept input data indicative of a distribution of the sequence of poses, a memory configured to store a discriminative one-class classifier having a pair of complementary classifiers bounding normal distribution of pose sequences in a reproducing kernel Hilbert space (RKHS), a processor configured to embed the input data into an element of the RKHS and classify the embedded data using the discriminative one-class classifier, and an output interface configured to render a classification result.