A sparse video inference chip is designed to extract spatio-temporal features from videos for action classification and motion tracking. The core is a sparse video inference processor that implements recurrent neural network in three layers of processing. High sparsity is enforced in each layer of processing, reducing the complexity by two orders of magnitude and allowing all multiply-accumulates (MAC) to be replaced by select-accumulates (SA). The design is demonstrated in a 3.98 mm2 40 nm CMOS chip with an Open-RISC processor providing software-defined control and classification.

The present invention relates to a method 100 for object detection (140), recognition, classification and tracking using a distributed networked architecture comprising one or more sensor units (20) wherein the image acquisition and the initial feature extraction are performed and a gateway processor (30) for further data processing. The present invention also relates to a vision system (10) for object detection (140) wherein the method may be implemented, to the devices of the vision system (10), and to the algorithms implemented in the vision system (10) for executing the method acts.

Systems and methods for image processing are configured. Embodiments of the present disclosure encode a content image and a style image using a machine learning model to obtain content features and style features, wherein the content image includes a first object having a first appearance attribute and the style image includes a second object having a second appearance attribute; align the content features and the style features to obtain a sparse correspondence map that indicates a correspondence between a sparse set of pixels of the content image and corresponding pixels of the style image; and generate a hybrid image based on the sparse correspondence map, wherein the hybrid image depicts the first object having the second appearance attribute.

Systems, and method and computer readable media that store instructions for configuring spanning elements of a signature generator.

An example wear detection system receives a left image and a right image of a bucket of a work machine having at least one ground engaging tool (GET). The example system identifies a first region of interest from the left image corresponding to the GET and a second region of interest from the right image corresponding to the GET. The example system also generates a left-edge digital image corresponding to the first region of interest and a right-edge digital image corresponding to the second region of interest. Further, the example system determines a sparse stereo disparity between the left-edge digital image and the right-edge digital image, and also determines a wear level or loss for the at least one GET based on the sparse stereo disparity.

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.

The present invention provides an efficient image classification method based on structured pruning, which incorporates a spatial pruning method based on variation regularization, including steps such as image data preprocessing, inputting images to neural network, image model pruning and retraining, and new image class predication and classification. The present invention adopts a structured pruning method that removes unimportant weight parameters of the original network model and reduces unnecessary computational and memory consumptions caused by the network model in image classification to simplify the image classifier, and then uses the sparsified network model to predict and classify new images. The simplified method according to the present invention improves the original network model in image classification efficiency by nearly two times, costs about 30% less memory consumption and produces a better classification result.

A method for urban scene reconstruction uses the top view of a scene as priori information to generate a UVA initial flight path, optimizes the initial path in real time, and realizes 3D reconstruction of the urban scene. There are four steps: (1): to analyze the top view of a scene, obtain the scene layout, and generate a UAV initial path; (2): to reconstruct the sparse point cloud of the building and estimate the building height according to the initial path, combine the scene layout to generate a rough scene model, and adjust the initial path height; (3): to use the rough scene model, sparse point cloud and the UAV flight trajectory to obtain the scene coverage confidence map and the details that need close-ups, optimize the flight path in real time; and (4): to obtain high resolution images, reconstruct them to obtain a 3D model of the scene.

A method includes accessing image data and depth data corresponding to image frames to be displayed on an extended reality (XR) display device, and determining sets of feature points corresponding to the image frames based on a multi-layer sampling of the image data and the depth data. The method further includes generating a set of sparse feature points based on an integration of the sets of feature points. The set of sparse feature points are determined based on relative changes in depth data with respect to the sets of feature points. The method further includes generating a set of sparse depth points based on the set of sparse feature points and the depth data and sending the set of sparse depth points to the XR display device for reconstruction of a dense depth map corresponding to the image frames utilizing the set of sparse depth points.

An exemplary system for generating an image(s) of a sample(s) can include, for example, an imaging arrangement that can include a superluminescent diode (SLD) configured to generate a radiation(s) to be provided to the sample(s), and a spectrometer configured to (i) sample an A-line sampling rate of at least about 200kHz, (ii) receive a resultant radiation from the sample(s) based on the sampling rate, and (iii) generate information based on the resultant radiation, and a computer hardware arrangement configured to generate the image(s) of the sample(s) based on the information received from the spectrometer. The imaging arrangement can be an interferometric imaging arrangement, which can be an optical coherence tomography imaging (OCT) arrangement. The computer hardware arrangement can be further configured to facilitate a plurality of b-scan acquisitions of the sample(s) and facilitate the b-scan acquisitions in order to generate the image(s).