Abstract: An automated method of inspecting a pipe includes: positioning the pipe with respect to a laser scanner using a positioning apparatus; scanning a size of the positioned pipe by the laser scanner; identifying a specification and historical data of the pipe's type by inputting the scanned size to an artificially intelligent module trained through machine learning to match input size data to standardized pipe types and output corresponding specifications and historical data of the pipe types; scanning dimensions of the positioned pipe by the laser scanner using a dimension portion of the identified historical data; comparing the scanned dimensions with standard dimensions from the identified specification; detecting a dimension nonconformity when the scanned dimensions are not within acceptable tolerances of the standard dimensions; and in response to detecting the dimension nonconformity, generating an alert and updating the dimension portion of the identified historical data to reflect the detected dimension n

Abstract: Systems and methods are provided for identifying a product in an image and outputting stock keeping units of the product. The system comprises three main components: a database server, a data analytics system and a standard dashboard. The database server contains real-time inventory images as well as historical images of each product type. The data analytics system is executed by a computer processor configured to apply a multi-head self-supervised learning-based classifier to detect product information captured by the image. The data analytics system is also configured to determine hierarchical classification categories for the product. The standard dashboard is configured to output a report regarding the product information.

Abstract: Systems and methods are disclosed for parsing resume documents using computer vision and optical character recognition technology in combination with a user feedback interface system to facilitate user feedback to improve the overall processing quality of the resumes that are imported into computer resume processing systems. In at least one embodiment, the system and method prompt a user to upload an input resume document, which is processed with a first parsing pass to generate initial resume data by extracting a plurality of resume text blocks. Further processing identifies an initial set of bounding blocks and to visually displays the initial resume data for user review and feedback to regroup one or more of the initial set of bounding blocks into a regrouped bounding block. Additional processing consolidates into a group text block each of the resume text blocks corresponding to the regrouped one or more of the initial set of bounding blocks.

Abstract: A video processing pipeline receives data derived from a feed of images of a plurality of objects passing in front of an inspection camera module forming part of a quality assurance inspection system. Quality assurance metrics for the object are generated by one or more containerized image analysis inspection tools forming part of the video processing pipeline using the received data for each object. Overlay images are later generated that characterize the quality assurance metrics. These overlay images are combined with the corresponding image of the object to generate an enhanced image of each of the objects. These enhanced images are provided to a consuming application or process for quality assurance analysis.

Abstract: An artificial intelligence (AI) system utilizing a machine learning algorithm such as deep learning for controlling an electronic device when a video is reproduced and a user's voice instruction is received, to acquire a frame corresponding to the time point when the input of the user's voice instruction is received, and obtain a search result for information on objects in the frame using an AI model trained according to at least one of machine learning, a neural network or a deep learning algorithm.

Abstract: A technique that automatically adjusts a motion given to a projection target using a perceptual model is provided. A motion vector generation apparatus includes a first parameter generation unit that generates a first parameter that is a parameter for scaling a motion vector based on a perceptual difference between a projection result reproduction image which is an image that is obtained when a projection target onto which a projection image obtained based on the motion vector has been projected is photographed and a warped image which is an image generated by distorting an image obtained when the projection target is photographed by a perceptual amount of motion perceived when the projection result reproduction image is viewed, and a motion vector reduction unit that scales the motion vector using the first parameter.

Abstract: A position of an object is determined by optically capturing at least one capture structure arranged at the object or at a reference object captured from the object and thereby obtaining capture information, the at least one capture structure having a point-symmetrical profile of an optical property that varies along a surface of the capture structure, transforming a location-dependent mathematical function corresponding to the point-symmetrical profile of the optical property into a frequency domain, forming a second frequency-dependent mathematical function from a first frequency-dependent mathematical function, wherein the second mathematical function is formed from a relationship of in each case a real part and an imaginary part of complex function values of the first frequency-dependent mathematical function, and forming at least one function value of the second frequency-dependent mathematical function and determining the same as location information about a location of a point of symmetry of the locati

Abstract: An example system for performing segmentation of data based on tensor inputs includes memory storing computer-executable instructions defining a learning network, where the learning network includes a plurality of sequential encoder down-sampling blocks. A processor is configured to execute the computer-executable instructions to receive a multi-dimensional input tensor including at least a first dimension, a second dimension and a plurality of channels. The processor is also configured to process the received multi-dimensional input tensor by passing the received multi-dimensional input tensor through the plurality of sequential encoder down-sampling blocks of the learning network, and to generate an output tensor in response to processing the received multi-dimensional input tensor. The output tensor includes at least one segmentation classification.

Abstract: Systems and methods for image processing are provided in the present disclosure. The systems and methods may obtain an image; determine a current resolution level of the image; determine, based on the current resolution level of the image, from a group of resolution level ranges, a reference resolution level range corresponding to the image; determine a target processing model corresponding to the reference resolution level range; and/or determine a processed image with a target resolution level by processing the image using the target processing model, the target resolution level of the processed image being higher than the current resolution level of the image.

Abstract: Described herein is a mechanism for visual recognition of items or visual search using Optical Character Recognition (OCR) of text in images. Recognized OCR blocks in an image comprise position information and recognized text. The embodiments utilize a location-aware feature vector created using the position and recognized information in each recognized block. The location-aware features of the feature vector utilize position information associated with the block to calculate a weight for the block. The recognized text is used to construct a tri-character gram frequency, inverse document frequency (TGF-IDP) metric using tri-character grams extracted from the recognized text. Features in location-aware feature vector for the block are computed by multiplying the weight and the corresponding TGF-IDF metric. The location-aware feature vector for the image is the sum of the location-aware feature vectors for the individual blocks.

Abstract: The method includes a step of acquisition of an image of the leading aircraft, a step of extraction of the outline, an adjustment step including implementing an outline registration algorithm, to determine an optimized profile from a comparison of the outline of the leading aircraft with a profile of the leading aircraft determined from a three-dimensional model of the leading aircraft and from an initial position of the following aircraft with respect to the leading aircraft, and a step of determination of the positioning according to six degrees of freedom of the following aircraft with respect to the leading aircraft, from the optimized profile and from the three-dimensional model of the leading aircraft. The method thus makes it possible to rapidly and accurately determine the positioning of the following aircraft with respect to the leading aircraft, this positioning being used to implement a formation flight.

Abstract: A method for processing a gaze signal in an eye tracking system is provided. The method comprises receiving a first image of a user's eye captured at a first point in time and a second image of the users eye captured at a second point in time subsequent to the first point in time, and determining, based on the first image and the second image, whether eye movement of the user's eye is in fixation or not. The method may further comprise to, on condition that the eye movement of the users eye is in fixation, applying a filter on the gaze signal, wherein the filter is adapted to decrease variance in the gaze signal.

Abstract: An image processing system includes a memory configured to store a plurality of reference images used for image quality tuning, an image signal processor configured to receive a plurality of captured images corresponding to the plurality of reference images and configured to generate a plurality of corrected images by being configured to perform a corresponding image processing operation among a plurality of image processing operations, and a tuning module configured to set parameters of the plurality of image processing operations based on the plurality of corrected images and the plurality of reference images.

Abstract: Embodiments of this application disclose a video data processing method and apparatus, and a storage medium. The method includes determining, in response to a trigger operation on a target video, a target pixel in a key video frame, and obtaining multimedia information associated with the target pixel, the key video frame being a video frame in which the trigger operation is located, and the target pixel corresponding to the trigger operation in the key video frame; identifying a trajectory obtaining request corresponding to the target pixel based on location information of the target pixel; obtaining target trajectory information associated with the location information of the target pixel, the target trajectory information comprising location information of the target pixel in a next video frame following the key video frame; and displaying the multimedia information based on the location information of the target pixel in the next video frame.

Abstract: A system for enhanced sports analytics and/or content creation includes: an object tracking system that generates coordinate data corresponding to object motion in a sports event; a data processing module that receives the coordinate data from the object tracking system, analyzes the coordinate data with an event recognition algorithm that identifies and characterizes events and outcomes of interest, and catalogs the data in accordance with the identified events and outcomes into event profile data; a database that receives and stores the event profile data generated by the data processing module; a user application that accesses the event profile data from the database; and at least one processing unit that executes instructions stored in at least one non-transitory medium to implement at least one of the object tracking system, the data processing module, or the user application.

Abstract: A statistical data processing device includes: a first statistical image generation unit for generating statistical images including a first statistical image representing a first statistical value as a corresponding pixel value, and a second statistical image representing a second statistical value as a corresponding pixel value; a mask generation unit for generating a mask image, the mask image extracting, if one of a pixel of a first statistical image and a corresponding pixel of a second statistical image does not have a pixel value indicating a statistical value, a pixel not having a pixel value indicating the statistical value or other pixel; and a second statistical image generation unit for generating a third statistical image in which a pixel value of a pixel not having a pixel value indicating the statistical value is complemented with a pixel value of the other statistical image.

Abstract: Among other things, techniques are described for cross-modality active learning for object detection. In an example, a first set of predicted bounding boxes and a second set of predicted bounding boxes is generated. The first set of predicted bounding boxes and the second set of predicted bounding boxes are projected into a same representation. The projections are filtered, wherein predicted bounding boxes satisfying a maximum confidence score are selected for inconsistency calculations. Inconsistencies are calculated across the projected bounding boxes based on filtering the projections. An informative scene is extracted based on the calculated inconsistencies. A first object detection neural network or a second object detection neural network is trained using the informative scenes.

Abstract: An apparatus for image reconstruction that includes input circuitry configured to acquire, from an image sequence, first and second 2D projection data of a region of interest of an object at respective acquisition times; and provide first 3D object data of a first object in a region of interest and second 3D object data of a second object in a region of interest. The apparatus also includes a processor configured to generate a first registration of the first and/or second 3D object data to the first 2D projection data and a second registration of the first and/or second 3D object data to the second 2D projection data; provide a vector field defining motion of the second object relative to the first object between the acquisition times based on the registrations; generate corrected projection data using the vector field; and generate a motion-compensated image sequence based on the corrected projection data.

Abstract: A detection system includes a filter and a detector. The filter receives input packets from one or more dynamic vision sensors (DVSs) deployed in an area. The filter reduces the bandwidth of the input packets to produce compressed data packets while maintaining good end-to-end detection capability. The compressed data packets are transmitted to a detector in a low bandwidth transmission. The detector may collect compressed data packets from many filters. The detector determines if an object of interest is represented in the information of the input packets. If an object of interest is present, the detector activates an alarm, sends a notification to a person associated with the area and/or stores an indication in a database.

Abstract: Provided are an object tracking system and an object tracking method.