A method performed by an image registration entity includes obtaining a matching between a first set of objects in a first image of a scene and a second set of objects in a second image of the scene, and obtaining a first set of key-points from the first image and a second set of key-points from the second image. Image registration is performed by matching the first set of key-points to the second set of key-points. Those of the first set of key-points that are mapped to objects in the first set of objects and that have matching objects in the second set of objects are restricted to only be matched to those of the second set of key-points that are mapped to any of the matching objects in the second set of objects. The matching between key-points resulting from the image registration is applied when constructing an image representation of the scene.

An image matching system includes a non-transitory computer-readable medium and a processor. The non-transitory computer-readable medium is configured to store information of a plurality of images. The processor is configured to identify an object area in an original image that illustrates an object. The processor is configured to normalize the object area, resulting in a normalized image. The processor is configured to calculate a shape vector and a color vector from the normalized image. The processor is configured to calculate a match score using the shape vector and the color vector. The processor is configured to determine if the non-transitory computer-readable medium stores an identical match for the original image based on the match score.

The Loupe system defines Loupe Visual Art DNA for art images to be presented to a user so as to maximize and customize the user experience in viewing art images delivered onto digital displays, TVs and other screens facilitating the artwork transition with and without human interaction. The Loupe system recommendations engine utilizes both human and machine curated data to determine factors of art images that will appeal to a user viewing the images. The Loupe system gathers data about visual perception, historical and academic provenance, and emotion or intention represented in an image. The gathered data is analyzed through deep learning and AI algorithms to inform recommendations and select art images to be presented to a user. The user may purchase fine art prints or select originals of the artwork image displayed, if the artist elects to make it available for sale, presented from the Loupe integrated electronic marketplace.

The present invention discloses a system and a method for language independent automatic license plate localization by analysis of plurality of images in real-time under day-light condition without using any external light. In one embodiment, the system can work without any spatiality constraints and/or demographic considerations without any restriction on jurisdiction and can effectively localize license plates (LPs) of any type consisting of alpha-numeric characters and symbols. In other embodiment, methods for search-space reduction system based on motion-based filtration and LPs localization based on edge-active-region filtration with high frame-per-second (FPS) throughput are described. In another embodiment, a dual-binarization scheme is described for color invariant LP localization.

Systems, methods, computer-readable media, techniques, and methodologies are disclosed for performing end-to-end, learning-based keypoint detection and association. A scene graph of a signalized intersection is constructed from an input image of the intersection. The scene graph includes detected keypoints and linkages identified between the keypoints. The scene graph can be used along with a vehicle's localization information to identify which keypoint that represents a traffic signal is associated with the vehicle's current travel lane. An appropriate vehicle action may then be determined based on a transition state of the traffic signal keypoint and trajectory information for the vehicle. A control signal indicative of this vehicle action may then be output to cause an autonomous vehicle, for example, to implement the appropriate vehicle action.

The present invention relates to a detection method for an assembly body multi-view change based on feature matching, comprising the following steps: S1, acquiring a first image and a second image; S2, performing feature point extraction and feature matching on the first image and the second image to obtain a matching pair set, a first unmatched point set of the first image and a second unmatched point set of the second image; S3, acquiring a first to-be-matched area set of the first image according to the first unmatched point set; acquiring a second to-be-matched area set of the second image according to the second unmatched point set; S4, performing feature matching on each first unmatched area and each second unmatched area one by one to obtain a plurality of matching results; and S5, outputting the assembly body change type according to the plurality of matching results.

Disclosed in the present specification is a self-checkout device to which hybrid product recognition technology is applied and which helps a user to more conveniently and quickly make a payment. The self-checkout device according to the present specification photographs a product with a plurality of cameras, and then recognizes a barcode by detecting a barcode region from the captured images and, simultaneously, extracts a feature point of the images such that a product can be recognized through the calculation of the proportion that matches with a reference image of the product. Thus, a product can be quickly recognized through barcode recognition and packaging paper recognition in a product image.

It is an object of the present invention to provide an object collating device and an object collating method that enable matching of images of a dividable medical article with desirable accuracy and easy confirmation of matching results. In the object collating device according to the first aspect, when the object is determined to be divided, the first image for matching is collated with the image for matching (the second matching image) for the objects in the undivided state, so that the region to be matched is not narrowed, and matching of the images of the dividable medical article is achieved with desirable accuracy. In addition, since the first and second display processing is performed on the images for display determined to contain the objects of the same type, matching results can easily be confirmed.

An image capture apparatus detects a subject in a captured image. The image capture apparatus further recognizes its user based on an eyeball image of the user. The image capture apparatus then selects a main subject area from among the detected subject areas, based on information regarding subjects captured in the past and stored being associated with the recognized user.

Provided is a guide display device with which incorrect recognition of a place having small features as a ground surface having no features can be suppressed in generating and updating a three-dimensional map. A data processing unit (70): detects an edge (Eg) of a feature (E) on the basis of a result of subjecting image data captured by a camera (61) to image processing, and recognizes the feature (E) on the basis of the edge (Eg); and accumulates, from a predetermined number of frames from before the time at which the image data was captured, point group data (P) of a nearby range (Ma) of the edge (Eg) of the feature (E) that was recognized as a ground surface (F), and generates, on the basis of the accumulated point group data (P), a three-dimensional map (M) of the feature (E) the edge (Eg) of which has been recognized.