The present invention relates to an automatic method to determine the authenticity of a product.

Systems and methods disclosed in this application are directed to augmented reality for used with pin badges. Pin badges can be worn, held, or set within view of an AR device having a camera. The AR device sends images or video from its camera to a platform server that determines whether a pin badge exists in view of the camera. If a pin badge exists, it is identified and augmented reality imagery related to the pin badge is transmitted back to the AR device so that the AR device can incorporate that augmented reality imagery into a video stream from its camera as shown on its display.

In some arrangements, product packaging is digitally watermarked over most of its extent to facilitate high-throughput item identification at retail checkouts. Imagery captured by conventional or plenoptic cameras can be processed (e.g., by GPUs) to derive several different perspective-transformed views—further minimizing the need to manually reposition items for identification. Crinkles and other deformations in product packaging can be optically sensed, allowing such surfaces to be virtually flattened to aid identification. Piles of items can be 3D-modelled and virtually segmented into geometric primitives to aid identification, and to discover locations of obscured items. Other data (e.g., including data from sensors in aisles, shelves and carts, and gaze tracking for clues about visual saliency) can be used in assessing identification hypotheses about an item. Logos may be identified and used—or ignored—in product identification. A great variety of other features and arrangements are also detailed.

A computing system for realizing visual content of an image collection executes feature detection algorithms and semantic reasoning techniques on the images in the collection to elicit a number of different types of visual features of the images. The computing system indexes the visual features and provides technologies for multi-dimensional content-based clustering, searching, and iterative exploration of the image collection using the visual features and/or the visual feature indices.

The inventors of the present application developed novel optically active materials, methods, and systems for reading identifying information on an optically active article. Specifically, the present application relates to substantially simultaneously capturing and/or processing a first optically active image and a second optically active image. In some embodiments, the first optically active image is taken at a first wavelength and the second optically active image is taken at a second wavelength, wherein the first wavelength is different from the second wavelength. In one aspect, the present applications relates to reading information on a license plate for purposes of vehicle identification.

A method for detecting a static logo of a video, an electronic apparatus and a storage medium. The method includes: calculating a pixel grayscale flag value, an edge gradient flag value and an edge direction flag value; calculating, in a preset neighborhood centered on the pixel at each pixel position of the current video frame, a first local confidence degree of the pixel grayscale flag value, a second local confidence degree of the edge gradient flag value, and a third local confidence degree of the edge direction flag value respectively; calculating a contribution score of each local confidence degree and a total contribution score of each pixel position; and gathering the total contribution score of each pixel position of the current video frame, and determining a static logo in the current video frame according to the total contribution score of each pixel position.

The present disclosure provides an image recognition method and apparatus, a device and a non-volatile computer storage medium. In embodiments of the present disclosure, it is feasible to obtain the to-be-recognized image of a designated size, extract the different-area image from the to-be-recognized image, and obtain the image feature of the different-area image according to the different-area image, so as to obtain the recognition result of the to-be-recognized image, according to the image feature of the different-area image and a preset template feature. In this way, recognition processing can be performed for images in a limited number of classes without employing the deep learning method based on hundreds of thousands of even millions of training samples.

A homography is determined between a test image and a plurality of database images based on a location of a character along a main line of text of the test image. A similarity score is determined for each of the plurality of database images based on the determined homography. The similarity score may measure a similarity of a characteristic between the test image and the corresponding database image. The characteristic may be at a location separate from the main line of text of the test image. A type of the test image may be selected based on the similarity scores.

An information processing apparatus includes a processor configured to extract from one or more electronic documents viewed before by a user a region of interest on which the user focuses attention and create a thumbnail of an electronic document to be viewed by the user in accordance with the region of interest by extracting an image corresponding to the region of interest from the electronic document.

The present invention addresses the problem of acquiring information regarding a component or product and identifying said component or product easily and inexpensively. The present invention has an image-feature storage, an extracting means, an acquiring means, and an identifying means. The image-feature storage stores image features of texture patterns formed on components or products. The extracting means extracts an n-dimensional-symbol image and a texture-pattern image from a taken image containing at least the following: an n-dimensional symbol (n being a natural number) that represents information regarding a component, a product, or a product comprising said component; and a texture pattern formed on said component or product. The acquiring means acquires, from the extracted n-dimensional-symbol image, the aforementioned information regarding the component or product. The identifying means identifies the component, product, or component-comprising product by matching image features of the extracted texture-pattern image against the image features stored by the image-feature storage.