Abstract: A semiconductor image classifier. Convolution functions are applied to modify the wafer images in order to extract key information about the image. The modified images are condensed then processed through a series of pairwise classifiers, each classifier configured to determine that the image is more like one of the pair than the other. Probabilities from each classifier are collected to form a prediction for each image.

Abstract: Techniques for generating an arbitrary view or perspective of an ensemble scene are disclosed. In some embodiments, in response to a received request for a prescribed perspective of an ensemble scene comprising a plurality of assets, an output image of the ensemble scene for the requested prescribed perspective is generated based at least in part on combining at least a portion of an existing image of each of at least a subset of the plurality of assets.

Abstract: Aspects of the disclosure provide methods and apparatuses for point cloud compression and decompression. In some examples, an apparatus for point cloud compression/decompression includes processing circuitry. In some examples, the processing circuitry receives a bitstream carrying compressed data for a point cloud. The processing circuitry determines that a current node in an octree structure is eligible for an isolated mode. The octree structure corresponds to three dimensional (3D) partitions of a space of the point cloud. Then the processing circuitry determines, based on information of one or more other nodes, a single isolated point flag for the current node that indicates whether the current node is coded with a single isolated point.

Abstract: Embodiments of this application provide a measurement method and a measurement apparatus. The measurement method includes: acquiring a first image and a second image of a target object, where the first image is acquired by a camera located on a non-backlight side of the target object, and the second image is acquired by a camera located on a backlight side of target object; and measuring the target object for size information according to the first image and the second image. The technical solution of this application can improve accuracy and precision of inspection while improving production efficiency.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a classification neural network.

Abstract: An image compression sampling method and assembly are provided. The method includes: performing sparse representation on a target image by using an initial sparse matrix, quantifying an initial sparse representation result to obtain an optimized sparse representation result, and obtaining an optimized sparse matrix; constructing a product matrix by using the optimized sparse matrix and an initial measurement matrix, and adjusting absolute values of off-diagonal elements in the product matrix to be less than a correlation threshold; performing singular value decomposition on the product matrix to obtain a diagonal matrix and a left singular matrix, and updating the diagonal matrix according to a quantity of samplings of the initial measurement matrix; and optimizing the initial measurement matrix by using the left singular matrix and the updated diagonal matrix to obtain an optimized measurement matrix, and collecting image data by using the optimized sparse matrix and the optimized measurement matrix.

Abstract: A construction method and system for a visible near-infrared spectrum dictionary is provided. The method includes: constructing a four-primary color chromaticity cone by using normalized spectral response curves of four primary colors of a visible light camera as spectral basis functions; performing weighted combination on the spectral basis functions and forming an initial visible near-infrared spectrum dictionary; acquiring points on the four-primary color chromaticity cone on the basis of the initial visible red infrared spectrum dictionary according to different spectral resolutions and performing discretization, and forming words in the initial visible near-infrared spectrum dictionary; clustering chromaticity coordinates corresponding to the words into different groups, performing weighted combination on multi-scale spectral response curves corresponding to different group types, generating phrases or sentences in the dictionary, and generating a final visible near-infrared spectrum dictionary.

Abstract: Provided is an image processing method, which includes: determining whether the number of written rows of source image data in a DDR is a preset number of rows or a stop write number of rows, the preset number of rows being an integer multiple of the number of rows of an image block determined according to a compression condition, and the image block being any image block in an image block matrix determined on the basis of an entire frame of image; if yes, reading the source image data of the current image block from the DDR on the basis of a first read address of the current image block, the number of rows, and the number of columns; and performing compression on the source image data of the current image block to obtain compressed data, and writing the compressed data to the DDR.

Abstract: Methods and compression units for compressing a block of image data, the block of image data comprising a plurality of image element values, the image element values being divisible into at least a first value and a second value such that the block of image data comprises a two-dimensional block of first values, the method comprising: compressing a first data set comprising all or a portion of the two-dimensional block of first values in accordance with a first fixed-length compression algorithm to generate a first compressed block by: identifying common base information for the first data set; and identifying a fixed-length parameter for each first value in the first data set, the fixed-length parameter being zero, one or more than one bits in length; and forming a compressed block for the block of image data based on the first compressed block.

Abstract: The present disclosure provides an image processing method and apparatus, and an electronic device, and relates to the field of artificial intelligence (AI). The method includes: obtaining an input image and extracting region features of image regions in the input image to obtain a first image feature; processing pixels in the first image feature according to a predetermined rule, and determining a second image feature according to the processed pixels; and determining, based on the second image feature and at least one word vector that is determined for the input image, word vectors corresponding to the region features of the image regions in the first image feature at different moments, predicting positions of the word vectors in a text description, and forming the text description corresponding to the input image according to the word vectors and the positions.

Abstract: A counting device bears a visually distinct pattern or marking, and is coupled to a pusher or other movable system of a shelf in a manner that causes the counting device to rotate in response to changes in position of the pusher. When items are placed on a shelf, or removed from the shelf, causing the pusher to move, the pattern on the counting device rotates at an angle defined by an extent of the movement of the pusher. Images captured of the counting device at various times may be processed to determine orientations of the pattern at such times. Where a change in an orientation of a pattern is detected, a number of items added to or removed from a shelf may be calculated based on the change in orientation and an event involving the number of items may be determined to have occurred.

Abstract: A method includes identifying a plurality of images corresponding to sky conditions and isolating cloud pixels from sky pixels in each of the plurality of images. Responsive to determining percentage of cloud pixels in one or more of the plurality of images meets a threshold value, the method further includes determining predicted cloud movement relative to sun position. The method further includes causing a tint level of an electrochromic device to be controlled based on the predicted cloud movement relative to the sun position.

Abstract: At least one embodiment relates to a method for adding at least one 3D sample to a point cloud frame and for assigning a color-coding mode to said at least one 3D sample, said color-coding mode indicating if color information associated with said at least one 3D sample is explicitly encoded in a bitstream or if said color information is implicit.

Abstract: Disclosed is a compression system for compressing image data. The compression system receives an uncompressed image file with data points that are defined with absolute values for elements representing the data point position in a space. The compression system stores the absolute values defined for a first data point in a compressed image file, determines a difference between the absolute values of the first data point and the absolute values of a second data point, derives a relative value for the absolute values of the second data point from the difference, and stores the relative value in place of the absolute values of the second data point in the compressed image file.

Abstract: Methods and image processing systems are provided for determining a dominant gradient orientation for a target region within an image. A plurality of gradient samples are determined for the target region, wherein each of the gradient samples represents a variation in pixel values within the target region. The gradient samples are converted into double-angle gradient vectors, and the double-angle gradient vectors are combined so as to determine a dominant gradient orientation for the target region.

Abstract: Embodiments of the present disclosure provide a method of matching image, an apparatus of matching image, a device, and a computer-readable storage medium. The method includes: acquiring an image to be matched; determining a key point feature similarity between any image in an image library and the image to be matched, and determining a color feature similarity between the any image and the image to be matched; determining a fusion similarity between the any image and the image to be matched according to the key point feature similarity and the color feature similarity; and determining whether an image matching the image to be matched exists in the image library or not according to the fusion similarity between each of at least one image in the image library and the image to be matched.

Abstract: A system and method of identifying objects is provided. In one aspect, the system and method includes a hand-held device with a display, camera and processor. As the camera captures images and displays them on the display, the processor compares the information retrieved in connection with one image with information retrieved in connection with subsequent images. The processor uses the result of such comparison to determine the object that is likely to be of greatest interest to the user. The display simultaneously displays the images the images as they are captured, the location of the object in an image, and information retrieved for the object.

Abstract: A system and method for encoding a digital image may include obtaining image data of the digital image, wherein the image data comprises a plurality of image pixels, selecting a plurality of sample pixels from the plurality of image pixels, clustering the plurality of sample pixels based on an influence function to generate a plurality of hue clusters, classifying the image pixels to the plurality of the hue clusters, and assigning hue values of the hue clusters to the plurality of the image pixels.

Abstract: Techniques for generating a prescribed perspective of a scene are disclosed. In some embodiments, a specification of a scene comprising a plurality of objects is specified using object views that are substantially free of perspective distortions, and a prescribed perspective of the scene is at least in part generated by populating the prescribed perspective of the scene with pixels from existing images of the plurality of objects.

Abstract: A three-dimensional data encoding method includes: performing motion compensation on a plurality of encoded point clouds; merging the plurality of encoded point clouds that have been motion compensated to generate a reference point cloud; generating an N-ary tree structure of a current point cloud, where N is an integer greater than or equal to 2; and encoding the N-ary tree structure of the current point cloud using the reference point cloud.