Abstract: A system and method that distinguishes between logos and other categories of images, such as natural images, cartoon images, and computer-processed or generated images (“concept images”). The system receives a query image, which may, for example, be intended to be evaluated by an image search and comparison engine to identify matches to a catalog of images. The system evaluates characteristics of the query image, such as the gray-levels in the image, the edge crossings in the image, and the gradient magnitudes in the image, to identify whether the image is a logo. Based on identifying the image is a logo, the logo query image may be excluded from being evaluated by the image search and comparison engine.

Abstract: A system and method that distinguishes between logos and other categories of images, such as natural images, cartoon images, and computer-processed or generated images (“concept images”). The system receives a query image, which may, for example, be intended to be evaluated by an image search and comparison engine to identify matches to a catalog of images. The system evaluates characteristics of the query image, such as the gray-levels in the image, the edge crossings in the image, and the gradient magnitudes in the image, to identify whether the image is a logo. Based on identifying the image is a logo, the logo query image may be excluded from being evaluated by the image search and comparison engine.

Abstract: A method for processing data includes receiving a depth map of a scene containing a human hand, the depth map consisting of a matrix of pixels having respective pixel depth values. The method continues by extracting from the depth map information based on the depth values in a plurality of positions distributed over the human hand and processing the information in order to estimate respective candidate positions of the finger joints. The pose of the human hand is estimated by choosing a combination of the positions of the finger joints, responsively to anatomical constraints of the hand, that gives a hand configuration that is most anatomically probable among the candidate positions.

Abstract: High quality upscaling and denoising are required in mobile imaging devices that do not contain high quality lenses. Such is also required in order to scale up standard-definition video content for display in high-definition television screens. The disclosed method uses contextual information obtained during upscaling and/or denoising of frames. Relevant correspondences between patches within a frame and between frames, are detected, managed and exploited. The correspondence information is simultaneously used and updated while video frames are being processed. Two approaches may be used: 1. keeping, searching for and updating a database of useful patches, by adding frequently visible similar patches, aggregating high-frequency, low-noise information associated with the similar patches, and removing less-observed patches; 2.

Abstract: A method for processing data includes receiving a depth map of a scene containing a human hand, the depth map consisting of a matrix of pixels having respective pixel depth values. The method continues by extracting from the depth map information based on the depth values in a plurality of positions distributed over the human hand and processing the information in order to estimate respective candidate positions of the finger joints. The pose of the human hand is estimated by choosing a combination of the positions of the finger joints, responsively to anatomical constraints of the hand, that gives a hand configuration that is most anatomically probable among the candidate positions.

Abstract: A method for processing data includes receiving a depth map of a scene containing a human hand, the depth map consisting of a matrix of pixels having respective pixel depth values. The method continues by extracting from the depth map respective descriptors based on the depth values in a plurality of patches distributed in respective positions over the human hand, and matching the extracted descriptors to previously-stored descriptors in a database. A pose of the human hand is estimated based on stored information associated with the matched descriptors.

Abstract: High quality upscaling and denoising are required in mobile imaging devices that do not contain high quality lenses. Such is also required in order to scale up standard-definition video content for display in high-definition television screens. The disclosed method uses contextual information obtained during upscaling and/or denoising of frames. Relevant correspondences between patches within a frame and between frames, are detected, managed and exploited. The correspondence information is simultaneously used and updated while video frames are being processed. Two approaches may be used: 1. keeping, searching for and updating a database of useful patches, by adding frequently visible similar patches, aggregating high-frequency, low-noise information associated with the similar patches, and removing less-observed patches; 2.

Abstract: A method for processing data includes receiving a depth map of a scene containing a human hand, the depth map consisting of a matrix of pixels having respective pixel depth values. The method continues by extracting from the depth map respective descriptors based on the depth values in a plurality of patches distributed in respective positions over the human hand, and matching the extracted descriptors to previously-stored descriptors in a database. A pose of the human hand is estimated based on stored information associated with the matched descriptors.

Abstract: Methods and systems are provided for defining, identifying and learning geometric features.

Abstract: A method of operating an image sensor apparatus is provided. In this method, first digital image data is generated based on a first exposure data signal, wherein the first exposure data signal is indicative of an exposure level of the image sensor during a first exposure period. The first digital image data is stored in a storage circuit. Second digital image data is generated based on a second exposure data signal, wherein the second exposure data signal is indicative of an exposure level of the image sensor during a second exposure period. The second exposure period is different from the first exposure period. The second digital image data associated with the image sensor is selectively stored based on a value of the first digital image data associated with the image sensor.

Abstract: Methods and systems are provided for defining, identifying and learning geometric features.