Apple Inc.

Samsung Electronics Co., Ltd.

Picscout (israel) Ltd.

Leonid Brailovsky

Shai Litvak

Tomer Yanir

German Voronov

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.

Logo detection system for automatic image search engines

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.

Learning based estimation of hand and finger pose

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. Using the high-resolution and noise-reduced information that was collected from earlier video frames, and is expressed by the output of latest processed frame, for upscaling and/or noise-reducing the next processed frame.

Image processor for and method of upscaling and denoising using contextual video information

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.

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Herzliya, Israel

Leonid Brailovsky