Apple Inc.

Primesense Ltd

Samsung Electronics Co., Ltd.

Applied Materials South East Asia Pte. Ltd.

Tomer Yanir

Eran Guendelman

Amiad Gurman

Shai Litvak

Michael Brand

Yaniv Shaked

Leonid Brailovsky

Yael Shor

Erez Sali

Noam Dotan

Mark Wagner

German Voronov

Ran Zaslavsky

Yuval Dorfan

A method for face detection includes capturing a depth map and an image of a scene and selecting one or more locations in the image to test for presence of human faces. At each selected location, a respective face detection window is defined, having a size that is scaled according to a depth coordinate of the location that is indicated by the depth map. Apart of the image that is contained within each face detection window is processed to determine whether the face detection window contains a human face. Similar methods may also be applied in identifying other object types.

Enhanced face detection using depth information

A method for image processing includes receiving a depth image of a scene containing a human subject and receiving a color image of the scene containing the human subject. A part of a body of the subject is identified in at least one of the images. A quality of both the depth image and the color image is evaluated, and responsively to the quality, one of the images is selected to be dominant in processing of the part of the body in the images. The identified part is localized in the dominant one of the images, while using supporting data from the other one of the images.

Combined color image and depth processing

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 humanoid form. The depth map is processed so as to identify three-dimensional (3D) connected components in the scene, each connected component including a set of the pixels that are mutually adjacent and have mutually-adjacent depth values. Separate, first and second connected components are identified as both belonging to the humanoid form, and a representation of the humanoid form is generated including both of the first and second connected components.

Analysis of three-dimensional scenes

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.

Learning-based estimation of hand and finger pose

Identifying components of a humanoid form in three-dimensional scenes

Tracking body parts by combined color image and depth processing

Analysis of three-dimensional scenes with a surface model

A method for processing data includes receiving a temporal sequence of depth maps of a scene containing a humanoid form having a head. The depth maps include a matrix of pixels having respective pixel depth values. A digital processor processes at least one of the depth maps so as to find a location of the head and estimates dimensions of the humanoid form based on the location. The processor tracks movements of the humanoid form over the sequence using the estimated dimensions.

Extraction of skeletons from 3D maps

A method for inspecting a wafer including a multiplicity of dies, the method including dividing an image of at least a portion of the wafer into a plurality of sub-images each representing a sub-portion of the wafer and selecting at least one defect candidate within each sub-image by comparing each sub-image to a corresponding sub-image of a reference including a representation, which is assumed to be faultless, of the portion of the wafer.

Growing community of inventors

Rinatya, Israel

Tomer Yanir