Samsung Electronics Co., Ltd.

Technion Research & Development Foundation Ltd

Yonathan Aflalo

Nathan Henri Levy

Ron Kimmel

Matan Sela

Ariel Orfaig

Sagi Tzur

Ayal Keisar

In various embodiments of an image processing method and apparatus, first and second point clouds representing respective images of a scene/object from different viewpoints are obtained. Extracted features points from the first point cloud are matched with extracted feature points from the second point cloud, using depth based weighting, as part of an ICP initiation process. The first and second point clouds are then further ICP processed using results of the initiation process to generate at least one coordinate-transformed point cloud.

Image processing with iterative closest point (ICP) technique

A first image and a second image of an object taken with different viewing directions are received. The first image and the second image are downscaled in a ratio of a downscale factor DF to generate a first downscaled image and a second downscaled image, respectively. An edge map is generated by detecting an edge pixel from the first downscaled image. An initial cost volume matrix is generated from the first downscaled image and the second downscaled image according to the edge map. An initial disparity estimate is generated from the initial cost volume matrix. The initial disparity estimate is refined using the initial disparity estimate to generate a final disparity set. A depth map is generated from the first image and the second image using the final disparity set.

Stereo matching system and method of operating thereof

A method of detecting a bad pixel in an image sensor includes: determining, a first color of a center pixel of a Bayer patch output by the image sensor; extracting, a main patch, a first auxiliary patch, and a second auxiliary patch from the Bayer patch, having the first color, a second other color, and a third other color, respectively; generating a normalized patch of the first color from the main patch and the auxiliary patches that brings a level of the auxiliary patches to a level of the main patch; and detecting whether the center pixel of the Bayer patch is a bad pixel using the normalized patch.

Method and system for bad pixel correction in image sensors

A method that uses at least one hardware processor for receiving a three-dimensional model of an object, receiving an audio sequence embodied as a digital file that comprises a musical composition, generating a video frame sequence, and synthesizing the audio sequence and the video frame sequence into an audiovisual clip. The three-dimensional model is embodied as a digital file that comprises a representation of the object. The generating step comprises computing a caricature of the object by applying a computerized caricaturization algorithm to the three-dimensional model. The computing has scaling gradient fields of surface coordinates of the three-dimensional model by a function of a Gaussian curvature of the surface, and finding a regular surface whose gradient fields fit the scaled gradient fields. The computing is with a different exaggeration factor for each of multiple ones of the video frames, and the different exaggeration factor is based on one or more parameters of the musical composition of the audio sequence.

Audio-based caricature exaggeration

Computerized, audio-based caricature exaggeration which includes: receiving a three-dimensional model of an object; receiving an audio sequence; generating a video frame sequence, said generating comprising computing a caricature of the object, wherein (a) the computing is with a different exaggeration factor for each of multiple ones of the video frames, and (b) the different exaggeration factor is based on one or more parameters of the audio sequence; and synthesizing the audio sequence and the video frame sequence into an audiovisual clip.

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Tel Aviv, Israel

Yonathan Aflalo