Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method of determining correspondence between non-planar surfaces is disclosed. The method comprises calculating, for each surface, a spectral representation based on an eigenbasis of at least a portion of the surface; calculating a mapping matrix, based on the spectral representations; and determining the correspondence between the non-planar surfaces, based on the mapping matrix and the eigenbases.

Abstract: A method of reducing a dimensionality of a dataset is disclosed. The method comprises: calculating an interpolation matrix based on a Laplacian eigenbasis matrix of a sparse representation of the dataset; applying multidimensional scaling (MDS) to a transformation matrix of the interpolation matrix, thereby providing a reduced dataset; and storing the reduced dataset is a computer readable medium.

Abstract: A method of constructing a set of basis functions is disclosed. The method comprises: receiving a set of data vectors describing a physical object or a physical phenomenon; using a data processor for calculating a set of eigenvalues for an objective matrix defined as a sum of a first matrix corresponding to the set of data vectors and a second matrix corresponding to a Laplace-Beltrami operator, the objective matrix being a positive definite matrix; and constructing the set of basis functions based on at least a subset of the eigenvalues.