Abstract: A method of similarity-based deduplication comprising the steps of: receiving an input data block; computing discrete wavelet transform (DWT) coefficients; extracting feature-related DWT data from the computed DWT coefficients; applying quantization to the extracted feature-related DWT data to obtain keys as results of the quantization; constructing a locality-sensitive fingerprint of the input data block; computing a similarity degree between the locality-sensitive fingerprint of the input data block and a locality-sensitive fingerprint of each data block in the plurality of the data blocks in a cache memory; selecting an optimal reference data block as the data block; determining a differential compression is required to be applied based on the similarity degree between the input data block and the optimal reference data block; applying the differential compression to the input data block and the optimal reference data block.

Abstract: A method of similarity-based deduplication comprising the steps of: receiving an input data block; computing discrete wavelet transform (DWT) coefficients; extracting feature-related DWT data from the computed DWT coefficients; applying quantization to the extracted feature-related DWT data to obtain keys as results of the quantization; constructing a locality-sensitive fingerprint of the input data block; computing a similarity degree between the locality-sensitive fingerprint of the input data block and a locality-sensitive fingerprint of each data block in the plurality of the data blocks in a cache memory; selecting an optimal reference data block as the data block; determining a differential compression is required to be applied based on the similarity degree between the input data block and the optimal reference data block; applying the differential compression to the input data block and the optimal reference data block.

Abstract: An image processing system comprises an image processor having image processing circuitry and an associated memory. The image processor is configured to implement a foreground processing module utilizing the image processing circuitry and the memory. The foreground processing module is configured to obtain one or more images, to estimate a foreground region of interest from the one or more images, to determine a plurality of segments of the foreground region of interest, to calculate amplitude statistics for respective ones of the plurality of segments, to classify respective segments as being respective portions of static foreground objects or as being respective portions of dynamic foreground objects based at least in part on the calculated amplitude statistics and one or more defined patterns for known static and dynamic objects, and to remove one or more segments classified as static foreground objects from the foreground region of interest.

Abstract: A method and system for key frame based region of interest (ROI) tracking is disclosed. The method includes storing a key ROI set in a key ROI buffer, the key ROI set including at least one key ROI; designating one of the key ROI in the key ROI set as an active key ROI; receiving a point cloud representing a particular ROI to be processed for tracking; establishing a correspondence between that particular ROI and the active key ROI; determining whether to switch the active key designation to another key ROI in the key ROI set and switching the active key designation accordingly; and determining whether to modify the key ROI set and modifying the key ROI set accordingly.

Abstract: A method for estimating error rates in low-density parity check codes includes calibrating an encoder according to specific channel parameters and according to dominant error events in the low-density parity-check code. Dominant codewords are classified based on characteristics of each codeword that are likely to produce similar error rates at similar noise levels; codeword classes that produce the highest error rate are then tested. Error boundary distance is estimated using multiple binary searches on segments. Segments are defined based on codeword, trapping set and biasing noise components of the channel. To improve calculation speed the most significant subclasses of codewords, trapping sets and noise signals are used.

Abstract: An image processing system comprises an image processor having image processing circuitry and an associated memory. The image processor is configured to implement a gesture recognition system utilizing the image processing circuitry and the memory. The gesture recognition system implemented by the image processor comprises a static pose recognition module. The static pose recognition module is configured to identify a hand region of interest in at least one image, to determine a contour of the hand region of interest, to triangulate the determined contour, to flatten the triangulated contour, to compute one or more features of the flattened contour, and to recognize a static pose of the hand region of interest based at least in part on the one or more computed features.

Abstract: An image processing system comprises an image processor having image processing circuitry and an associated memory. The image processor is configured to implement a foreground processing module utilizing the image processing circuitry and the memory. The foreground processing module is configured to obtain one or more images, to estimate a foreground region of interest from the one or more images, to determine a plurality of segments of the foreground region of interest, to calculate amplitude statistics for respective ones of the plurality of segments, to classify respective segments as being respective portions of static foreground objects or as being respective portions of dynamic foreground objects based at least in part on the calculated amplitude statistics and one or more defined patterns for known static and dynamic objects, and to remove one or more segments classified as static foreground objects from the foreground region of interest.

Abstract: An image processing system comprises an image processor having image processing circuitry and an associated memory. The image processor is configured to implement a gesture recognition system comprising a contour classification module. The contour classification module is configured to identify one or more hand poses from one or more isolated regions in a first image, to determine a contour of a given one of the one or more hand poses, to calculate one or more features of the contour of the given hand pose, to identify one or more isolated regions in a second image, and to determine whether at least a portion of one or more isolated regions in the second image matches the given hand pose based on a comparison of one or more points characterizing the portion of the one or more isolated regions in the second image and the one or more features of the contour of the given hand pose.

Abstract: An image processing system comprises an image processor having image processing circuitry and an associated memory. The image processor is configured to implement a gesture recognition system utilizing the image processing circuitry and the memory. The gesture recognition system comprises a finger detection and tracking module configured to identify a hand region of interest in a given image, to extract a contour of the hand region of interest, to detect fingertip positions using the extracted contour, and to track movement of the fingertip positions over multiple images including the given image.

Abstract: An image processing system comprises an image processor having image processing circuitry and an associated memory. The image processor is configured to implement a gesture recognition system comprising a static pose recognition module. The static pose recognition module is configured to identify a hand region of interest in at least one image, to obtain a vocabulary of hand poses, to estimate a plurality of hand features based on the hand region of interest, the plurality of hand features comprising a first set of features estimated from the hand region of interest and a second set of features comprising at least one feature estimated using a transform on a contour of the hand region of interest, and to recognize a static pose of the hand region of interest based on the first set of features and the second set of features, wherein respective numbers of features in the first set of features and the second set of features are based at least in part on a size of the vocabulary of hand poses.

Abstract: The disclosure is directed to a system and method of image processing. According to various embodiments of the disclosure, a storage module in communication with a plurality of memory banks stores a plurality of pixels of an image in the memory banks and interleaves the memory banks to enable a plurality of image scanners to access the plurality of pixels. A scanning module scans a selection of pixels in at least four directions relative to a first pixel of the plurality of pixels utilizing the plurality of image scanners. A singular points detection module in communication with the scanning module acquires a depth of each pixel of the selection of scanned pixels and determines a singularity value of the first pixel by comparing the depth of the first pixel with the depth of each pixel of the selection of pixels.

Abstract: A method for ordering trapping sets to find one or more dominant trapping sets includes analyzing a trapping set and a random set of codewords to generate a distance value for each trapping set, and ordering the trapping sets by the distance value. Distance values may be determined for each trapping set by tracking a vote count wherein a correct decode at a certain noise level produces a “right” vote and an incorrect decode at a certain noise level produces a “left” vote. A certain threshold number of “left” votes terminates processing at that noise level.

Abstract: A method for estimating error rates in low-density parity check codes includes calibrating an encoder according to specific channel parameters and according to dominant error events in the low-density parity-check code. Dominant codewords are classified based on characteristics of each codeword that are likely to produce similar error rates at similar noise levels; codeword classes that produce the highest error rate are then tested. Error boundary distance is estimated using multiple binary searches on segments. Segments are defined based on codeword, trapping set and biasing noise components of the channel. To improve calculation speed the most significant subclasses of codewords, trapping sets and noise signals are used.

Abstract: A method and system for key frame based region of interest (ROI) tracking is disclosed. The method includes storing a key ROI set in a key ROI buffer, the key ROI set including at least one key ROI; designating one of the key ROI in the key ROI set as an active key ROI; receiving a point cloud representing a particular ROI to be processed for tracking; establishing a correspondence between that particular ROI and the active key ROI; determining whether to switch the active key designation to another key ROI in the key ROI set and switching the active key designation accordingly; and determining whether to modify the key ROI set and modifying the key ROI set accordingly.

Abstract: A method and system for registration of three-dimensional (3D) image frames is disclosed. The method includes receiving two point clouds representing two 3D image frames obtained at two time instances; locating the origins for the two point clouds; constructing two 2D grids for representing the two point clouds, wherein each 2D grid is constructed based on spherical representation of its corresponding point cloud and origin; identifying two sets of feature points based on the two 2D grids constructed; establishing a correspondence between the first set of feature points and the second set of feature points based on a neighborhood radius threshold; and determining an orthogonal transformation between the first 3D image frame and the second 3D image frame based on the correspondence between the first set of feature points and the second set of feature points.

Abstract: A method for ordering trapping sets to find one or more dominant trapping sets includes analyzing a trapping set and a random set of codewords to generate a distance value for each trapping set, and ordering the trapping sets by the distance value. Distance values may be determined for each trapping set by tracking a vote count wherein a correct decode at a certain noise level produces a “right” vote and an incorrect decode at a certain noise level produces a “left” vote. A certain threshold number of “left” votes terminates processing at that noise level.