Abstract: Systems, devices, and methods disclosed herein may generate captured views and a plurality of intermediate views within a pixel disparity range, Td the plurality of intermediate views being extrapolated from the captured views.

Abstract: A method for verifying a consistency of snapshot metadata maintained in an ordered data structure for a plurality of snapshots in a snapshot hierarchy is provided. The method includes identifying a first plurality of nodes maintained in a first ordered data structure for a first snapshot that is a child of a second snapshot; for a first node of the first plurality of nodes, verifying the first node by checking for the first node in a second node map maintained in memory for the second snapshot, wherein the second node map includes a plurality of verified nodes in a second ordered data structure; and based on whether the first node is in the second node map: adding the first node to a first node map maintained in memory for the first snapshot, wherein the first node map includes verified nodes of the first plurality of nodes; or triggering an alarm.

Abstract: Systems, devices, and methods disclosed herein may generate captured views and a plurality of intermediate views within a pixel disparity range, Td, the plurality of intermediate views being extrapolated from the captured views.

Abstract: Systems, devices, and methods disclosed herein may generate captured views and a plurality of intermediate views within a pixel disparity range, Td, the plurality of intermediate views being extrapolated from the captured views.

Abstract: A transaction recording method associated with a page-oriented non-volatile memory is disclosed. The method includes identifying page requiring one or more update for implementing a defined transaction. Further, the method includes replicating the page in a non-volatile buffer. The method further includes updating the identified page with the transaction contents. The transaction is committed if the page is updated without interruption else the entire transaction is rolled-back.

Abstract: Systems, devices, and methods disclosed herein may generate captured views and a plurality of intermediate views within a pixel disparity range, Td, the plurality of intermediate views being extrapolated from the captured views.

Abstract: A transaction recording method associated with a page-oriented non-volatile memory is disclosed. The method includes identifying page requiring one or more update for implementing a defined transaction. Further, the method includes replicating the page in a non-volatile buffer. The method further includes updating the identified page with the transaction contents. The transaction is committed if the page is updated without interruption else the entire transaction is rolled-back.

Abstract: Systems, devices, and methods disclosed herein may generate captured views and a plurality of intermediate views within a pixel disparity range, Td, the plurality of intermediate views being extrapolated from the captured views.

Abstract: Systems, devices, and methods disclosed herein may generate captured views and a plurality of intermediate views within a pixel disparity range, Td, the plurality of intermediate views being extrapolated from the captured views.

Abstract: Systems, devices, and methods disclosed herein may generate captured views and a plurality of intermediate views within a pixel disparity range, Td, the plurality of intermediate views being extrapolated from the captured views.

Abstract: A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. The video sequence is segmented into scenes by identifying scene boundaries based on the determined video frame clusters.

Abstract: Systems, devices, and methods disclosed herein may apply a computational spatial-temporal analysis to assess pixels between temporal and/or perspective view imagery to determine imaging details that may be used to generate image data with increased signal-to-noise ratio.

Abstract: A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. The video sequence is segmented into scenes by identifying scene boundaries based on the determined video frame clusters.

Abstract: A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. The video sequence is segmented into scenes by identifying scene boundaries based on the determined video frame clusters.

Abstract: A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. A summary is formed based on the determined video frame clusters.

Abstract: Systems, devices, and methods disclosed herein may apply a computational spatial-temporal analysis to assess pixels between temporal and/or perspective view imagery to determine imaging details that may be used to generate image data with increased signal-to-noise ratio.

Abstract: A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. The video sequence is segmented into scenes by identifying scene boundaries based on the determined video frame clusters.

Abstract: A method for identifying a set of key video frames from a video sequence comprising extracting feature vectors for each video frame and applying a group sparsity algorithm to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames. Weighting coefficients associated with the group sparse combination are analyzed to determine video frame clusters of temporally-contiguous, similar video frames. The video sequence is segmented into scenes by identifying scene boundaries based on the determined video frame clusters.

Abstract: A method for representing a video sequence including a time sequence of input video frames, the input video frames including some common scene content that is common to all of the input video frames and some dynamic scene content that changes between at least some of the input video frames. Affine transform are determined to align the common scene content in the input video frames. A common video frame including the common scene content is determined by forming a sparse combination of a first basis functions. A dynamic video frame is determined for each input video frame by forming a sparse combination of a second basis functions, wherein the dynamic video frames can be combined with the respective affine transforms and the common video frame to provide reconstructed video frames.

Abstract: A method for determining a scene boundary location dividing a first scene and a second scene in an input video sequence. The scene boundary location is determined responsive to a merit function value, which is a function of the candidate scene boundary location. The merit function value for a particular candidate scene boundary location is determined by representing the dynamic scene content for the input video frames before and after candidate scene boundary using sparse combinations of a set of basis functions, wherein the sparse combinations of the basis functions are determined by finding a sparse vector of weighting coefficients for each of the basis functions. The weighting coefficients determined for each of the input video frames are combined to determine the merit function value. The candidate scene boundary providing the smallest merit function value is designated to be the scene boundary location.