Abstract: A wide spread adoption of 3D videos and technologies is hindered by the lack of high-quality 3D content. One promising solution to address this problem is to use automated 2D-to-3D conversion. However, current conversion methods, while general, produce low-quality results with artefacts that are not acceptable to many viewers. Creating a database of 3D stereoscopic videos with accurate depth is, however, very difficult. Computer generated content can be used to generate high-quality 3D video reference database for 2D-to-3D conversion. The method transfers depth information from frames in the 3D reference database to the target frame while respecting object boundaries. It computes depth maps from the depth gradients, and outputs a stereoscopic video.

Abstract: A wide spread adoption of 3D videos and technologies is hindered by the lack of high-quality 3D content. One promising solution to address this problem is to use automated 2D-to-3D conversion. However, current conversion methods, while general, produce low-quality results with artefacts that are not acceptable to many viewers. Creating a database of 3D stereoscopic videos with accurate depth is, however, very difficult. Computer generated content can be used to generate high-quality 3D video reference database for 2D-to-3D conversion. The method transfers depth information from frames in the 3D reference database to the target frame while respecting object boundaries. It computes depth maps from the depth gradients, and outputs a stereoscopic video.

Abstract: A wide spread adoption of 3D videos and technologies is hindered by the lack of high-quality 3D content. One promising solution to address this problem is to use automated 2D-to-3D conversion. However, current conversion methods, while general, produce low-quality results with artefacts that are not acceptable to many viewers. Creating a database of 3D stereoscopic videos with accurate depth is, however, very difficult. Computer generated content can be used to generate high-quality 3D video reference database for 2D-to-3D conversion. The method transfers depth information from frames in the 3D reference database to the target frame while respecting object boundaries. It computes depth maps from the depth gradients, and outputs a stereoscopic video.

Abstract: The method for dynamic video magnification magnifies small motions occurring simultaneously within large motions. The method involves selecting a region of interest from a video for magnification. The region of interest is warped to obtain a stabilized sequence of frames that discounts large motions. Each frame of the stabilized sequence is decomposed to a foreground layer, a background layer, and an alpha matte layer, and each of the foreground and alpha matte layers is magnified. Then a magnified sequence is generated from the magnified layers using matte inversion. Any image holes in the magnified sequence are filled in using texture synthesis. Finally, the magnified sequence is de-warped to the original space-time coordinates.

Abstract: A control system includes first and second hardware components and a server which is remote from the hardware components. The server is a server in the cloud which is connected via the Internet to the hardware components. A control module is implemented as a service running on the server and the control module is operable to communicate with the hardware components to control at least one of the hardware components.

Abstract: The method for dynamic video magnification magnifies small motions occurring simultaneously within large motions. The method involves selecting a region of interest from a video for magnification. The region of interest is warped to obtain a stabilized sequence of frames that discounts large motions. Each frame of the stabilized sequence is decomposed to a foreground layer, a background layer, and an alpha matte layer, and each of the foreground and alpha matte layers is magnified. Then a magnified sequence is generated from the magnified layers using matte inversion. Any image holes in the magnified sequence are filled in using texture synthesis. Finally, the magnified sequence is de-warped to the original space-time coordinates.

Abstract: A wide spread adoption of 3D videos and technologies is hindered by the lack of high-quality 3D content. One promising solution to address this problem is to use automated 2D-to-3D conversion. However, current conversion methods, while general, produce low-quality results with artefacts that are not acceptable to many viewers. Creating a database of 3D stereoscopic videos with accurate depth is, however, very difficult. Computer generated content can be used to generate high-quality 3D video reference database for 2D-to-3D conversion. The method transfers depth information from frames in the 3D reference database to the target frame while respecting object boundaries. It computes depth maps from the depth gradients, and outputs a stereoscopic video.

Abstract: A control system comprises first and second hardware components and a server which is remote from the hardware components. The server is a server in the cloud which is connected via the Internet to the hardware components. A control module is implemented as a service running on the server and the control module is operable to communicate with the hardware components to control at least one of the hardware components.

Abstract: A method for detecting whether query video data appears in a reference video, comprising determining a measure of depth from a portion of the query video data, comparing the measure against a measure of depth for the reference video to perform a depth match and, if a match is determined; comparing a visual signature derived from the query video data against a visual signature of the reference video to perform a visual match to determine a measure representing the likelihood that the query video data derives from the reference video.

Abstract: A computer-implemented method comprises of processing data for a source video to determine representative topics of the video, determining multiple topics of interest for respective ones of multiple market segments, using the representative topics and the multiple topics to determine a target audience for the video from the market segments, using the topics of interest for the target audience to determine a set of keywords for an advertisement, using the set of keywords to provide multiple candidate advertisements for the target audience, generating a ranked list from the multiple candidate advertisements, and selecting an advertisement for the source video from the ranked list of candidate advertisements.

Abstract: A method for detecting whether query video data appears in a reference video, comprising determining a measure of depth from a portion of the query video data, comparing the measure against a measure of depth for the reference video to perform a depth match and, if a match is determined; comparing a visual signature derived from the query video data against a visual signature of the reference video to perform a visual match to determine a measure representing the likelihood that the query video data derives from the reference video.