Abstract: A method and system for detection of video segments in compressed digital video streams is presented. The compressed digital video stream is examine to determine synchronization points, and the compressed video signal is analyzed following detection of the synchronization points to create video fingerprints that are subsequently compared against a library of stored fingerprints.

Abstract: A method and system for detection of video segments in compressed digital video streams is presented. The compressed digital video stream is examine to determine synchronization points, and the compressed video signal is analyzed following detection of the synchronization points to create video fingerprints that are subsequently compared against a library of stored fingerprints.

Abstract: A method and system for detection of video segments in compressed digital video streams is presented. The compressed digital video stream is examine to determine synchronization points, and the compressed video signal is analyzed following detection of the synchronization points to create video fingerprints that are subsequently compared against a library of stored fingerprints.

Abstract: A method and system for detection of video segments in compressed digital video streams is presented. The compressed digital video stream is examine to determine synchronization points, and the compressed video signal is analyzed following detection of the synchronization points to create video fingerprints that are subsequently compared against a library of stored fingerprints.

Abstract: A method and system for detection of video segments in compressed digital video streams is presented. The compressed digital video stream is examine to determine synchronization points, and the compressed video signal is analyzed following detection of the synchronization points to create video fingerprints that are subsequently compared against a library of stored fingerprints.

Abstract: A method and system for detecting automatic ad detection is presented in which video streams are modified such that features cannot be automatically recognized or statistical parameters and fingerprints derived from the video stream cannot be matched against fingerprints of known video segments in a database.

Abstract: A method and system for detection of video segments in compressed digital video streams is presented. The compressed digital video stream is examine to determine synchronization points, and the compressed video signal is analyzed following detection of the synchronization points to create video fingerprints that are subsequently compared against a library of stored fingerprints.

Abstract: A method and system for detection of video segments in compressed digital video streams is presented. The compressed digital video stream is examine to determine synchronization points, and the compressed video signal is analyzed following detection of the synchronization points to create video fingerprints that are subsequently compared against a library of stored fingerprints.

Abstract: A method and system for detection of video segments in compressed digital video streams is presented. The compressed digital video stream is examine to determine synchronization points, and the compressed video signal is analyzed following detection of the synchronization points to create video fingerprints that are subsequently compared against a library of stored fingerprints.

Abstract: A method and system for detection of video segments in compressed digital video streams is presented. The compressed digital video stream is examine to determine synchronization points, and the compressed video signal is analyzed following detection of the synchronization points to create video fingerprints that are subsequently compared against a library of stored fingerprints.

Abstract: A method and system for detecting automatic ad detection is presented in which video streams are modified such that features cannot be automatically recognized or statistical parameters and fingerprints derived from the video stream cannot be matched against fingerprints of known video segments in a database.

Abstract: A television signal substitution system that replaces known video segments such as advertisements with selected replacement advertisements. Fingerprint data of known advertisements can be stored in a fingerprint database. When new fingerprint data is available, the fingerprint data can be automatically or manually transmitted to subscribers. Various techniques can be used to identify advertisements based on the fingerprint data.

Abstract: In general, in one aspect, the disclosure describes a method for detecting a known video entity within a video stream. The method includes receiving a video stream and continually creating statistical parameterized representations for windows of the video stream. The statistical parameterized representation windows are continually compared to windows of a plurality of fingerprints. Each of the plurality of fingerprints includes associated statistical parameterized representations of a known video entity. A known video entity in the video stream is detected when a particular fingerprint of the plurality of fingerprints has at least a threshold level of similarity with the video stream.

Abstract: According to one embodiment of the present invention, a novel apparatus is disclosed. The apparatus includes an analog to digital converter to receive a predefined synchronization signal and a receiver clock; and an interpolation module coupled to the analog to digital converter to receive an output of the analog to digital converter and to continuously estimate and adapt current delay and skew estimates to synchronize a signal.

Abstract: A method and system for detecting automatic ad detection is presented in which video streams are modified such that features cannot be automatically recognized or statistical parameters and fingerprints derived from the video stream cannot be matched against fingerprints of known video segments in a database.

Abstract: According to one embodiment of the present invention, a novel method and system are disclosed. In one embodiment, a first node records a first node local time of receiving a wirelessly transmitted packet, the first node local time recorded with a monotonically increasing clock of the first node. The first node wirelessly transmits the recorded local time to at least a second node. The second node records a second node local time of receiving the wirelessly transmitted packet and records the first node local time of receiving the wirelessly transmitted packet. The second node updates a second node timing model to synchronize with the first node, the updating based on the second node local time of receiving the wirelessly transmitted packet and the first node local time of receiving the wirelessly transmitted packet.

Abstract: A system and method for adaptation of time synchronization of a plurality of multimedia streams is presented. In one embodiment, multimedia data is captured, via a plurality of multimedia platforms. A first synchronization signal is received by each of the plurality of multimedia platforms. The first synchronization signal is used to generate a first common clock reference among the plurality of multimedia platforms. Captured multimedia data is synchronized according to the first common clock reference. A second synchronization signal is received by each of the plurality of multimedia platforms. The second synchronization signal is used generate a transitional common clock reference among the plurality of multimedia platforms. The second synchronization signal is also used to generate a second common clock reference among the plurality of multimedia platforms.

Abstract: A tree classifier may include a number of stages. Some stages may include monolithic classifiers, and other stages may be split into two or more classifiers.

Abstract: According to one embodiment of the present invention, a novel method and system are disclosed. In one embodiment, an I/O device of a system receives or outputs a multimedia stream, the I/O device having a I/O clock and the system having a system clock. Samples from the multimedia stream are synchronized with the system clock.

Abstract: A procedure for fast training and evaluation of support vector machines (SVMs) with linear input features of high dimensionality is presented. The linear input features are derived from raw input data by means of a set of m linear functions defined on the k-dimensional raw input data. Training uses a one-time precomputation on the linear transform matrix in order to allow training on an equivalent training set with vector size k instead of m, given a great computational benefit in case of m>>k. A similar precomputation is used during evaluation of SVMs, so that the raw input data vector can be used instead of the derived linear feature vector.