Abstract: The invention relates to a method for the training of a classifier based on weakly labeled images and for the binary classification of an image. The training of the classifier comprises the steps of automatically and iteratively determining initial regions of interest for a training set and further on refining said regions of interest and adapting the classifier onto the refined regions of interest by a classifier refinement procedure. Further on, for a query image with unknown classification, an initial region of interest is determined and refined as to maximize the probability value derived at the output of said classifier. The query image is automatically assigned a negative classification label if said probability value is lower than or equal to a predetermined first threshold. The query image is automatically assigned a positive classification label if said probability value is greater than a predetermined second threshold.

Abstract: A method and system for the determination of new video segments is presented in which candidate sequences are recognized and stored, and analysis is performed on fingerprints of segments of the candidate sequences to isolate repeating video sequences, without prior knowledge of those repeating sequences. The repeating sequences are then added to a fingerprint library.

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: In general, in one aspect, the disclosure describes a method for specifying regions of interest for video event detection. The method includes receiving a video stream and identifying a region of interest in a video stream. The region of interest is a portion of at least one image of the video stream. The region of interest in the video stream is analyzed to detect a video event in the region of interest.

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 first computing device transmitting a wireless signal to a second and third computing devices, the signal requesting an actuator of the second computing device generate an acoustic signal to be received by a sensor of the third computing device, wherein the actuator and sensor are unsynchronized. The first computing device computes, based on an time estimate for the acoustic signal to travel from the actuator of the second computing device to the sensor of the third computing device, a physical location of the actuator of the second computing device and the sensor of the third computing device.

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: In general, in one aspect, the disclosure describes a method of determining a channel associated with a video stream. The method includes receiving a video stream and identifying a portion of the video stream containing information relevant to channel identification. The identified portion of the video stream is analyzed to determine the channel associated with the video stream.

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: 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: In general, in one aspect, the disclosure describes a method for ending advertisement insertion in 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. Advertisements are inserted into the video stream when a fingerprint for a known video entity indicative of a commercial break has at least a threshold level of similarity with the video stream. The inserting is ended when an end of the advertisement break is determined.

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 method, machine readable medium, and system are disclosed. In one embodiment the method comprises generating an acoustic signal from an actuator of a first computing device, receiving the acoustic signal with a sensor of a second computing device, receiving the acoustic signal with a sensor of a third computing device, generating an estimate of a difference between the amount of time required for the acoustic signal to travel from the actuator of the first computing device to the sensor of the second computing device and the amount of time required for the acoustic signal to travel from the actuator of the first computing device to the sensor of the third computing device, wherein the sensors and actuator are unsynchronized, and computing, based on the estimated difference in time, a physical location of at least one of the said sensors and actuator.

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 first computing device transmitting a wireless signal to a second and third computing devices, the signal requesting an actuator of the second computing device generate an acoustic signal to be received by a sensor of the third computing device, wherein the actuator and sensor are unsynchronized. The first computing device computes, based on an time estimate for the acoustic signal to travel from the actuator of the second computing device to the sensor of the third computing device, a physical location of the actuator of the second computing device and the sensor of the third computing device.

Abstract: A method and apparatus to detect transition effects are described. A method comprises deriving at least one frame-based video stream, each video stream forms a time series scaled to form a temporal time series pyramid. A fixed-size window slides over the time series. Each fixed-sized time series window is analyzed by a transition detector which determines the probability of a transition effect existing within the window. The time series of transition probabilities are rescaled to the original temporal scale of the video under analysis and integrated into a final transition detection results. Each transition detector is trained by a transition synthesizer to detect transition effects.

Abstract: A first computing device transmitting a wireless signal to a second and third computing devices, the signal requesting an actuator of the second computing device generate an acoustic signal to be received by a sensor of the third computing device, wherein the actuator and sensor are unsynchronized. The first computing device computes, based on an time estimate for the acoustic signal to travel from the actuator of the second computing device to the sensor of the third computing device, a physical location of the actuator of the second computing device and the sensor of the third computing device.