Abstract: Techniques for managing receiving, at a network receiver device, from a video streaming solution, segments for each of a plurality of video streams over one or more multicast channels, where the network receiver device is configured to cache the segments for consumption by one or more client devices. A first network congestion condition is satisfied at the network receiver device. In response to detecting the first network congestion condition is satisfied, a first one of the plurality of video streams having a lower priority is selected, relative to a second one of the plurality of video streams. Segments for the first video stream are requested using an alternate channel. Embodiments unsubscribe from a first one of the multicast channels for the selected first video stream.

Abstract: In one embodiment, a method includes receiving fast channel change (FCC) data for a program, the FCC data having a first set of content identifiers, receiving a video stream for the program, the video stream having a second set of content identifiers, changing at least one of the first set of content identifiers of the FCC data or the second set of content identifiers of the video stream to a common set of content identifiers, and providing the changed FCC data and video stream to a decoder.

Abstract: In one embodiment, a method comprises receiving a stream of transport packets encapsulating media packets, and generating a reference to the point-of-interest media packet relative to the location of the point-of-interest within the transport packet stream. The reference is generated for each media packet that is identified as a point-of-interest.

Abstract: In one embodiment, a method comprises receiving a stream of transport packets encapsulating media packets, and generating a reference to the point-of-interest media packet relative to the location of the point-of-interest within the transport packet stream. The reference is generated for each media packet that is identified as a point-of-interest.

Abstract: In one embodiment, a method comprises receiving a stream of transport packets encapsulating media packets, and generating a reference to the point-of-interest media packet relative to the location of the point-of-interest within the transport packet stream. The reference is generated for each media packet that is identified as a point-of-interest.

Abstract: In one embodiment, a method comprises receiving a stream of transport packets encapsulating media packets, and generating a reference to the point-of-interest media packet relative to the location of the point-of-interest within the transport packet stream.

Abstract: A method and apparatus subsamples, in a constraint satisfying manner, an image for subsequent processing of the subsampled image. The subsampling is based on constraints provided for the subsequent image processing. A subsampling factor or pair of factors are determined for the image and the image is subsampled by the subsampling factor(s). The constraints comprise at least time constraints, uncertainty constraints, accuracy constraints or implementation constraints. When time constraints are given, the constraints specify a function of the time needed for the subsequent image processing along with at least a maximum and a minimum time that the subsequent image processing should take. When uncertainty constraints are specified, the constraints give a function of the uncertainty in the subsequent image processing and at least a maximum and a minimum uncertainty required from the subsequent image processing.

Abstract: A method and apparatus subsamples, in a constraint satisfying manner, an image for subsequent processing of the subsampled image. The subsampling is based on constraints provided for the subsequent image processing. A subsampling factor or pair of factors are determined for the image and the image is subsampled by the subsampling factor(s). The constraints comprise at least time constraints, uncertainty constraints, accuracy constraints or implementation constraints. When time constraints are given, the constraints specify a function of the time needed for the subsequent image processing along with at least a maximum and a minimum time that the subsequent image processing should take. When uncertainty constraints are specified, the constraints give a function of the uncertainty in the subsequent image processing and at least a maximum and a minimum uncertainty required from the subsequent image processing.

Abstract: An automated object inspection system involving locating an object in an input image obtains a digital representation of an image including the object. Expected geometric parameters representing expected geometric features of the object are obtained. A set of pixels required for input to an object locating process is selected. A subsampling factor is defined based upon at least the expected geometric parameters. The subsampling factor defines a reduction in resolution of the set of pixels. The set of pixels is subsampled in accordance with the defined subsampling factor. The object locating process is run on the subsampled set of pixels. In defining the subsampling factor based upon at least the expected geometric parameters, the system takes into account constraints such as time an uncertainty constraints.

Abstract: Determining the position of a generally rectangular device is achieved by performing subsampling of image data, thresholding of the subsampled data, extraction of boundary features based on the output of the thresholding, calculation of correspondence between extracted boundary features and the generally rectangular device, and a final fit against a model of the generally rectangular device. Correspondence between the boundary features of the image and the generally rectangular device is performed by locating points in the extracted boundary feature which may correspond to corners of the generally rectangular device, removing those points which correspond to known objects other than the generally rectangular device, removing those points likely to belong to unknown objects, and fitting the points to an input model of the generally rectangular device to determine its position.

Abstract: The image pipeline for a vision system includes a series of digital image processing boards controlled by a computer. A video camera generates from a laser beam reflected from the target surface an RS-170 analog video signal which is converted to a digital signal for processing. Bins of digitizing parameters suitable for several target surface reflectivities are stored in computer memory. The appropriate bin is selected from histogram data of pixel intensity levels generated by one of the image processing boards using digitizing parameters from a reference bin. Improved imaging, especially of black surfaces, is achieved by scanning a laser beam to generate a light stripe over a number of image frames using the digitizing parameters from the selected bin and integrating the data from the successive frames using the image processing boards.