David Crawford Gibbon

Automatic generation of pictorial transcripts of video programs

An automatic authoring system for the generation of pictorial transcripts of video programs which are accompanied by closed caption information is presented. A number of key frames, each of which represents the visual information in a segment of the video (i.e., a scene), are selected automatically by performing a content-based sampling of the video program. The textual information is recovered from the closed caption signal and is initially segmented based on its implied temporal relationship with the video segments. The text segmentation boundaries are then adjusted, based on lexical analysis and/or caption control information, to account for synchronization errors due to possible delays in the detection of scene boundaries or the transmission of the caption information. The closed caption text is further refined through linguistic processing for conversion to lower- case with correct capitalization. The key frames and the related text generate a compact multimedia presentation of the contents of the video program which lends itself to efficient storage and transmission. This compact representation can be viewed on a computer screen, or used to generate the input to a commercial text processing package to generate a printed version of the program.

Machine vision testbed

The Machine Vision Testbed (MVT) is an integrated hardware and software environment for prototyping machine vision algorithms. The system incorporates special purpose image processing hardware (Datacube MaxVideo20TM,) multiple high performance general purpose computers (SKYboltTM) and a Sun SPARCengine 1ETM. A software environment provides the Sun processor with the capability to thread and control tasks amongst various processing elements. To support video processing requirements, special purpose hardware has been designed which provides an image broadcast bus from the Maxvideo20 into multiple SKYbolt processors. The underlying programming model for the system is to use the special purpose image processing hardware to perform computationally intensive image processing tasks and to have the local (or neighborhood) type operations run on the general purpose processors. This partitioning provides a path to migrate large portions of an application, which would typically run on special purpose hardware, onto fast general purpose processors for the purposes of software reuse and portability. This paper is intended to discuss the motivation for choosing such a hybrid architecture and to discuss the details of how such a heterogeneous collection of components was integrated into a system capable of processing video data.