The US HDTV process has fostered substantial research and development activity over the last several years. The Advisory Committee on Advanced Television Service (ACATS) was formed to advise the FCC on the technology and systems suitable for delivery of high definition service over terrestrial broadcast channels. Four digital HDTV systems where tested at the Advanced Television Testing Center. All the systems gave excellent performance, but the results were inconclusive and a plan for a second round of tests was prepared. As each of the four systems where being readied for retest. The proponents of the four individual digital HDTV proposals worked together to define a single HDTV system which incorporated the best technology from the individual systems. The consortium of companies, called the Grand Alliance (GA), announced a combined system and submitted it to ACATS for consideration. After ACATS certification, the GA began construction of a prototype system to submit for laboratory testing at the end of 1994. This paper describes the video compression subsystem and the hardware prototype. The preprocessing, motion estimation, quantization, and rate control subsystems are described. The system uses bidirectional motion compensation, discrete cosine transform, quantization and Huffman coding. The resulting bitstream is input into a transport system which uses fixed length packets. The multiplex transport stream is input into the 8-VSB transmission system. Finally, the specifics of the hardware implementation are described and some simulation results are presented.>

This paper describes new algorithms for automatically constructing a computer aided design (CAD) model of a structured scene as imaged by a single camera on a mobile robot. The scene to be modeled is assumed to be composed mostly of linear edges with particular orientations in 3-D. This is the case for most indoor scenes as well as some outdoor urban scenes. The orientation data is used by a motion stereo algorithm to estimate the 3-D structure using a sequence of images. The algorithm assumes that the linear edges are the boundaries of opaque planar patches, such as the floor, the ceiling and the walls. The resulting 3-D description is a CAD model of the scene. Applications of this technique include CAD modeling for architecture and computer graphics, and robot navigation. This paper completes earlier publications and therefore concentrates on the latter parts of processing, including automatically tracking segments, and using the resulting models in different applications.>

New algorithms for detecting and interpreting linear features of a real scene as imaged by a single camera on a mobile robot are described. The low-level processing stages are specifically designed to increase the usefulness and the quality of the extracted features for indoor scene understanding. In order to derive 3-D information from a 2-D image, we consider only lines with particular orientation in 3-D. The detection and interpretation processes provide a 3-D orientation hypothesis for each 2-D segment. This in turn is used to estimate the robot's orientation and relative position in the environment. Next, the orientation data is used by a motion stereo algorithm to fully estimate the 3-D structure when a sequence of images becomes available. From detection to 3-D estimation, a strong emphasis is placed on real-world applications and very fast processing with conventional hardware. Results of experimentation with a mobile robot under realistic conditions are given and discussed.>

The authors describe algorithms for detecting and interpreting linear features of a real scene as images by a single camera on a mobile robot. The low-level processing stages were specifically designed to increase the usefulness and the quality of the extracted features for a semantic interpretation. The detection and interpretation processes provided a 3-D orientation hypothesis for each 2-D segment. This, in turn, was used to estimate the robot's orientation and relative position in the environment and to delimit the free space visible in the image. The orientation data was used by a motion stereo algorithm to fully estimate the 3-D structure when a sequence of images becomes available. From detection to 3-D estimation, an emphasis was placed on real-world applications and very fast processing with conventional hardware.>