A novel methodology to represent the contents of a video sequence is presented. The representation is used to allow the user to rapidly view a video sequence in order to find a particular point within the sequence ad/or to decide whether the contents of the sequence are relevant to his or her needs. This system, referred to as content-based browsing, forms an abstraction to represent each shot of the sequence by using a representative frame, or an Rframe, and it includes management techniques to allow the user to easily navigate the Rframes. This methodology is superior to the current techniques of fast forward and rewind because rather than using every frame to view and judge the contents, only a few abstractions are used. Therefore, the need to retrieve the video from a storage system and to transmit every frame over the network in its entirety no longer exists, saving time, expenses, and bandwidth.

This paper presents a novel approach to processing encoded video sequences prior to decoding. Scene changes may be easily detected using DCT coefficients in JPEG and MPEG encoded video sequences. In addition, by analyzing the DCT coefficients, regions of interest may be isolated prior to decompression, increasing efficiency of any subsequent image processing steps, such as edge detection. The results are currently used in a video browser, and are part of an ongoing research project in creating large video databases. The procedure is presented in detail and several examples are exhibited.

The goal in computer vision systems is to analyze data collected from the environment and derive an interpretation to complete a specified task. Vision system tasks may be divided into data acquisition, low-level processing, representation, model construction, and matching subtasks. This paper presents a comprehensive survey of model-based vision systems using dense-range images. A comprehensive survey of the recent publications in each subtask pertaining to dense-range image object recognition is presented.

Large video databases, as well as many other applications involving video, such as multimedia, training, and "movie-on-demand" systems, require efficient steps to manipulate the enormous amount of data associated with full motion video. In this paper the incoming video is systematically and efficiently reduced via a frame selection procedure which takes advantage of the fact that the incoming video is encoded using one of several existing DCT-based standards. The procedure is performed in the frequency domain prior to video decoding. Further refinement in the frame selection step is achieved using a robust metric based upon the color histogram of the selected subset of decoded frames. The procedure is presented in detail and several examples are exhibited

Abstract. This paper presents a novel approach to processing encoded video sequences prior to complete decoding. Scene changes are easily detected using DCT coefficients in JPEG and MPEG encoded video sequences. In addition, by analyzing the DCT coefficients, regions of interest may be isolated prior to decompression, increasing the efficiency of any subsequent image processing steps, such as edge detection. The results are currently used in a video browser and are part of an ongoing research project in creating large video databases. The procedure is detailed with several examples presented and studied in depth.