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 and/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.

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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.

Given a 3D range image of a scene containing multiple arbitrarily shaped objects, the authors segment the scene into homogeneous surface patches. A novel modular framework for the segmentation task is proposed. In the first module, over-segmentation is achieved using zeroth and first order local surface properties. The segmentation is then refined in the second module using high order surface representations dictated by the high level vision tasks. The procedure has been applied successfully to many range images, five of which are presented.>

A model-based vision system is proposed in which a commercial CAD system has been used for object modeling. Assuming that the model is known, the corresponding object in the scene is located. Given the CAD model of an object, certain features of the model are extracted, while others are precalculated and stored. The given dense 3-D range image is segmented into a set of homogeneous surface patches using a segmentation procedure. Properties such as curvature, surface normal, and surface area are approximated for each surface patch. For each extracted surface patch, three filters are applied to the previously obtained model features to find the best match. Then, a global consistency filter is applied to remove ambiguities and to find the best matched model.>

The authors present a novel approach for segmentation of dense three-dimensional range images. In this approach, four local properties, namely the 3-D coordinate, the surface normal, the Gaussian curvature, and the mean curvature of each data point, are combined in a hierarchical data structure to segment a given 3-D dense range map into surface patches. This algorithm is applicable to planar as well as curved surfaces; several examples of segmentation of such surfaces are presented.>