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A novel approach to the automatic extraction of geological faults from three-dimensional (3-D) seismic data is described, and qualitative and quantitative comparisons of manually and automatically picked fault geometries interpreted from both high and medium quality 3-D seismic datasets are presented. An algorithm has been developed that allows semiautomated identification, extraction, and modeling of fault surfaces imaged in 3-D seismic datasets. Based on a multistage approach, the algorithm operates initially at a small spatial scale, identifying local discontinuities in the seismic horizons, and then gradually considers larger and larger segments of fault surfaces until a set of complete fault surfaces are identified. A large portion of the work involves merging of segments of fault surfaces, performed using a highest confidence first (HCF) stratagem, taking into consideration the context of the resultant fault geometry. We show that results from the automated fault picker compare favorably with a manually labeled set of faults surfaces interpreted from a high-quality dataset. Last, we present an estimate of the savings in human operator time that can be made by using the automated approach, indicating savings of multiple person-days for the multigigabyte datasets that typify the petroleum industry. [ABSTRACT FROM AUTHOR]

A new method for the compression of angiogram video sequences is presented. The method is based on the philosophy that diagnostically significant areas of the image should be allocated the greatest proportion of the total allocated bit budget. The approach uses a three-dimensional wavelet-coder based on the popular set partitioning in hierarchical trees algorithm. Incorporated into this framework are a region of interest (ROI) detection stage and a texture.modeling stage. The combined result is an approach that models the high-frequency wavelet coefficients for some diagnostically unimportant regions of the image in an extremely efficient manner. This allows additional bits to be used within the ROI to improve the quality of the diagnostically significant areas. Results are compared for a number of real data sets and evaluated by trained cardiologists. [ABSTRACT FROM AUTHOR]

This paper presents an approach to the problem of estimating a dense optical flow field. The approach is based on a multiframe, irregularly spaced motion trajectory set, where each trajectory describes the motion of a given point as a function of time. From this motion trajectory set a dense flow field is estimated using a process of interpolation. A set of localized motion models are estimated, with each pixel labeled as belonging to one of the motion models. A Markov random field framework is adopted, allowing the incorporation of contextual constraints to encourage region-like structures. The approach is compared with a number of conventional optical flow estimation algorithms taken over a number of real and synthetic sequences. Results indicate that the method produces more accurate results for sequences with known ground truth flow. Also, applying the method to real sequences with unknown flow results in lower DFD, for all of the sequences tested. [ABSTRACT FROM AUTHOR]

Presents a study which described a region merging technique based on a robust statistical test in range image segmentation. Categories of segmentation algorithms; Results; Conclusions.