Abstract-We present a method for nonrigid motion analysis in time sequences of volume images (4D data). In this method, nonrigid motion of the deforming object contour is dynamically approximated by a physically-based deformable surface. In order to reduce the number of parameters describing the deformation, we make use of a modal analysis which provides a spatial smoothing of the surface. The deformation spectrum, which outlines the main excited modes, can be efficiently used for deformation comparison. Fourier analysis on time signals of the main deformation spectrum components provides a ternporal smoothing of the data. Thus a complex nonrigid deformation is described by only a few parameters: the main excited modes and the main Fourier harmonics. Therefore, 4D data can be analyzed in a very concise manner. The power and robustness of the approach is illustrated by various results on medical data. We believe that our method has important applications in automatic diagnosis of heart diseases and in motion compression.

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W e present a sound framework for relevance feedback in content-based image retrieval. The modeling is based on non-parametric density estimation of relevant and non-relevant items and Bayesian inference. This theory has been successfully applied to benchmark image databases, quantitatively demonstrating its performance for target search, selective control of precision and recall an category search, and improvement of retrieval eaectiveness. The paper is illustrated with several experiments and retrieval results on real-world data.

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