A novel and efficient invertible transform for shape segmentation is defined that serves to localize and extract shape characteristics. This transform-the chordal axis transform (CAT}-remedies the deficiencies of the well-known medial axis transform (MAT). The CAT is applicable to shapes with discretized boundaries without restriction on the sparsity or regularity of the discretization. Using Delaunay triangulations of shape interiors, the CAT induces structural segmentation of shapes into limb and torso chain complexes of triangles. This enables the localization, extraction, and characterization of the morphological features of shapes. It also yields a pruning scheme for excising morphologically insignificant features and simplifying shape boundaries and descriptions. Furthermore, it enables the explicit characterization and exhaustive enumeration of primary, semantically salient, shape features. Finally, a process to characterize and represent a shape in terms of its morphological features is presented. This results in the migration of a shape from its affine description to an invariant, and semantically salient feature-based representation in the form of attributed planar graphs. The research described here is part of a larger effort aimed at automating image understanding and computer vision , 6
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Abst.ractThe process of gradually settling a combinatorial system into configurations of globally minimum energy has variously been called simulated annealing, statistical cooling, and so on. Very large combinatorial optimization problems have been solved using this technique. It has also been shown .that this method is effective in obtaining close-to-optimal solutions for problems known to be NP complete.The purpose of this paper is to illustrate an efficient version of the simulated annealing method as applied to a variant of the bin-packing problem. The computational complexity of the method is linear in input size similar to various wellknown heuristic methods for the problem. The solutions obtained, however, are much better than any of the heuristic methods. The particular variant of the bin-packing problem we consider has several practical applications such as static task allocation in process scheduling and batch processing.
This study describes a detailed study of the effect of finite precision computation on wavelet-based image compression. Specifically, we examine how the quality of the final decoded image is affected by various choices that a hardware designed will have to make, such as choice of wavelet (integer or real), fixed-point attributes (number of integer and fractional bits), and compression. The algorithm studied here is that adopted by the JPEG 2000 committee, and it uses trellis-coded quantization on the wavelet coefficients, followed by bit-plane coding.
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