Bézier and Splines in Image Processing and Machine Vision by Sambhunath Biswas

By Sambhunath Biswas

This publication bargains with numerous photo processing and computer imaginative and prescient difficulties successfully with splines and comprises: the importance of Bernstein Polynomial in splines, specified assurance of Beta-splines functions that are really new, Splines in movement monitoring, a variety of deformative types and their makes use of. eventually the ebook covers wavelet splines that are effective and potent in numerous snapshot applications.

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The reconstructed image in each case is a faithful reproduction of its input version. The butterfly contour, having the larger number of GEs, incurs the higher percent of error in their regeneration. Furthermore, since the regeneration/reconstruction procedure uses the quadratic B´ezier approximation, the reconstruction is very fast. 8 Concluding Remarks Bernstein polynomial together with its properties and approximation capabilities provides a major step in the formulation of B´ezier spline model.

4, provide details of approximation, along with a new approach for the determination of polynomial order. In most of the cases, order is seen to be 2 but it can go up to 3 or 4 depending on variations in the segmented regions and the criterion, C. Fig. 2. Binary tree structure for hierarchical segmentation. 2 Approximation Problem For approximation, one can first formulate the problem using B´ezier-Bernstein polynomial and then can consider the issue of the polynomial order determination. Choose the B´ezier-Bernstein polynomial because the segmentation algorithm we are considering is for image compression, for which B´ezier-Bernstein polynomial provides a number of merits during reconstruction.

Therefore, to observe the deviation of the image quality, one can compute different objective measures. One such is to provide the error in area and shape compactness between the original and reconstructed images. Kulpa [96] provided a good way to compute the area and perimeter. Since the key pixels are always on the contour and the reconstructed arcs between them are restricted by the respective B´ezier characteristic triangles, the maximum error for an arc is the area of its pair of B´ezier characteristic triangles.

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