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

By Sambhunath Biswas

This publication offers with numerous snapshot processing and computer imaginative and prescient difficulties successfully with splines and contains: the importance of Bernstein Polynomial in splines, certain insurance of Beta-splines functions that are fairly new, Splines in movement monitoring, numerous deformative versions and their makes use of. ultimately the ebook covers wavelet splines that are effective and powerful in numerous photo applications.

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There are many approaches [173, 65, 74] to achieve this goal. For example, it can be based on pixel level decision making such as iterative pixel modification, region growing, or adaptive region growing, or it can be based on multilevel thresholding. Each of these categories of algorithms, except multilevel thresholding, produces one region of similar graylevels at a time and, therefore, it forces local approximation for a region. Such methods may be called local thresholding schemes as a decision is made at the pixel level.

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. Also, for this constraint, shape compactness is a good measure for distortion in reconstructed images.

A cluster can be thought of as an image region. 1 Contour-based Segmentation Contours or edges are the line segments (curved or straight) that separate one region from the other. Therefore, an edge detection technique can be used for segmentation. One of the major drawbacks of this segmentation technique is that it does not provide any guarantee for connected edges. However, we can use an edge linking algorithm to connect fragmented edges. Since edge is a feature of an image, edge-based or contour-based segmentation can also be thought of as a feature-based segmentation.

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