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

By Sambhunath Biswas

Digital photograph processing and laptop imaginative and prescient have grown significantly over the past few many years. Of a few of the suggestions, constructed up to now splines play a good and important position in lots of of them. powerful mathematical concept and simplicity of implementations is likely one of the keys in their good fortune in lots of study concerns.

This booklet bargains with quite a few photo processing and laptop imaginative and prescient difficulties successfully with splines and includes:

• the importance of Bernstein Polynomial in splines

• effectiveness of Hilbert test for electronic images

• distinct assurance of Beta-splines, that are particularly new, for attainable destiny functions

• discrete smoothing splines and their energy in program

• snakes and lively contour versions and their uses

• the importance of worldwide optimum contours and surfaces

Finally the ebook covers wavelet splines that are effective and potent in numerous photograph purposes.

Dr Biswas is a method analyst on the Indian Statistical Institute, Calcutta the place he teaches laptop imaginative and prescient in M Tech (Computer Science). His study pursuits comprise snapshot processing, computing device imaginative and prescient, special effects, trend attractiveness, neural networks and wavelet image-data analysis.

Professor Lovell is a study chief in nationwide ICT Australia and learn Director of the clever Real-Time Imaging and Sensing examine crew on the collage of Queensland. His examine pursuits are presently focussed on optimum picture segmentation, real-time video research and face popularity.

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Bézier and Splines in Image Processing and Machine Vision

Electronic photo processing and desktop imaginative and prescient have grown significantly over the past few a long time. Of a few of the thoughts, built to this point splines play a favorable and important position in lots of of them. powerful mathematical idea and simplicity of implementations is likely one of the keys in their luck in lots of study matters.

Extra resources for Bézier and Splines in Image Processing and Machine Vision

Example text

For this, segment an image corresponding to a threshold and if that segmentation does not fulfill certain objectives, then re-segment the segmented image through an iterative computation of a new threshold. So, segmentation and re-segmentation go on continuously until the criterion is satisfied for a definite task. Region Growing, Merge, and Split Region growing normally starts from a small region, and merges small nearby regions to grow in size. If the merge is successful, neighborhood regions are further merged depending on a condition for successful merge.

This is a very simple, yet effective, measure of interaction. Small regions detected by MI are the potential candidates for merge and they are merged if the magnitude of the average gradient computed over their region boundaries is less than a preassigned positive value. This criterion will avoid merging small but informative regions. , the white spot in the eye ball in a face image. 27) where p is the perimeter of the region Ω1 and G(i, j) is the gradient at the position (i, j). The average gradient over other regions can likewise be computed.

Also, let h be the value of the intercept and (X , Y ) be the coordinate of the end point of the intercept. Y P4 P3 II P2 (x2 ,y2 ) I (x, y ) h X X P1 (x1,y1) IV III Y / Fig. 11. Detection of B´ezier characteristic triangles for Method 2. Since an arc may lie either in the left (clockwise) or in the right (counterclockwise) side of the line joining (x1 , y1 ) and (x2 , y2 ), X and Y may have the values Y = y1 + h or, X = x1 X = x1 + h Y = y1 corresponding to the two possible senses of the arc in quadrant I where x2 > x1 and y2 > y1 .

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