Joachim Weickert, Brahim Benhamouda (auth.), Prof. Dr. Franc's Advances in Computer Vision PDF

By Joachim Weickert, Brahim Benhamouda (auth.), Prof. Dr. Franc Solina, Prof. Dr. Walter G. Kropatsch, Prof. Dr. Reinhard Klette, Prof. Dr. Ruzena Bajcsy (eds.)

ISBN-10: 3211830227

ISBN-13: 9783211830222

ISBN-10: 3709168678

ISBN-13: 9783709168677

Computer imaginative and prescient ideas was very particular and tough to evolve to assorted or maybe unexpected occasions. the present improvement is looking for easy to take advantage of but strong purposes that may be hired in a variety of occasions. This pattern calls for the reassessment of a few theoretical matters in machine imaginative and prescient. a greater normal knowing of imaginative and prescient tactics, new insights and higher theories are wanted. The papers chosen from the convention staged in Dagstuhl in 1996 to assemble scientists from the West and the previous eastern-block international locations deal with those ambitions and canopy such fields as second photos (scale area, morphology, segmentation, neural networks, Hough remodel, texture, pyramids), restoration of 3D constitution (shape from shading, optical circulate, three-D item acceptance) and the way imaginative and prescient is built-in right into a greater task-driven framework (hand-eye calibration, navigation, perception-action cycle).

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However, the wrong classification occurs for smaller 46 Table 1: Sorted distance measures for Lena image corrupted by additive Gaussian noise with various standard deviations u. 00 Table 2: Sorted distance measures for Lena image corrupted by multiplicative uniform (speckle) noise for various standard deviations u. 9 I u (see Table 2). The similar very good performance has been obtained for uniform additive noise. Table 3: Sorted distance measures for Lena image corrupted by spiky noise for various probabilities p.

F(p')), for which the slope f(p) - f(p') is maximal is denoted by r(p). We call7r = (Po = P,PI,··· ,Pn = q) a path of steepest descent from p to q if, for each i = 0, ... , n - 1, PHI E r(pi). The topographical distance has the following property, on which the watershed definition crucially depends. Proposition 6 There exists a path 7r of steepest descent from p to q if and only if Tf(P, q) = f(p) - f(q)· In all other cases, Tf(P, q) > f(p) - f(q)· This proposition implies that lines of steepest descent are the geodesics (shortest paths) of the topographical distance function.

The algorithm assigns a label lab to each minimum and its associated basin by iteratively flooding the grid using a breadth first algorithm. Initially, all nodes with grey level h are given the label MASK. If some node v is adjacent to two or more different basins, it is marked a watershed node by the label WSHED. If the node can only be reached from nodes which have the same label the node is merged with the corresponding basin. Nodes which at the end still have the value MASK are new minima, and get a new label.

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Advances in Computer Vision by Joachim Weickert, Brahim Benhamouda (auth.), Prof. Dr. Franc Solina, Prof. Dr. Walter G. Kropatsch, Prof. Dr. Reinhard Klette, Prof. Dr. Ruzena Bajcsy (eds.)


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