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

Computer imaginative and prescient suggestions was once very particular and tough to conform to assorted or maybe unexpected events. the present improvement is asking for easy to take advantage of but strong purposes that may be hired in quite a few occasions. This development calls for the reassessment of a few theoretical matters in machine imaginative and prescient. a greater common figuring out of imaginative and prescient techniques, new insights and higher theories are wanted. The papers chosen from the convention staged in Dagstuhl in 1996 to collect scientists from the West and the previous eastern-block international locations handle those targets and canopy such fields as second pictures (scale area, morphology, segmentation, neural networks, Hough rework, texture, pyramids), restoration of three-D constitution (shape from shading, optical movement, three-D item popularity) 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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Store for each v E D in the first coordinate of d[v] the index of the nearest minimum, and in the second coordinate the distance to this minimum. The range of the function d is R = (I U {WSHED}) x N. This leads to the implementation in Algorithm 1. In each minimum a wavefront is initiated, labelled with the index of the minimum it started in. If wavefront i reaches a node v after it has propagated over a distance l, and l is less then the value of the second coordinate of d[v] (denoted by snd(d[v])), the value l is placed in the second coordinate of d[v], while the first coordinate fst(d[v]) is set to i.

1: Regional structure (dashed) and PAG 35 Now we consider network layers (levels) 1 =2,3, .... Input elements are the sub-segments m = 0,1, ... , Nseg(l-I)-1 of level 1-1 with the features (l-I,m). Sub-segments can be adjacent if they belong to the same region Reg(l, kbk:z} and if they are '4-neighbours'. Generalizing (5), the adjacency of two segments ml and m2 now is defined by l(g)(I-I, m l ) - (g)(I-I, m2)1 ~ MAX {t l (I)O'[ (g)(I- I, m);m Here the standard deviation 0'[ (g)(I-I, m);m E E N(m l , m2)], t 2 (1)} (6) N(m l , m 2)] of the values (1-1,m) is given by 0'[ (g)(l- I, m);m E N(m l , m2)] 1 = 2:[0'(1- I, m l ) + 0'(1- 1, m2 )].

Figures 5c and 5d show the 100 and 500 biggest segments, respectively. One can hope, that these segments (after an elimination of very small ones) 39 can be used as texture elements in a subsequent segmentation of textures. 65, t2=7 c) The 100 biggest segments d) The 500 biggest segments 4 Conclusions A new graph network for image segmentation basing on a hierarchical construction of the Pixel Adjacency Graph was presented. The method gives encouraging results for various types of images but some problems must be solved in the future in order to enhance the quality of the results .

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