&lt;title&gt;GALE: a combined genetic algorithm-linear technique approach to edge detection&lt;/title&gt;

Donovan, Timothy P.; Passos, N.L.

doi:10.1117/12.316404

Cited by 1 publication

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“…These results were considered an improvement over the work presented in [8], where d 3 generations were required in order to obtain a first group of highly qualified points. However, the continuous running of the GA process, for example, up to n generations presents a strong convergence to the stronger points already detected, i.e., to the sharpest edges in the picture, almost eliminating the possibility of identifying weak edges.…”

Section: -188mentioning

confidence: 94%

“…By comparing the pixel value between the twc points in the chromosome, the method decides if there is chance for the existence of an edge traversing in between thosl points. Figure 2, initially presented in [8], shows a sketch of thi approach. There is no possibility of an edge between points f and B, while there is an edge between C and D and between I and F. However, by comparing the pixel values, the Sam1 conclusions can not be achieved since the comparison of E and 1 gives the same information obtained from A and B.…”

Section: The Gale Methodsmentioning

confidence: 99%

“…Edge detection is also a cornerstone in artistic works that are based on tools that produce striking outlined images. A combined application of genetic algorithms and a linear time method was used to identify edge information in a time efficient manner in the GALE method presented in [8]. In this paper, a refinement of the method is proposed by introducing fine tuning procedures that will allow a more complete result to be generated.…”

Section: Introductionmentioning

confidence: 97%

“…The Sobel number is the basis for evaluating the potential edge points in the GALE technique 1. 8,9]. In that method, genetic algorithms are used to locate pixels that have the best chance of identifying an edge, considering their Sobel number as the distinguishing factor.…”

Section: Introductionmentioning

confidence: 99%

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Fine tuning the GALE edge detection method

Donovan

Passos²

ISCAS'99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No.99CH36349)

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Most of the vision systems require the use of image processing applications that are highly dependent on the efficiency of edge detection techniques. These techniques are commonly implemented by applying an edge enhancement method followed by a thresholding point process. Most of these techniques are based on convolution algorithms that have a time complexity of O(n2) when the picture has size n x n. In order to reduce this time complexity, an improved solution depends on the reduction of the problem space. Such a reduction was recently achieved by a new method, named GALE, which combines the random search mechanisms of Genetic Algorithms with linear time methods. In this paper, a refinement of the GALE method is accomplished by introducing an iterative process that selectively eliminates from the population of the Genetic Algorithm those pixels that were previously identified as part of an edge. Experimental results show the improved performance of this method.

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Section: -188mentioning

confidence: 94%

Section: The Gale Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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