Application of Genetic Programming to Edge Detector Design

Golonek, T.; Grzechca, Damian; Rutkowski, Jerzy

doi:10.1109/iscas.2006.1693675

Cited by 18 publications

(15 citation statements)

References 2 publications

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“…Evolutionary techniques have been successfully applied in many difficult tasks [11]- [20]. This group of soft computation consists of genetic algorithm (GA) [11]- [13], genetic programming [15]- [17], differential evolution [18], evolution strategy (ES), and evolutionary programming [21]. These methods imitate basic processes of natural evolutionary adaptation to environment conditions, such as reproduction "re" (selection), crossover "co" (recombination), and mutation "mu."…”

Section: Introductionmentioning

confidence: 99%

Genetic-Algorithm-Based Method for Optimal Analog Test Points Selection

Golonek

Rutkowski

2007

IEEE Trans. Circuits Syst. II

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110

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A new approach to an optimal analog test points selection is proposed. The described method uses ambiguity set concept and evolutionary computations to determine the optimal set of analog test points. After a brief introduction to analog testing and genetic algorithms, the proposed strategy is explained. The presented evolutionary approach is illustrated by a practical example of analog circuit and by a series of hypothetical circuits. The efficiency of the technique is compared with a method based on entropy index, and the obtained results are discussed.Index Terms-Analog circuits, analog system testing, design for testability (DfT), genetic algorithms (GAs).

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Section: Introductionmentioning

confidence: 99%

Genetic-Algorithm-Based Method for Optimal Analog Test Points Selection

Golonek

Rutkowski

2007

IEEE Trans. Circuits Syst. II

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110

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“…In [20], a 64-bit digital transfer function as an edge detector is evolved by GP based on artificial images. For detecting object boundaries, some image operators were employed to combine high-level detectors by GP [21].…”

Section: ) Regression In Edge Detectionmentioning

confidence: 99%

Soft edge maps from edge detectors evolved by genetic programming

Johnston

Zhang

2012

2012 IEEE Congress on Evolutionary Computation

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Genetic Programming (GP) has been used for edge detection, but there is no previous work that analyses the outputs from a GP detector before thresholding them to binary edge maps. When the threshold used in a GP system slightly changes, the final edge map from a detector may change a lot. Mapping the outputs of a GP detector to a grayscale space by a linear transformation is not effective. In order to address the problem of the sensitivity to the threshold values, we replace the linear transformation with an S-shaped transformation. We design two new fitness functions so that the outputs from an evolved detector can obtain better edge maps after mapping into a grayscale space. Experimental results show that the S-shaped transformation obtains soft edge maps similar to the fixed threshold and the new fitness functions improve the edge detection accuracy.

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“…Wang and Tan [19] used a linear GP system to find binary image edges, inspired by morphological operators, erosion and dilation, as terminals [18] for binary images. A 4 × 4 window has been employed to evolve digital transfer functions (combination of bit operators or gates) for edge detection by GP [8]. In another approach, not regression or classification, Bolis et al [2] simulated an artificial ant to search edges in images.…”

Section: Related Work To Gp For Edge Detectionmentioning

confidence: 99%

Genetic programming for edge detection using blocks to extract features

Johnston

Zhang

2012

Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation

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Single pixels can be directly used to construct low-level edge detectors but these detectors are not good for suppressing noise and some texture. In general, features based on a small area are used to suppress noise and texture. However, there is very little guidance in the literature on how to select the area size. In this paper, we employ Genetic Programming (GP) to evolve edge detectors via automatically searching for features based on flexible blocks rather than dividing a fixed window into small areas based on different directions. Experimental results for natural images show that using blocks to extract features obtains better performance than using single pixels only to construct detectors, and that GP can successfully choose the block size for extracting features.

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Application of Genetic Programming to Edge Detector Design

Cited by 18 publications

References 2 publications

Genetic-Algorithm-Based Method for Optimal Analog Test Points Selection

Genetic-Algorithm-Based Method for Optimal Analog Test Points Selection

Soft edge maps from edge detectors evolved by genetic programming

Genetic programming for edge detection using blocks to extract features

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