A novel genetic programming algorithm for designing morphological image analysis method

Authors:
Jun Wang;Ying Tan
Affiliations:
Key Laboratory of Machine Perception (Ministry of Education), Peking University, Department of Machine Intelligence, School of EECS, Peking University, P.R. China;Key Laboratory of Machine Perception (Ministry of Education), Peking University, Department of Machine Intelligence, School of EECS, Peking University, P.R. China
Venue:
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Year:
2011

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Abstract

In this paper, we propose an applicable genetic programming approach to solve the problems of binary image analysis and gray scale image enhancement. Given a section of original image and the corresponding goal image, the proposed algorithm evolves for generations and produces a mathematic morphological operation sequence, and the result performed by which is close to the goal. When the operation sequence is applied to the whole image, the objective of image analysis is achieved. In this sequence, only basic morphological operations-- erosion and dilation, and logical operations are used. The well-defined chromosome structure leads brings about more complex morphological operations can be composed in a short sequence. Because of a reasonable evolution strategy, the evolution effectiveness of this algorithm is guaranteed. Tested by the binary image features analysis, this algorithm runs faster and is more accurate and intelligible than previous works. In addition, when this algorithm is applied to infrared finger vein gray scale images to enhance the region of interest, more accurate features are extracted and the accuracy of discrimination is promoted.