Automatic construction of tree-structural image transformations using genetic programming

We previously proposed an automatic construction method of image transformations. In this method, we approximate an unknown image transformation by a series of several known image filters, and a genetic algorithm optimizes their combination to meet the processing purpose presented by sets of original and target images. In this paper, we propose an extended method named "automatic construction of tree-structural image transformations (ACTIT)". In this new method, a tree whose interior nodes are image filters and leaf ones are input images approximates the transformation. The structures of the trees are optimized using genetic programming. ACTIT finds practical filter combinations that are too complicated to be designed by hand. It is applied to various kinds of image processing tasks. We show examples of its application and verify its validity.

[1]  Bangalore S. Manjunath,et al.  Genetic Programming for Object Detection , 1996 .

[2]  Takashi Matsuyama.  Expert systems for image processing: Knowledge-based composition of image analysis processes , 1989, Comput. Vis. Graph. Image Process..

[3]  Robert C. Vogt,et al.  Automatic Generation of Morphological Set Recognition Algorithms , 1989, Springer Series in Perception Engineering.

[4]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[5]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[6]  Walter Alden Tackett,et al.  Genetic Programming for Feature Discovery and Image Discrimination , 1993, ICGA.

[7]  Kazuyoshi Itoh,et al.  Generalization of shift invariant neural networks: Image processing of corneal endothelium , 1996, Neural Networks.

[8]  Tomoharu Nagao,et al.  Automatic construction of image transformation processes using genetic algorithm , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[9]  Hiromitsu Yamada,et al.  An automatic acquisition of hierarchical mathematical morphology procedures by GA , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).

[10]  Wei Zhang,et al.  Image processing of human corneal endothelium based on a learning network. , 1991, Applied optics.

[11]  R. Poli.  Genetic programming for image analysis , 1996 .

[12]  Jun-ichi Hasegawa,et al.  IMPRESS: A system for image processing procedure construction based on sample-figure presentation , 1989, Systems and Computers in Japan.

[13]  Bernard F. Buxton,et al.  Evolving edge detectors with genetic programming , 1996 .

[14]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.