Automatic construction of image inspection algorithm by using image processing network programming

Yuichiro Yoshimura; Kimiya Aoki

doi:10.1117/12.2266918

14 May 2017 Automatic construction of image inspection algorithm by using image processing network programming

Yuichiro Yoshimura, Kimiya Aoki

Proceedings Volume 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017; 103381B (2017) https://doi.org/10.1117/12.2266918
Event: The International Conference on Quality Control by Artificial Vision 2017, 2017, Tokyo, Japan

Abstract

In this paper, we discuss a method for automatic programming of inspection image processing. In the industrial field, automatic program generators or expert systems are expected to shorten a period required for developing a new appearance inspection system. So-called “image processing expert system” have been studied for over the nearly 30 years. We are convinced of the need to adopt a new idea. Recently, a novel type of evolutionary algorithms, called genetic network programming (GNP), has been proposed. In this study, we use GNP as a method to create an inspection image processing logic. GNP develops many directed graph structures, and shows excellent ability of formulating complex problems. We have converted this network program model to Image Processing Network Programming (IPNP). IPNP selects an appropriate image processing command based on some characteristics of input image data and processing log, and generates a visual inspection software with series of image processing commands. It is verified from experiments that the proposed method is able to create some inspection image processing programs. In the basic experiment with 200 test images, the success rate of detection of target region was 93.5%.

Citation Download Citation

Yuichiro Yoshimura and Kimiya Aoki "Automatic construction of image inspection algorithm by using image processing network programming", Proc. SPIE 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017, 103381B (14 May 2017); https://doi.org/10.1117/12.2266918

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