A Dual-Population GA Based Visual Identification with Model Matching

Abstract:

Aiming at increasing speed of analysis and understanding of image and improving identification accuracy and efficiency, a novel model matching method with imaged object feature is presented, by which the grey value of image information collected from vision sensor is utilized directly and the finished image recognized result is achieved without pre-processing, so it saves the time for identification. In model matching process, a dual populations GA (DPGA) method is used for searching the best matched object, in which one is the global population served for detecting the possible area existing the optimal position, the other is the local population served for searching in the possible area for the optimal position carefully and quickly. The effectiveness of the method has been verified using real image of workpiece with rectangular feature.

• Periodical: Applied Mechanics and Materials (Volumes 121-126)
• Pages: 3708-3713
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.121-126.3708
• Online since: October 2011
• Authors: Yan Li, Feng Gao, Yu Mei Huang, Xin Gang Yang
• Keywords: Dual-Population GA, Model-Based Matching, Visual Identification

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