The Human Body Edge Detection of Infrared Image with IGA_CNN Algorithm

Authors

  • Gangyi Hu School of Information Technology, Shinawatra University 197 BBD Building Viphavadi-Rangsit Road, Samsen Nai, Phayathai, Bangkok 10400, Thailand
  • Sumeth Yuenyong School of Information Technology, Shinawatra University 197 BBD Building Viphavadi-Rangsit Road, Samsen Nai, Phayathai, Bangkok 10400, Thailand

Keywords:

human body edge detection, infrared image, Cellular Neural Network, improved genetic algorithm

Abstract

This paper presents an algorithm with improved genetic algorithm design template in cellular neural networks parameters and realizes the human body edge detection in infrared image. The algorithm uses population Cross generational elitist selection and the subgroups of parallel population that overcome the shortcomings of the simple genetic algorithm in solving for the optimal template in cellular neural networks, which is premature convergence. The improved genetic algorithm can converge quickly to the stable and optimal value. The simulation results show that this algorithm is more effective than traditional algorithms based on Particle Swarm Optimization and on a simple genetic algorithm. At the same time, compared with the traditional Canny algorithm, this algorithm can detect the human body more clearly and accurately in infrared images, with low miss-detection. This greatly improves the processing speed of the subsequent target tracking and provides a new method for body edge detection in an infrared image.

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Published

2016-03-17

How to Cite

Hu, G., & Yuenyong, S. (2016). The Human Body Edge Detection of Infrared Image with IGA_CNN Algorithm. Science & Technology Asia, 21(1), 46–58. Retrieved from https://ph02.tci-thaijo.org/index.php/SciTechAsia/article/view/52121

Issue

Section

Engineering