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The Improved Canny Edge Detection Algorithm Based on an Anisotropic and Genetic Algorithm

  • Mingjie Wang
  • Jesse S. JinEmail author
  • Yifei Jing
  • Xianfeng Han
  • Lei Gao
  • Liping Xiao
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 634)

Abstract

Edge detection plays a crucial role in image processing. This paper proposes an improved Canny edge detection algorithm to deal with existing problems in traditional algorithms. Firstly, we use the anisotropic filter to denoise original grayscale images. This method can effectively suppress noise and preserve the edge feature. Secondly, the paper searches optimizing high and low thresholds used in Canny operator utilizing genetic algorithm based on the Otsu evaluative function to avoid human factors. In our experiment, we got the optimizing value (227, 84), and the interclass variance (3833) for image Lena. Compared with the traditional operator, this improved algorithm can reduce the false positive rate and improve the accuracy of detection. Meanwhile, the experiment shows that the algorithm is also robust in pedestrian detection.

Keywords

Canny Anisotropic filter Genetic algorithm OTSU Adaptation 

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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Mingjie Wang
    • 1
  • Jesse S. Jin
    • 1
    • 2
    Email author
  • Yifei Jing
    • 1
  • Xianfeng Han
    • 1
  • Lei Gao
    • 2
  • Liping Xiao
    • 2
  1. 1.School of Computer SoftwareTianjin UniversityTianjinChina
  2. 2.Beijing Aerospace Institute of Automatic ControlBeijingChina

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