Color Image Segmentation with a Hyper-Conic Multilayer Perceptron

  • Juan Pablo Serrano
  • Arturo Hernández
  • Rafael Herrera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8333)

Abstract

We apply the Hyper-Conic Artificial Multilayer Perceptron (HC-MLP) to color image segmentation, where we consider image segmentation as a classification problem distinguishing between foreground and background pixels. The HC-MLP was designed by using the conic space and conformal geometric algebra. The neurons in the hidden layer contain a transfer function that defines a quadratic surface (spheres, ellipsoids, paraboloids and hyperboloids) by means of inner and outer products, and the neurons in the output layer contain a transfer function that decides whether a point is inside or outside a sphere. The Particle Swarm Optimization algorithm (PSO) is used to train the HC-MLP. A benchmark of fifty images is used to evaluate the performance of the algorithm and compare our proposal against statistical methods which use copula gaussian functions.

Keywords

Color Image Segmentation Artificial Neural Networks Geometric Algebra 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Juan Pablo Serrano
    • 1
  • Arturo Hernández
    • 1
  • Rafael Herrera
    • 1
  1. 1.Computer Science DepartmentCenter for Research in MathematicsGuanajuatoMexico

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