Improved SURF Algorithm Based On ACO

SURF (Speeded-Up Robust Features) is a scale- and rotation-invariant algorithm, which has a better repeatability, distinctiveness, robustness, and a faster computing and comparing speed. In this paper, we propose an improved SURF algorithm based on ACO (Ant Colony Optimization). First of all, the algorithm uses SURF to find all interest points. Secondly, each pixel of the original image is seen as an ant, imitates the process of ants search food to get the image edge. Finally, selects the interest points from the area around the image edge. The experimental results show that the interest points extracted by the improved SURF algorithm based on ACO are more robust, and the number of them is effectively reduced. In this way, we can reduce the amount of calculation for the subsequent image registration.

[1]  Xiaohong Wang,et al.  Image Matching Based on Unification , 2011, 2011 Fourth International Joint Conference on Computational Sciences and Optimization.

[2]  S. Govindarajulu,et al.  A Comparison of SIFT, PCA-SIFT and SURF , 2012 .

[3]  Luo Juan,et al.  A comparison of SIFT, PCA-SIFT and SURF , 2009 .

[4]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[5]  Jiliu Zhou,et al.  An Ant Colony Optimization Algorithm for Image Edge Detection , 2010, 2010 International Conference on Artificial Intelligence and Computational Intelligence.

[6]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[7]  Xiao Xue-mei,et al.  Improved SIFT algorithm based on Canny feature points , 2011 .

[8]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[9]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..