Research on the Graph Matching based on Shape Context and Sequential Monte Carlo
Meiju Liu, Hongyu Yang, Zhaohua Chen, Lingyan Li
Available Online April 2015.
- https://doi.org/10.2991/amcce-15.2015.394How to use a DOI?
- Graph Matching; Shape Context; Sequential Monte Carlo
- According to the traditional shape context algorithm within the image distortion, excessive noise points and lower matching rate issues, we present a Sequential Monte Carlo algorithm based on graph matching. First, the image feature points were evenly distributed to compute shape context information. Second, remaining points obtain a histogram of all the feature points by the shape context information. Using histogram function to calculate the cost of the square distance cost, were begin to match. Finally, structuring the graph model, using graphical models construct the affinity matrix; the matrix will be close to integer quadratic programming use the Sequential Monte Carlo algorithm to find out the optimal matching schemes. Experimental results show that: the proposed algorithm in image matching to ensure a high rate, while images of different perspectives and images in quite different conditions has good robustness and stableness.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Meiju Liu AU - Hongyu Yang AU - Zhaohua Chen AU - Lingyan Li PY - 2015/04 DA - 2015/04 TI - Research on the Graph Matching based on Shape Context and Sequential Monte Carlo BT - 2015 International Conference on Automation, Mechanical Control and Computational Engineering PB - Atlantis Press SN - 1951-6851 UR - https://doi.org/10.2991/amcce-15.2015.394 DO - https://doi.org/10.2991/amcce-15.2015.394 ID - Liu2015/04 ER -