Research Article Open Access

Improvement of Image Matching by using the Proximity Criterion: Application to Omnidirectional and Perspective Images

Ibrahim Guelzim1, Ahmed Hammouch1 and Driss Aboutajdine1
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Journal of Computer Science
Volume 7 No. 8, 2011, 1230-1236

DOI: https://doi.org/10.3844/jcssp.2011.1230.1236

Submitted On: 29 April 2011 Published On: 15 July 2011

How to Cite: Guelzim, I., Hammouch, A. & Aboutajdine, D. (2011). Improvement of Image Matching by using the Proximity Criterion: Application to Omnidirectional and Perspective Images. Journal of Computer Science, 7(8), 1230-1236. https://doi.org/10.3844/jcssp.2011.1230.1236

Abstract

Problem statement: In computer vision, matching is an important phase for several applications (object reconstruction, robot navigation ...). The similarity measures used provided results which could be improved. Approach: This research proposed to improve image matching by using the proximity criterion. The similarity measures used mutual information and correlation coefficient. The matching was done between neighborhoods of points of interest extracted from the images. The second chance algorithm was also applied. We have worked in case which the sensor had a slight displacement between two images. The tests were performed on omnidirectional and perspective grayscale images. Results: The improvement by introducing the proximity criterion reached 15.9% for non-noised perspective images, 32.1% for noised perspective images, 47.69% for non-noised omnidirectional images and 58.5% for noised omnidirectional images. Conclusion/Recommendations: The introduction of the proximity criterion has significantly improved the performance of the matching. The method is recommended in mobile robotics, knowing that a good matching leads to a better location and better movement of the robot.

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Keywords

  • Images matching
  • 3D reconstruction
  • omnidirectional vision
  • proximity criterion