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Problem statement: In computer vision, matching is an important phase for several&#13; applications (object reconstruction, robot navigation ...). The similarity measures used provided&#13; results which could be improved. Approach: This research proposed to improve image matching&#13; by using the proximity criterion. The similarity measures used mutual information and correlation&#13; coefficient. The matching was done between neighborhoods of points of interest extracted from the&#13; images. The second chance algorithm was also applied. We have worked in case which the&#13; sensor had a slight displacement between two images. The tests were performed on&#13; omnidirectional and perspective grayscale images. Results: The improvement by introducing&#13; the proximity criterion reached 15.9% for non-noised perspective images, 32.1% for noised&#13; perspective images, 47.69% for non-noised omnidirectional images and 58.5% for noised&#13; omnidirectional images. Conclusion/Recommendations: The introduction of the proximity&#13; criterion has significantly improved the performance of the matching. The method is&#13; recommended in mobile robotics, knowing that a good matching leads to a better location and&#13; better movement of the robot

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