In this paper, we present a high performance, coarse-to-fine scheme for image matching of infrared image and optical image. In order to eliminate the gray level difference and tolerance to distortion and noise existing in practice, this scheme uses the edge feature and combine a new similarity measures with modified Hausdorff distance to achieve the coarse-to-fine matching scheme. Our proposed method firstly extracts the feature points based on the method of Monte Carlo that reduce the computation load for the next matching, and then a new similarity measure is defined for coarse matching. Based on existing method of Monte Carlo evaluation the Huasdorff distance (MCHD), we define Monte Carlo modified Hausdorff distance (MCM-HD) to achieves the fine matching. Experiment are performed on a large test set and the result show that the fast search method diminishes the number of positions for calculate of Hausdorff distance, thus the computational load reduces and it is helpful to use Hausdorff distance in real time image matching. Compare with MCHD algorithm, our proposed method effectively improves the precision and reduces the execution time.

Date Published: 15 November 2007
PDF: 8 pages
Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 67862D (15 November 2007); doi: 10.1117/12.749656

Published in SPIE Proceedings Vol. 6786:
MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition
Tianxu Zhang; Tianxu Zhang; Carl Anthony Nardell; Carl Anthony Nardell; Hanqing Lu; Duane D. Smith; Hangqing Lu, Editor(s)