A Robust Appearance Model and Similarity Measure for Image Matching
Dong Liang*,**, Shun’ ichi Kaneko**, and Yutaka Satoh***
*College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics
Yudao Street 29, Nanjing 210016, China
**Graduate School of Information Science and Technology, Hokkaido University
Kita 14, Nishi 9, Sapporo 060-0814, Japan
***National Institute of Advanced Industrial Science and Technology (AIST)
Tsukuba-shi 305-8568, Japan
An ideal similarity measure for matching image should be discriminative, producing a conspicuous correlation peak and suppressing false local maxima. Image matching tasks in practice, however, often involves complex conditions, such as blurring and fluctuating illumination. These may cause the similarity measure to not be discriminative enough. We utilized a robust scene modeling method to model the appearance of an image and propose an associated similarity measure for image matching. The proposed method utilizes a spatio-temporal learning stage to select a group of supporting pixels for each target pixel, then builds a differential statistic model of them to describe the uniqueness of the spatial structure and to provide illumination invariance for robust matching. We utilized this method for image matching in several challenging environments. Experimental results show that the proposed similarity measure produces explicit correlation peaks to achieve robust image matching.
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