Abstract
Robust and efficient feature matching in multi-source image is a difficult task duo to significant noise and intensity changes. Motivated by problems encountered in present methods, a local similarity descriptor, called local central-tendency similarity (LCTS), is proposed to eliminate the effects of noise and intensity changes by establishing the correlation between the central pixels and surrounding regions of interest. Then, an efficient template-matching framework–namely, the best-buddies direction pairs (BBDP)–is developed for multi-source images by calculating the number of nearest-neighbor points between template and target. When tested on 200 pairs of multi-source images acquired from unconstrained environments, the results demonstrate that BBDP achieves higher computational efficiency and better matching performance than many popular multi-source image matching algorithms.
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