Histogram of maximal point-edge orientation for multi-source image matching
Quan Wu, Guili Xu, Yuehua Cheng, Wende Dong, Limin Ma, Zhenhua Li · International Journal of Remote Sensing · 2020
Features determined from keypoints or edges are widely used in multi-source image matching. However, for cases that exhibit non-linear intensity changes and significant noise, identifying sufficient identical features for multi-source image pairs can be complicated. Motivated by problems encountered in existing multi-source image matching algorithms, a robust and effective algorithm for multi-source image matching was proposed in this paper. First, keypoints uniformly and sufficiently distributed on the significant edge were extracted by a location-based boosting detector. Second, in order to effectively describe the corresponding region and reduce the influence of noise and non-linear intensity changes, a novel descriptor, denoted as the histogram of point-edge orientation (HPEO) was proposed for multi-source image matching. A bilateral matching process was then used to remove the incorrect matches. Experiments were performed with standard infrared-visible datasets and the results demonstrate that the proposed algorithm achieves a more accurate matching performance.