ABSTRACT 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.

中文翻译：

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多源图像匹配的最大点边缘方向直方图

摘要 从关键点或边缘确定的特征被广泛用于多源图像匹配。然而，对于表现出非线性强度变化和显着噪声的情况，为多源图像对识别足够的相同特征可能很复杂。针对现有多源图像匹配算法存在的问题，本文提出了一种鲁棒有效的多源图像匹配算法。首先，通过基于位置的增强检测器提取在重要边缘上均匀且充分分布的关键点。其次，为了有效地描述相应区域并减少噪声和非线性强度变化的影响，提出了一种新的描述符，称为点边缘方向直方图（HPEO），用于多源图像匹配。然后使用双边匹配过程去除不正确的匹配。使用标准的红外可见光数据集进行了实验，结果表明所提出的算法实现了更准确的匹配性能。