为了实现红外图像与可见光图像的信息融合，弥补单一模态图像的不足，提出了一种基于显著性分析与改进的边缘方向直方图 EOH（Edge Orientation Histogram ）特征的红外与可见光图像配准算法。该算法首先利用显著性分析技术找到可见光图像中的重要信息，得到显著性图；将其与可见光图像融合，实现可见光图像中重要信息的划分。然后，利用自适应 FAST （Features from Accelerated Segment Test ）算法，探测可见光与红外图像上的特征点；利用改进的 EOH ，描述特征点。最后，根据描述计算特征点的相似性，在可见光与红外图像上找出对应的特征点，实现红外与可见光图像的匹配。在3种不同情况下对红外与可见光图像数据进行了配准实验。结果表明：在红外图像与可见光图像采集条件相似情况下，特征点正确匹配率为96．55％，而在图像采集条件差异较大的情况下，特征点正确匹配率可达74．21％。该算法可实现红外与可见光图像的精确快速匹配，即使红外图像与可见光图像采集的角度与位置均存在较大差异的情况下，仍可以满足红外与可见光图像匹配对精度和稳定性的要求。%To realize the information fusion of infrared and visible images and make up the deficiency of the single modality image ,a new algorithm based on saliency and Edge Orientation Histogram (EOH) features was proposed .Firstly ,the saliency analysis was used to find the important information of the visible image and to obtain the saliency map .By fusing it with the visible image , the important information in the visible image was divided .Then ,adaptive Features from Accelerated Segment Test (FAST ) algorithm was employed in detecting feature points on the visible image and infrared image , and the improved EOH was used to describe the detected feature points . Finally , corresponding feature points were found by calculating the similarity of feature points in the visible and infrared images and the infrared and visible images were matched .An image matching experiments at three conditions were carried out ,and the results indicate that when the collection conditions between the infrared and visible images are similar ,the feature matching accuracy reaches 96 .55% . When the difference of collection conditions between the infrared and visible images is large , the feature matching accuracy still can reach 74 .21% .The algorithm realizes fast and accurate matching of infrared and visible images ,and meets the requirements of image matching for accuracy and stability ,especially under a collection condition that the infrared and visible images are bigger different .
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