北京理工大学 增强现实智能医疗实验室

Abstract—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.