光电信息技术研究室知识库

本发明涉及一种图像匹配系统的异源测试图像标定方法，包括以下步骤：建立可见光图像和红外图像序列的相似特征指标候选集合；利用灰色关联度分析方法度量异源图像候选集合中各个特征的相似性，并依此对相似特征指标集合进行关联程度排序，选取相似特征向量；根据基于Canny算子的边缘特征提取算法的阈值参数，选取不同参数，计算并划分特征值组合，形成标定测试试验方案；分别统计光电成像匹配系统对可见光图像和红外图像的匹配率，依据红外图像输出的匹配率结果进行分级，标定对应的可见光图像特征值范围。本发明保证在匹配系统工作在未知环境中，可以利用标定后的可见光图像完成匹配系统的测试与评估，保障图像匹配系统顺利完成任务。

The invention relates to a different-source test image calibration method of an image matching system, comprising the following steps: establishing a similar feature index candidate set of visible-light image and infrared image sequences; using a grey correlation analysis method to measure the similarity between the features in a different-source image candidate set, performing correlation degree sorting on the similar feature index candidate set on the basis, and selecting similar feature vectors; selecting different parameters according to the threshold parameter of an edge feature extraction algorithm based on a Canny operator, calculating and dividing feature value combinations, and forming a calibration test pilot scheme; and making a statistic analysis of the matching rate of a photoelectric imaging matching system to visible-light images and infrared images, performing grading based on a matching rate result output by an infrared image, and calibrating the feature value range of a corresponding visible-light image. Through the method, the matching system can be tested and evaluated through use of a calibrated visible-light image when working in an unknown environment, and therefore, the image matching system can complete the task smoothly.