基于快速特征点提取和描述算法与色调、饱和度和明度的图像特征点匹配算法

Image Feature Point Matching Algorithm Based on Oriented Fast and Rotated Brief Algorithm and Hue, Saturation and Value

提出了一种基于快速特征点提取和描述(ORB)算法与色调、饱和度和明度(HSV)的图像特征点匹配算法,并进行了实验研究。首先利用双边滤波和均值滤波结合对图像进行预处理;然后使用ORB算法进行特征点提取;接着利用K维二叉树(K-D Tree)算法与汉明距离进行特征点粗匹配;再利用图像的HSV信息对匹配特征点对进行二次筛选。实验结果表明,在图像进行预处理阶段,采用方差、Vollath、信息熵的加权平均作为评价指标,与原图、直方图均衡化、双边滤波结果相比,双边滤波和均值滤波结合得到的图像指标值最佳;在特征点匹配和图像拼接阶段,利用HSV信息筛选后特征点匹配正确率提高了12.60个百分点,由此得到的图像拼接结果质量更好,其自然图像质量评价(NIQE)指数值更小。

An image feature point matching algorithm based on the oriented fast and rotated brief(ORB) algorithm and hue, saturation and value(HSV) is proposed and the experimental research is carried out. Firstly, the image is preprocessed by the combination of bilateral filtering and mean filtering. Secondly, the ORB algorithm is used to extract feature points. Thirdly, the K-D Tree algorithm and Hamming distance are used for matching of feature points roughly, and then the HSV information of the image are used for the secondary screening of matched feature point pairs. The experimental results show that, in the image preprocessing stage, the weighted average of variance, vollath and information entropy is used as the evaluation index, and compared with the original image,histogram equalization and bilateral filtering results, the evaluation index value obtained by the combination of bilateral filtering and mean filtering is the best. In the stage of feature point matching and image mosaic, the average matching correct rate of feature points is improved by 12.60 percentage points after using HSV information, and the quality of image mosaic result is better, as its natural image quality evaluation(NIQE) index value is smaller.