Research on multi-view image stitching method in mine
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摘要: 针对煤矿井下监控图像视野范围较小、细节特征不清晰等问题，提出了一种矿井多视角图像拼接方法。首先，采用一种改进对比度受限的自适应直方图均衡化方法对图像进行预处理，以突出图像细节，提高对比度；其次，选用ORB算法提取图像特征点，采用改进的Brief算法计算特征描述子；再次，通过K最近邻（KNN）算法实现特征点对粗匹配，基于随机采样一致性（RANSAC）算法对误匹配特征点对进行筛选、消除，并求解最优透视变换矩阵，对待匹配图像像素点进行坐标变换；最后，采用帽子函数加权平均融合算法对固定图像和待匹配图像进行拼接融合。实验结果表明：ORB算法较尺度不变的特征变换(SIFT)、KAZE算法对于单张图像提取的特征点数分别减少48%，33%，提高了有效特征点提取能力，特征点提取耗时分别减少17%，34%，提高了计算效率；采用该方法拼接的图像避免了连接处的裂缝、黑线现象，图像过渡自然，清晰度高。Abstract: In order to solve the problems of small field of view and unclear detail features of monitoring images in coal mines, a multi-view image stitching method in mine is proposed. Firstly, an improved adaptive histogram equalization method with contrast limitation is used to pre-process the images to highlight the image details and improve the contrast. Secondly, the ORB algorithm is used to extract the image feature points, and the improved Brief algorithm is used to calculate the feature descriptors. Thirdly, the K-nearest neighbor(KNN) algorithm is used to achieve rough matching of feature point pairs, and the random sample consensus(RANSAC) algorithm is used to filter and eliminate the mismatched feature point pairs, and the optimal perspective transformation matrix is solved to transform the coordinates of the pixel points of the image to be matched. Finally, the hat function weighted average fusion algorithm is used to stitch and fuse the fixed image and the image to be matched. The experimental results show that compared with the speeded up robust features(SIFT) and KAZE algorithms, the ORB algorithm reduces the number of feature points extracted for a single image by 48% and 33% respectively, which improves the effective feature point extraction capability. The feature point extraction time is reduced by 17% and 34% respectively, which improves the calculation efficiency. The images stitched by this method avoid the phenomenon of cracks and black lines at the joints, the image transition is natural, and the definition is high.
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期刊类型引用(11)
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