基于SR-SURF的岩石显微图像拼接

Rock Microscopic Image Stitching Based on SR-SURF

岩石显微图像拼接是对岩石分析和研究的关键环节,由于岩石显微图像数量多(成百上千张)内容丰富并且包含大量相似易混淆区域,导致拼接速率和配准准确率低,并且多幅图像拼接时会产生误差累积导致拼接错位,针对此问题提出了一种SR-SURF(similar region-SURF)的岩石显微图像拼接方法.首先选用哈希指纹快速提取相似区域(similar region),然后在此区域检测特征点;之后利用改进的RANSAC(random sample consensus)算法剔除错误匹配点;再然后选用最佳模板匹配纠正错误配准图像;最后引入最小二乘法消除单应性矩阵相乘产生的累计误差;实验结果显示本文的算法消除了多幅图像拼接产生的累计误差,解决了拼接错位问题,提高了拼接速率和配准准确率,具有较高的实用价值,推动了岩石薄片的数字化存储进程.

Rock microscopic image stitching is a key part of rock analysis and research.The rock microscopic images are large in number(hundreds of images)and rich in content and contain many similar and confusing areas,which result in low stitching efficiency and low alignment accuracy.In addition,the stitching of multiple images will result in error accumulation and misalignment.For this problem,a similar region-SURF(SR-SURF)method for rock microscopic image stitching is proposed.Firstly,similar regions are quickly extracted by using hash fingerprints.Secondly,feature points are detected in this region.Then the improved random sample consensus(RANSAC)algorithm is used to eliminate the wrong matching points.The misaligned image is corrected by the best matching template.Finally,the least squares method is introduced to eliminate the cumulative error caused by the cumulative multiplication of homography matrices.The experimental results show that the algorithm proposed in this study eliminates the cumulative error caused by multiple image stitching and solves the problem of stitching misalignment,which improves the stitching speed and alignment accuracy.It has high practical value and promotes the digital storage process of rock slices.