【摘要】 针对SURF(speeded-up robust features)算法计算量大、图像拼接效率低的不足,以FAST(features from accelerated segment test)角点取代SURF斑点在图像重合区域提取特征点,使用SURF描述子进行特征点描述,通过描述子降维、自适应最近邻与次近邻比值法、几何约束法剔除错误匹配点,提高匹配的准确性。匹配完成后,通过减少样本集的个数和舍弃不合理参数模型来改进RANSAC(random sample consensus)方法,获取单应性矩阵,最后进行图像变换、融合和拼接。实验结果显示,该图像拼接算法与传统的SURF算法相比,图像拼接总时间减少了12%,拼接效率得到了显著提高。更多还原

【Abstract】 With the shortages of the large amounts of calculations in speeded-up robust features(SURF)algorithm,and low efficiency of image stitching,the features from accelerated segment test(FAST) corner points were used to instead of the SURF spots in order to extract the feature points in image overlapping area.The SURF descriptor was used to describe the feature points,by using the descriptor dimensionality reduction method,the adaptive nearest neighbor and nearer neighbor ratio method,and the geometric constraint method,the false matching points were eliminated in order to improve the matching accuracy.After matching,the random sample consensus(RANSAC) algorithm was improved by reducing the number of sample set and rejecting the unreasonable parameter models to obtain the homography matrix.Finally,the image transformation,fusion and stitching were carried out.The experimental results show that the total time of image stitching is reduced by 12% compared with the traditional SURF algorithm,and the stitching efficiency is improved significantly.更多还原