基于图像深度信息的尺度不变特征变换算法误匹配点对剔除

特征点匹配是基于特征点的图像配准技术中的一个重要环节。针对现有基于尺度不变特征变换(SIFT）图像配准技术特征点匹配不理想,也无法较客观、快速地筛选正确匹配点对的问题,提出结合图像深度信息进行特征点误匹配筛选剔除的方法。该算法首先根据模糊聚焦线索和机器学习算法估计出待配准图像的深度信息图,再提取SIFT特征点,并在特征点匹配环节利用随机抽样一致性(RANSAC)算法迭代循环,结合深度局部连续性的原理来进一步提高匹配精度。实验结果表明,该算法具有很好的误匹配点对剔除功能。

Removal of mismatches in scale-invariant feature transform algorithm using image depth information

Feature point matching is of central importance in feature-based image registration algorithms such as Scale-Invariant Feature Transform (SIFT) algorithm. Since most of the existed feature matching algorithms are not so powerful and efficient in mismatch removing, in this paper, a mismatch removal algorithm was proposed which adopted the depth information in an image to improve the performance. In the proposed approach, the depth map of an acquired image was produced using the clues of defocusing blurring effect, and machine learning algorithm, followed by SIFT feature point extraction. Then, the correct feature correspondences and the transformation between two feature sets were iteratively estimated using the RANdom SAmple Consensus (RANSAC) algorithm and exploiting the rule of local depth continuity. The experimental results demonstrate that the proposed algorithm outperforms conventional ones in mismatch removing.