A Mean-Shift-Based Feature Descriptor for Wide Baseline Stereo Matching

• Main Authors: Yiwen Dou, Kuangrong Hao, Yongsheng Ding, Min Mao
• Format: Article
• Language: English
• Published: Hindawi Limited 2015-01-01
• Series: Mathematical Problems in Engineering
• Online Access: http://dx.doi.org/10.1155/2015/398756

We propose a novel Mean-Shift-based building approach in wide baseline. Initially, scale-invariance feature transform (SIFT) approach is used to extract relatively stable feature points. As to each matching SIFT feature point, it needs a reasonable neighborhood range so as to choose feature points set. Subsequently, in view of selecting repeatable and high robust feature points, Mean-Shift controls corresponding feature scale. At last, our approach is employed to depth image acquirement in wide baseline and Graph Cut algorithm optimizes disparity information. Compared with the existing methods such as SIFT, speeded up robust feature (SURF), and normalized cross-correlation (NCC), the presented approach has the advantages of higher robustness and accuracy rate. Experimental results on low resolution image and weak feature description in wide baseline confirm the validity of our approach.