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

Author:

Dou Yiwen1,Hao Kuangrong1,Ding Yongsheng1,Mao Min1

Affiliation:

1. Engineering Research Center of Digitized Textile & Apparel Technology, Ministry of Education, College of Information Science and Technology, Donghua University, Shanghai 201620, China

Abstract

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.

Funder

Key Project of the NationalNature Science Foundation of China

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

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