Reliable RANSAC Using a Novel Preprocessing Model | Zendy

Xiaoyan Wang | Zendy; Hui Zhang | Zendy; Sheng Liu | Zendy

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Reliable RANSAC Using a Novel Preprocessing Model

Author(s) -

Xiaoyan Wang,

Hui Zhang,

Sheng Liu

Publication year - 2013

Publication title -

computational and mathematical methods in medicine

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.462

H-Index - 48

eISSN - 1748-6718

pISSN - 1748-670X

DOI - 10.1155/2013/672509

Subject(s) - ransac , preprocessor , scale invariant feature transform , computer science , matching (statistics) , set (abstract data type) , artificial intelligence , feature (linguistics) , pattern recognition (psychology) , computer vision , feature extraction , mathematics , image (mathematics) , statistics , linguistics , philosophy , programming language

Geometric assumption and verification with RANSAC has become a crucial step for corresponding to local features due to its wide applications in biomedical feature analysis and vision computing. However, conventional RANSAC is very time-consuming due to redundant sampling times, especially dealing with the case of numerous matching pairs. This paper presents a novel preprocessing model to explore a reduced set with reliable correspondences from initial matching dataset. Both geometric model generation and verification are carried out on this reduced set, which leads to considerable speedups. Afterwards, this paper proposes a reliable RANSAC framework using preprocessing model, which was implemented and verified using Harris and SIFT features, respectively. Compared with traditional RANSAC, experimental results show that our method is more efficient.

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