CODE: Coherence based decision boundaries for feature correspondence

• Main Authors: LIN, Wen-yan, WANG, Fan, CHENG, Ming-Ming, YEUNG, Sai-Kit, TORR, Philip H. S., LU, Jiangbo
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Feature matching; wide-baseline matching; visual correspondence; RANSAC; Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/4800; https://ink.library.smu.edu.sg/context/sis_research/article/5803/viewcontent/matching.pdf
• Institution: Singapore Management University

A key challenge in feature correspondence is the difficulty in differentiating true and false matches at a local descriptor level. This forces adoption of strict similarity thresholds that discard many true matches. However, if analyzed at a global level, false matches are usually randomly scattered while true matches tend to be coherent (clustered around a few dominant motions), thus creating a coherence based separability constraint. This paper proposes a non-linear regression technique that can discover such a coherence based separability constraint from highly noisy matches and embed it into a correspondence likelihood model. Once computed, the model can filter the entire set of nearest neighbor matches (which typically contains over 90% false matches) for true matches. We integrate our technique into a full feature correspondence system which reliably generates large numbers of good quality correspondences over wide baselines where previous techniques provide few or no matches.