Open AccessPoint match refinement through rigidity constraint and vote
Author(s) -
Liu Yonghuai,
Zhao Yitian,
Li Longzhuang,
Han Jiwan
Publication year - 2016
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2016.1643
Subject(s) - rigidity (electromagnetism) , computer science , constraint (computer aided design) , computer vision , artificial intelligence , point (geometry) , algorithm , robustness (evolution) , matching (statistics) , mathematics , engineering , geometry , statistics , biochemistry , chemistry , structural engineering , gene
Feature extraction and matching has been widely used for the registration of overlapping partial shapes. However, the established tentative point matches (TPMs) usually include a large proportion of false ones due to a number of factors: imaging noise, occlusion, repetitive structures, and cluttered background. To apply the rigidity constraint to refine these TPMs in three main steps is proposed. First, a set of one‐to‐many point matches is established, then use them to vote against each other, and finally select such TPMs with the same surface type, the minimum error, and the most votes significantly more than the second most ones as the more reliable ones. A comparative study using real data captured by a Microsoft Kinect sensor shows that the refinement is successful.