Utilize Feature Distinctiveness to Recover Feature Correspondences
Feature corresponding problem is important for many computer vision tasks, but it is very difficult when the feature sets are corrupted by noise features. This paper formulates this problem as an optimization problem, and then proposes to measure distinctiveness of one feature match based on appearance similarity between two features. Then candidate feature matches are initialized based on their distinctiveness values. By weighting each candidate feature match by its distinctiveness value, the feature corresponding map can be robustly estimated by weighted Support Vector Regression Machine. Then the outlier feature matches are rejected by checking their geometric consistence with the estimated corresponding map. The proposed algorithm iterate above steps until the true feature correspondences are recovered. Experimental results demonstrate the effectiveness of this method.
feature distinctiveness feature correspondence corresponding problem weighted Support Vector Regression
Bangsheng Cheng
College of Computer Science and Technology Chengdu University of Information Technology Chengdu,China
国际会议
昆明
英文
443-447
2010-10-17（万方平台首次上网日期，不代表论文的发表时间）