[KSCI] Korea Science Citation Index Service

Fast Outlier Removal for Image Registration based on Modified K-means Clustering

Soh, Young-Sung (명지대학교 정보통신공학과)
Qadir, Mudasar (명지대학교 정보통신공학과)
Kim, In-Taek (명지대학교 정보통신공학과)

Publication Information

Journal of the Institute of Convergence Signal Processing / v.16, no.1, 2015 , pp. 9-14 More about this Journal

Abstract

Outlier detection and removal is a crucial step needed for various image processing applications such as image registration. Random Sample Consensus (RANSAC) is known to be the best algorithm so far for the outlier detection and removal. However RANSAC requires a cosiderable computation time. To drastically reduce the computation time while preserving the comparable quality, a outlier detection and removal method based on modified K-means is proposed. The original K-means was conducted first for matching point pairs and then cluster merging and member exclusion step are performed in the modification step. We applied the methods to various images with highly repetitive patterns under several geometric distortions and obtained successful results. We compared the proposed method with RANSAC and showed that the proposed method runs 3~10 times faster than RANSAC.

Keywords

Outlier removal; K-means clustering; Random Sample Consensus (RANSAC); image registration;

Citations & Related Records

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