Shao-Tzu Huang

Publications

1 K-Means Based Matching Algorithm for Multi-Resolution Feature Descriptors

Authors: Chen-Chien Hsu, Shao-Tzu Huang, Wei-Yen Wang

Abstract:

Matching high dimensional features between images is computationally expensive for exhaustive search approaches in computer vision. Although the dimension of the feature can be degraded by simplifying the prior knowledge of homography, matching accuracy may degrade as a tradeoff. In this paper, we present a feature matching method based on k-means algorithm that reduces the matching cost and matches the features between images instead of using a simplified geometric assumption. Experimental results show that the proposed method outperforms the previous linear exhaustive search approaches in terms of the inlier ratio of matched pairs.

Keywords: K-Means Clustering, feature matching, scale invariant feature transform, linear exhaustive search

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Abstracts

1 K-Means Based Matching Algorithm for Multi-Resolution Feature Descriptors

Authors: Chen-Chien Hsu, Shao-Tzu Huang, Wei-Yen Wang

Abstract:

Matching high dimensional features between images is computationally expensive for exhaustive search approaches in computer vision. Although the dimension of the feature can be degraded by simplifying the prior knowledge of homography, matching accuracy may degrade as a tradeoff. In this paper, we present a feature matching method based on k-means algorithm that reduces the matching cost and matches the features between images instead of using a simplified geometric assumption. Experimental results show that the proposed method outperforms the previous linear exhaustive search approaches in terms of the inlier ratio of matched pairs.

Keywords: K-Means Clustering, SIFT, feature matching, RANSAC

Procedia PDF Downloads 187