Wei-Yen Wang

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

2 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

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1 Image Segmentation of Visual Markers in Robotic Tracking System Based on Differential Evolution Algorithm with Connected-Component Labeling

Authors: Chen-Chien Hsu, Wei-Yen Wang, Shu-Yu Hsu

Abstract:

Color segmentation is a basic and simple way for recognizing the visual markers in a robotic tracking system. In this paper, we propose a new method for color segmentation by incorporating differential evolution algorithm and connected component labeling to autonomously preset the HSV threshold of visual markers. To evaluate the effectiveness of the proposed algorithm, a ROBOTIS OP2 humanoid robot is used to conduct the experiment, where five most commonly used color including red, purple, blue, yellow, and green in visual markers are given for comparisons.

Keywords: Humanoid Robot, Differential Evolution, color segmentation, connected component labeling

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