Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 3

RANSAC Related Abstracts

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

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


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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2 An Efficient Fundamental Matrix Estimation for Moving Object Detection

Authors: Ho-Youl Jung, Ju H. Park, S. M. Lee, Yeongyu Choi


In this paper, an improved method for estimating fundamental matrix is proposed. The method is applied effectively to monocular camera based moving object detection. The method consists of corner points detection, moving object’s motion estimation and fundamental matrix calculation. The corner points are obtained by using Harris corner detector, motions of moving objects is calculated from pyramidal Lucas-Kanade optical flow algorithm. Through epipolar geometry analysis using RANSAC, the fundamental matrix is calculated. In this method, we have improved the performances of moving object detection by using two threshold values that determine inlier or outlier. Through the simulations, we compare the performances with varying the two threshold values.

Keywords: optical flow, RANSAC, corner detection, epipolar geometry

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1 Lane Detection Using Labeling Based RANSAC Algorithm

Authors: Ho-Youl Jung, Ju H. Park, Yeongyu Choi


In this paper, we propose labeling based RANSAC algorithm for lane detection. Advanced driver assistance systems (ADAS) have been widely researched to avoid unexpected accidents. Lane detection is a necessary system to assist keeping lane and lane departure prevention. The proposed vision based lane detection method applies Canny edge detection, inverse perspective mapping (IPM), K-means algorithm, mathematical morphology operations and 8 connected-component labeling. Next, random samples are selected from each labeling region for RANSAC. The sampling method selects the points of lane with a high probability. Finally, lane parameters of straight line or curve equations are estimated. Through the simulations tested on video recorded at daytime and nighttime, we show that the proposed method has better performance than the existing RANSAC algorithm in various environments.

Keywords: canny edge detection, K-means algorithm, RANSAC, inverse perspective mapping

Procedia PDF Downloads 48