A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Line detection in digital pictures: a hypothesis prediction/verification pardigm
Computer Vision, Graphics, and Image Processing
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Pattern Recognition
Algorithms for clustering data
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A combinatorial Hough transform
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A new curve detection method: randomized Hough transform (RHT)
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A hierarchical approach to line extraction based on the Hough transform
Computer Vision, Graphics, and Image Processing
A probabilistic Hough transform
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Probabilistic approach to the Hough transform
Image and Vision Computing
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Line Segments by Stick Growing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical model-based clustering of large datasets through fractionation and refractionation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A simple and robust line detection algorithm based on small eigenvalue analysis
Pattern Recognition Letters
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Robotics and Autonomous Systems
An improved Hough transform voting scheme utilizing surround suppression
Pattern Recognition Letters
Fast hough transform based on 3d image space division
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Research on Parts Measurement Method Based on Machine Vision
International Journal of Advanced Pervasive and Ubiquitous Computing
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This paper proposes a new algorithm for extracting line segments from edge images. Basically, the method performs two consecutive stages. In the first stage, the algorithm follows a line segment random window randomized Hough transform (RWRHT) based approach. This approach provides a mechanism for finding more favorable line segments from a global point of view. In our case, the RWRHT based approach is used to actualise an accurate Hough parameter space. In the second stage, items of this parameter space are unsupervisedly clustered in a set of classes using a variable bandwidth mean shift algorithm. Cluster modes provided by this algorithm constitute a set of base lines. Thus, clustering process allows using accurate Hough parameters and, however, detecting only one line when pixels along it are not exactly collinear. Edge pixels lying on the lines grouped to generate each base line are projected onto this base line. A fast and purely local grouping algorithm is employed to merge points along each base line into line segments. We have performed several experiments to compare the performance of our method with that of other methods. Experimental results show that the performance of the proposed method is very high in terms of line segment detection ability and execution time.