A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Hough transform: A hierarchical approach
Computer Vision, Graphics, and Image Processing
A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
Randomized Hough transform (RHT): basic mechanisms, algorithms, and computational complexities
CVGIP: Image Understanding
An optimizing line finder using a Hough transform algorithm
Computer Vision and Image Understanding
Constrained Hough transforms for curve detection
Computer Vision and Image Understanding
Robust detection of lines using the progressive probabilistic Hough transform
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Accurate and robust line segment extraction by analyzing distribution around peaks in Hough space
Computer Vision and Image Understanding
Mean shift based clustering of Hough domain for fast line segment detection
Pattern Recognition Letters
Extended Hough transform for linear feature detection
Pattern Recognition
Real-time line detection through an improved Hough transform voting scheme
Pattern Recognition
Automatic detection and analysis of discontinuity geometry of rock mass from digital images
Computers & Geosciences
Statistical properties of the Hough transform estimator in the presence of measurement errors
Journal of Multivariate Analysis
Real-time FPGA implementation of Hough Transform using gradient and CORDIC algorithm
Image and Vision Computing
Line cluster detection using a variant of the Hough transform for culture row localisation
Image and Vision Computing
Contour detection based on nonclassical receptive field inhibition
IEEE Transactions on Image Processing
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The Hough transform has been a frequently used method for detecting lines in images. However, when applying Hough transform and derived algorithms using the standard Hough voting scheme on real-world images, the methods often suffer considerable degeneration in performance, especially in detection rate, because of the large amount of edges given by complex background or texture. It is very likely that these edges form false peaks in Hough space and thus produce false positives in the final results, or even suppress true peaks and cause missing lines. To reduce the impact of these texture region edges, a novel method utilizing surround suppression is proposed in this paper. By introducing a measure of isotropic surround suppression, the new algorithm treats edge pixels differently, giving small weights to edges in texture regions and large weights to edges on strong and clear boundaries, and uses these weights to accumulate votes in Hough space. In this way, false peaks formed by texture region edges are suppressed, and the quality of detection results is improved. An efficient computation method for calculating the isotropic surround suppression was also given, accelerating the proposed algorithm. Experimental results on a real-world image base show that the new method improves line detection rate significantly, compared with the standard Hough transform and the Hough transform using gradient direction information to guide the voting process. Though slower than the other two methods, the new algorithm can be preferable in applications where detection rate is of the most concern and where there is no very strict requirement for high speed performance.