Edge detection and motion detection
Image and Vision Computing
The Radon transform and its application to shape parametrization in machine vision
Image and Vision Computing - Special issue: papers from the second Alvey Vision Conference
A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
Kendall's advanced theory of statistics
Kendall's advanced theory of statistics
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Accurate and robust line segment extraction by analyzing distribution around peaks in Hough space
Computer Vision and Image Understanding
Accuracy of the straight line Hough transform: the non-voting approach
Computer Vision and Image Understanding
The Distinctiveness of a Curve in a Parameterized Neighborhood: Extraction and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance characterization in computer vision: A guide to best practices
Computer Vision and Image Understanding
On Straight Line Segment Detection
Journal of Mathematical Imaging and Vision
Advanced hough transform using a multilayer fractional fourier method
IEEE Transactions on Image Processing
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Real-time line detection using accelerated high-resolution Hough transform
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Edge curvature and convexity based ellipse detection method
Pattern Recognition
Real-time detection of lines using parallel coordinates and CUDA
Journal of Real-Time Image Processing
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In this paper a formal, quantitative approach to designing optimum Hough transform (HT) algorithms is proposed. This approach takes the view that a HT is a hypothesis testing method. Each sample in the HT array implements a test to determine whether a curve with the given parameters fits the edge point data. This view allows the performance of HT algorithms to be quantified. The power function, which gives the probability of rejection as a function of the underlying parametric distribution of data points, is shown to be the fundamentally important characteristic of HT behaviour. Attempting to make the power function narrow is a formal approach to optimizing HT performance. To illustrate how this framework is useful the particular problem of line detection is discussed in detail. It is shown that the hypothesis testing framework leads to a redefinition of the HT in which the values are a measure of the distribution of points around a curve rather than the number of points on a curve. This change dramatically improves the sensitivity of the method to small structures. The solution to many HT design problems can be posed within the framework, including optimal quantizations and optimum sampling of the parameter space. In this paper the authors consider the design of optimum I-D filters, which can be used to sharpen the peak structure in parameter space. Results on several real images illustrate the improvements obtained.