A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Radon and projection transform-based computer vision: algorithms, a pipeline architecture, and industrial applications
Partitioned Hough transform for ellipsoid detection
Pattern Recognition
Curve description using the inverse Hough transform
Pattern Recognition
A combinatorial Hough transform
Pattern Recognition Letters
A new curve detection method: randomized Hough transform (RHT)
Pattern Recognition Letters
A hierarchical approach to line extraction based on the Hough transform
Computer Vision, Graphics, and Image Processing
Antialiasing the Hough transform
CVGIP: Graphical Models and Image Processing
CVGIP: Image Understanding
Randomized Hough transform (RHT): basic mechanisms, algorithms, and computational complexities
CVGIP: Image Understanding
Circular arc detection based on Hough transform
Pattern Recognition Letters
Hough transform based ellipse detection algorithm
Pattern Recognition Letters
Randomized Hough transform: improved ellipse detection with comparison
Pattern Recognition Letters
Some remarks on the straight line Hough transform
Pattern Recognition Letters
On the Inverse Hough Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combination of local and global line extraction
Real-Time Imaging
Efficient randomized algorithms for robust estimation of circular arcs and aligned ellipses
Computational Geometry: Theory and Applications
A new randomized algorithm for detecting lines
Real-Time Imaging
Computer Vision
Point-to-line mappings as Hough transforms
Pattern Recognition Letters
Hough Transform from the Radon Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advanced Radon transform using generalized interpolated Fourier method for straight line detection
Computer Vision and Image Understanding
Real-time line detection using accelerated high-resolution Hough transform
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Continuous plane detection in point-cloud data based on 3D Hough Transform
Journal of Visual Communication and Image Representation
Hi-index | 0.00 |
In this paper, we introduce the N-point Hough transform for the detection of a large number of planar lines in a noisy image. The N-point Hough transform yields the randomized Hough transform and the three-point Hough transform if we set N=2 and N=3, respectively. From the viewpoint of the number of sample points required for the computation of lines, the N-point Hough transform is a generalization of the usual randomized Hough transform. The three-point Hough transform is introduced to increase the speed of the randomized Hough transform; the third point is used to avoid the selection of meaningless first and second sample points, which are used for the computation of the parameters of a line. This additional sample point guarantees the accuracy and robustness of a line detected using the first and second sample points. The N-point Hough transform evaluates the accuracy and robustness of a computed line using additional (N-2) points for each line. The evaluation in the N-point Hough transform is achieved by counting the cardinality of sample points in the neighborhood of this line as the support of the sample points for the acceptance of this line. First, to define the neighborhood of a line mathematically, in this paper we clarify the relationship between a line and a set of parameters in the voting space using geometric duality. This relationship allows us to define a metric in the voting space. The metric is used for the clustering of bins in the spherical voting space to guarantee the accurate and robust computation of lines. Finally, we evaluate the performance of the N-point Hough transform by comparing it with the randomized Hough transform, which is the two-point Hough transform in our framework of the voting method. This comparative study shows the geometric and computational advantages of the N-point Hough transform.