Contribution to the Prediction of Performances of the Hough Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Hough transform: A hierarchical approach
Computer Vision, Graphics, and Image Processing
Digital Parallelism, Perpendicularity, and Rectangles
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved localization in a generalized Hough scheme the detection of straight edges
Image and Vision Computing
A Monolithic Hough Transform Processor Based on Restructurable VLSI
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Real-Time Processor for the Hough Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hough transform algorithms for mesh-connected SIMD parallel processors
Computer Vision, Graphics, and Image Processing
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
The Geometry of Basis Sets for Morphologic Closing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Clustering in Hough Space for Identification of Partially Occluded Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Inverse Hough Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contribution to the Determination of Vanishing Points Using Hough Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological Filtering as Template Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coronal loop detection from solar images
Pattern Recognition
An improved Hough transform neighborhood map for straight line segments
IEEE Transactions on Image Processing
Exact, scaled image rotation using the finite radon transform
DGCI'09 Proceedings of the 15th IAPR international conference on Discrete geometry for computer imagery
Exact, scaled image rotations in finite Radon transform space
Pattern Recognition Letters
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The grid of discrete sampled data points in a digital image supports a limited set of lines at angles and displacements 'natural' to that grid. The effect of this implicit line quantization on the parametrization of the Hough transform is presented. The function describing the discrete Hough transforms line-detection sensitivity is derived. Expressions for the orientation, frequency, and popularity of lines in the natural set are given. The results obtained are of importance to data arrays of small size. The distribution of lines in the natural set is also important as it determines the precision and reliability with which straight lines can be measured on a discrete imaging array. From the natural line set concept, a general (a,d) slope/offset straight-line parametrization is developed for which the Hough transform is compact and fast to compute, and which is as easy to interpret as the class (p, theta ) parametrization.