Results 181 to 190 of about 57,221 (223)

On the inverse Hough transform

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999
In this paper, an inverse Hough transform algorithm is proposed. This algorithm reconstructs correctly the original image, using only the data of the Hough transform space and it is applicable to any binary image. As a first application, the inverse Hough transform algorithm is used for straight-line detection and filtering.
Anastasios L. Kesidis, Nikos Papamarkos
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In defense of the Hough transform

Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170), 2002
The Hough transform has been a widely used technique for geometric primitive extraction. However, recently, a new family of techniques based on optimization, such as the genetic algorithm, the tabu search algorithm, the algorithm based on random samples of minimum subset, claimed their superiority over the Hough transform.
Zhanyi Hu, Yi Yang, Hung-Tat Tsui
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Optical Hough transform

Applied Optics, 1986
An optical technique is presented for achieving the Hough transform at video rates. Both coherent and incoherent approaches are discussed and analyzed. An incoherent system which combines edge enhancement with the Hough transform is also considered. Use of the optical Hough transform for the location of the bridges and roadways in outdoor scenes and ...
W H, Steier, R K, Shori
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Parametrization of the Hough Transform

1989
The Hough transform is used in picture processing for detecting linear structures in binary images. It associates to each line running through the image the integral along this line of the gray value function. There are many different possibilities for parametrizing lines in the plane and, depending on the applications under consideration, there exist ...
Ulrich Eckhardt, Gerd Maderlechner
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Projection of the Hough Transform

1987
The Hough (or Radon) transform in its classical form is used for identifying lines in binary images (Hough |7|, Deans |4|; Radon |8|). It can be interpreted as an evidence accumulation method. Its main advantage is that it is relatively insensitive to noise, even interrupted lines can be detected by means of it.
Ulrich Eckhardt, Gerd Maderlechner
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Antialiasing the Hough transform

CVGIP: Graphical Models and Image Processing, 1991
Abstract The discretization of the Hough transform parameter plane is considered. It is shown that the popular accumulator method implies sampling of a nonbandlimited signal. The resultant aliasing accounts for several familiar difficulties in the algorithm. Bandlimiting the parameter plane would allow Nyquist sampling, thus aliasing could be avoided.
Nahum Kiryati, Alfred M. Bruckstein
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On the hough transform of a polygon

Pattern Recognition Letters, 1996
It was recently shown that the Hough transform of a convex polygon uniquely determines it. In this note we generalize this result and lend further insight into why a non-convex polygon is, in general, not uniquely determined by its Hough transform.
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Probabilistic approach to the Hough transform

Image and Vision Computing, 1990
Abstract It is shown that there is a strong relationship between the Hough transform and the maximum likelihood method. The Probabilistic Hough Transform (PHT), a mathematically ‘correct’ form of the Hough transform, is defined as a likelihood function in the output parameters.
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Performance prediction of the hough transform

Journal of Computer Science and Technology, 1997
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhanyi Hu, Songde Ma
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Diagonal quantization for the Hough transform

Pattern Recognition Letters, 1993
Abstract Selecting the proper quantization intervals of the Parameter Space (PS) can improve the accuracy and efficiency of the Hough Transform (HT) for image processing. This paper proposes a technique to generally define the quantization intervals of the PS for any parameterization of a line by considering only the greatest tolerance of the y or x ...
Dennis N. K. Leung, Lam T. S. Lam, Wilson C. Y. Lam   +2 more
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