H. Ka lvia inen, P. Hirvonen, L. Xu, E. Oja, Probabilistic and non-probabilistic Hough transforms: overview and comparisons, Image and Vision Comput. 5 (4) (1995) 239---252. Home/Search Document Not in Database Summary Related Articles Check |
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RBF nets, mixture experts, and Bayesian Ying-Yang Learning - Xu (1998) (Correct)
....noise, c) d) the resulting J # (k) and J # (k) with # 1 curves for the data in (a) b) respectively. its various variants. In the end of 1980s, a new Hough like technique called randomized Hough transform (RH) has been proposed with several advantages over the conventional HT technique [39,38,13]. However, both HT and RHT techniques do not apply well to the cases of those fat lines due to strong noise disturbance. Also, there is no theoretical guide to detect the number of lines in an image. These two problems can be solved here. For a binary image with each point denoted by its ....
H. Ka lvia inen, P. Hirvonen, L. Xu, E. Oja, Probabilistic and non-probabilistic Hough transforms: overview and comparisons, Image and Vision Comput. 5 (4) (1995) 239---252.
Compression of line drawing images using Hough.. - Fränti, Ageenko.. (Correct)
....their location as a side information. The fact that a pixel is located along a line element improves the prediction of the local context model if the existence of the line is predicted accurately enough. In the present paper we study the use of the Hough transform for extracting the line elements [6]. The line feature extraction, and the storage of the line elements are optimized for maximal output quality of the feature image rather than for the compression performance. The motivation of the work, however, is merely to estimate the amount of expected improvement in the compression due to the ....
....system, however, is entirely lossless. The compressed file consists of the line elements and the compressed raster image. 2.1 Hough transform for line extraction. The motivation is to find rigid fixed length straight lines from the image. The lines are detected by the Hough transform (HT) [6] as follows: 1. Create a set of coordinates (x, y) from the black pixels in the image. 2. Transform each coordinate into a parametrized curve in the parameter space. 3. Increment the cells in the parameter space determined by the parametric curve. 4. Detect local maxima in the accumulator array. ....
H. Kälviäinen, P. Hirvonen, L. Xu, and E. Oja, "Probabilistic and non-probabilistic Hough transforms: overview and comparisons", Image and Vision Computing, 13, 239-251, May 1995.
RBF nets, mixture experts, and Bayesian Ying-Yang learning - Xu (1998) Self-citation (Xu) (Correct)
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H. Ka lvia inen, P. Hirvonen, L. Xu, E. Oja, Probabilistic and non-probabilistic Hough transforms: overview and comparisons, Image and Vision Comput. 5 (4) (1995) 239---252.
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