Accession Number : ADA132360

Title :   Bias and Noise in the Hough Transform I. Theory,

Corporate Author : ROCHESTER UNIV NY DEPT OF COMPUTER SCIENCE

Personal Author(s) : Brown,Christopher M

PDF Url : ADA132360

Report Date : Jun 1982

Pagination or Media Count : 41

Abstract : The main results of this work are the following. 1. An approach to Hough Transforms (HT) based on a linear imaging model. The HT produces a peak in accumulators (Parameter) space corresponding to likely parameters of an interesting phenomenon in the image. It also produces background off-peak sidelobes, which are important because they can add significant variance and background bias to the accumulator space and make peak-finding a local and difficult process. 2. Definitions of bias and noise in the HT. 3. Analytical techniques for reasoning about sidelobe shapes, and some descriptions of sidelobes in practically important parameter spaces under continuous noise-free conditions. 4. The Chough method can eliminate sidelobe bias and decrease sidelobe variance. The peak height is then an unbiased estimator for the amount of evidence consistent with the peak parameter vector, and simple global techniques (such as global thresholding) will find peaks. Compared to traditional HT, CHough seems to have much better sidelobe bias properties, significantly better sidelobe variance properties, equal or worse quantization noise properties, and equal or better resistance to noise features in the image. (Author)

Descriptors :   *Image processing, *Pattern recognition, *Sidelobes, Noise reduction, Quantization, Bias, Background, Variations, Estimates, Parameters, Shape, Global, Accumulators, Histograms, Models, Peak values, Algorithms

Subject Categories : Optics

Distribution Statement : APPROVED FOR PUBLIC RELEASE