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Proceedings Paper

Novel combinatorial probabilistic Hough transform technique for detection of underwater bubbles
Author(s): John Y. Goulermas; Panos Liatsis
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Paper Abstract

Combinatorial Probabilistic Hough Transforms (CPHTs) are a class of HTs that transform minimal subsets of points required to define an instance of the sought shape to single parameter cells, thus reducing redundant evidence. Existing CPHTs discard valuable information contained in the gradient of the object outlines. This research proposes a novel HT technique for detection of circular instances, called the C2PHT. The concept of the C2PHT is the incorporation of gradient information which results to a further reduction in the generation of redundant evidence, by transforming point- tuples to very small sets of parameter cells. Thus, the complexity of sampling is decreased to O(N2) enabling much more fertile sampling and faster detection. An additional characteristic of C2PHT is the strict conditional transformation scheme which means that only a very small fraction of feature space becomes eligible of voting and hence, an even higher suppression of correlated noise is achieved. The C2PHT allows very economic accumulator architectures to be used. In correspondence with the high reduction of redundant votes, it greatly mitigates the burden of the peak detection process. The performance of the technique is evaluated with synthetic and real-world underwater bubble images.

Paper Details

Date Published: 15 April 1997
PDF: 10 pages
Proc. SPIE 3029, Machine Vision Applications in Industrial Inspection V, (15 April 1997); doi: 10.1117/12.271237
Show Author Affiliations
John Y. Goulermas, University of Manchester Institute of Science and Technology (United Kingdom)
Panos Liatsis, University of Manchester Institute of Science and Technology (United Kingdom)


Published in SPIE Proceedings Vol. 3029:
Machine Vision Applications in Industrial Inspection V
A. Ravishankar Rao; Ning S. Chang, Editor(s)

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