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Please use this identifier to cite or link to this item: http://eprint.iitd.ac.in/handle/2074/1163

Title: Hough transform network: a class of networks for identifying parametric structures
Authors: Basak, Jayanta
Das, Anirban
Keywords: Hough transform
Unsupervised learning
Parametric representation
Issue Date: 2003
Citation: Neurocomputing, 51, 125-145
Abstract: A class of structure seeking neural networks is presented which are capable of learning parametric structures under unsupervised mode. The functionality of the class of networks is analogous to that of the classical Hough transform, one of the most widely used algorithms in visual pattern recognition. However, the present class of networks provide a much more efficient representation with a highly reduced storage space, capability of quantifying the impreciseness in the input, and ability to handle sparse data sets. The effectiveness of the network and its newly defined learning rules is demonstrated on different data sets under noisy conditions.
URI: http://eprint.iitd.ac.in/dspace/handle/2074/1163
Appears in Collections:Computer Science and Engineering

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