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Communication Dans Un Congrès Année : 2015

Algorithm and Implementation of an Associative Memory for Oriented Edge Detection Using Improved Clustered Neural Networks

Résumé

Associative memories are capable of retrieving previously stored patterns given parts of them. This feature makes them good candidates for pattern detection in images. Clustered Neural Networks is a recently-introduced family of associative memories that allows a fast pattern retrieval when implemented in hardware. In this paper, we propose a new pattern retrieval algorithm that results in a dramatically lower error rate compared to that of the conventional approach when used in oriented edge detection process. This function plays an important role in image processing. Furthermore, we present the corresponding hardware architecture and implementation of the new approach in comparison with a conventional architecture in literature, and show that the proposed architecture does not significantly affect hardware complexity.
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Dates et versions

hal-02116052 , version 1 (27-02-2024)

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Robin Danilo, Hooman Jarollahi, Vincent Gripon, P Coussy, Laura Conde-Canencia, et al.. Algorithm and Implementation of an Associative Memory for Oriented Edge Detection Using Improved Clustered Neural Networks. 2015 International Symposium on Circuits and Systems (ISCAS 2015), May 2015, Lisbonne, Portugal. pp.2501 - 2504, ⟨10.1109/ISCAS.2015.7169193⟩. ⟨hal-02116052⟩
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