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

R. Danilo, H. Jarollahi, V. Gripon, P. Coussy, L. Conde-Canencia and W. J. Gross, "Algorithm and Implementation of an Associative Memory for Oriented Edge Detection Using Improved Clustered Neural Networks," in Proceedings of ISCAS Conference, pp. 2501--2504, May 2015.

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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Bibtex
@inproceedings{DanJarGriCouConGro20155,
  author = {Robin Danilo and Homman Jarollahi and
Vincent Gripon and Philippe Coussy and Laura
Conde-Canencia and Warren J. Gross},
  title = {Algorithm and Implementation of an
Associative Memory for Oriented Edge Detection Using
Improved Clustered Neural Networks},
  booktitle = {Proceedings of ISCAS Conference},
  year = {2015},
  pages = {2501--2504},
  month = {May},
}




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