New Model of Heteroasociative Min Memory Robust to Acquisition Noise

Julio César Salgado-Ramírez, Jean Marie Vianney Kinani, Eduardo Antonio Cendejas-Castro, Alberto Jorge Rosales-Silva, Eduardo Ramos-Díaz and Juan Luis Díaz- de-Léon-Santiago
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Julio César Salgado-Ramírez: Ingeniería Biomédica, Universidad Politécnica de Pachuca (UPP), Zempoala 43830, Mexico
Jean Marie Vianney Kinani: Ingeniería Mecatrónica, Instituto Politécnico Nacional-UPIIH, Pachuca 07738, Mexico
Eduardo Antonio Cendejas-Castro: Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Pachuca 42083, Mexico
Alberto Jorge Rosales-Silva: Sección de Estudios de Posgrado e Investigación, Instituto Politécnico Nacional-ESIME Zacatenco, Mexico City 07738, Mexico
Eduardo Ramos-Díaz: Ingeniería en Sistemas Electrónicos y de Telecomunicaciones, Universidad Autónoma de la Ciudad de México, Mexico City 09790, Mexico
Juan Luis Díaz- de-Léon-Santiago: Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City 07700, Mexico

Mathematics, 2022, vol. 10, issue 1, 1-35

Abstract: Associative memories in min and max algebra are of great interest for pattern recognition. One property of these is that they are one-shot, that is, in an attempt they converge to the solution without having to iterate. These memories have proven to be very efficient, but they manifest some weakness with mixed noise. If an appropriate kernel is not used, that is, a subset of the pattern to be recalled that is not affected by noise, memories fail noticeably. A possible problem for building kernels with sufficient conditions, using binary and gray-scale images, is not knowing how the noise is registered in these images. A solution to this problem is presented by analyzing the behavior of the acquisition noise. What is new about this analysis is that, noise can be mapped to a distance obtained by a distance transform. Furthermore, this analysis provides the basis for a new model of min heteroassociative memory that is robust to the acquisition/mixed noise. The proposed model is novel because min associative memories are typically inoperative to mixed noise. The new model of heteroassocitative memory obtains very interesting results with this type of noise.

Keywords: associative memories; noise; kernel; Fast Distance Transform (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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