TY - JOUR
T1 - New Model of Heteroasociative min Memory Robust to Acquisition Noise
AU - Salgado-Ramírez, Julio César
AU - Vianney Kinani, Jean Marie
AU - Cendejas-Castro, Eduardo Antonio
AU - Rosales-Silva, Alberto Jorge
AU - Ramos-Díaz, Eduardo
AU - Díaz-De-léon-santiago, Juan Luis
N1 - Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - 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.
AB - 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.
KW - Associative memories
KW - Fast Distance Transform
KW - Kernel
KW - Noise
UR - http://www.scopus.com/inward/record.url?scp=85122180519&partnerID=8YFLogxK
U2 - 10.3390/math10010148
DO - 10.3390/math10010148
M3 - Artículo
AN - SCOPUS:85122180519
SN - 2227-7390
VL - 10
JO - Mathematics
JF - Mathematics
IS - 1
M1 - 148
ER -