New Model of Heteroasociative Min Memory Robust to Acquisition Noise
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...
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MDPI AG
2022-01-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/1/148 |
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| author | Julio César Salgado-Ramírez Jean Marie Vianney Kinani Eduardo Antonio Cendejas-Castro Alberto Jorge Rosales-Silva Eduardo Ramos-Díaz Juan Luis Díaz-de-Léon-Santiago |
| author_facet | Julio César Salgado-Ramírez Jean Marie Vianney Kinani Eduardo Antonio Cendejas-Castro Alberto Jorge Rosales-Silva Eduardo Ramos-Díaz Juan Luis Díaz-de-Léon-Santiago |
| author_sort | Julio César Salgado-Ramírez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:32:22Z |
| format | Article |
| id | doaj.art-f48fd98d1f9c4888ade4c51e5f40d602 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T03:32:22Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-f48fd98d1f9c4888ade4c51e5f40d6022023-11-23T11:54:52ZengMDPI AGMathematics2227-73902022-01-0110114810.3390/math10010148New Model of Heteroasociative Min Memory Robust to Acquisition NoiseJulio César Salgado-Ramírez0Jean Marie Vianney Kinani1Eduardo Antonio Cendejas-Castro2Alberto Jorge Rosales-Silva3Eduardo Ramos-Díaz4Juan Luis Díaz-de-Léon-Santiago5Ingeniería Biomédica, Universidad Politécnica de Pachuca (UPP), Zempoala 43830, MexicoIngeniería Mecatrónica, Instituto Politécnico Nacional-UPIIH, Pachuca 07738, MexicoEscuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Pachuca 42083, MexicoSección de Estudios de Posgrado e Investigación, Instituto Politécnico Nacional-ESIME Zacatenco, Mexico City 07738, MexicoIngeniería en Sistemas Electrónicos y de Telecomunicaciones, Universidad Autónoma de la Ciudad de México, Mexico City 09790, MexicoCentro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City 07700, MexicoAssociative 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.https://www.mdpi.com/2227-7390/10/1/148associative memoriesnoisekernelFast Distance Transform |
| spellingShingle | Julio César Salgado-Ramírez Jean Marie Vianney Kinani Eduardo Antonio Cendejas-Castro Alberto Jorge Rosales-Silva Eduardo Ramos-Díaz Juan Luis Díaz-de-Léon-Santiago New Model of Heteroasociative Min Memory Robust to Acquisition Noise Mathematics associative memories noise kernel Fast Distance Transform |
| title | New Model of Heteroasociative Min Memory Robust to Acquisition Noise |
| title_full | New Model of Heteroasociative Min Memory Robust to Acquisition Noise |
| title_fullStr | New Model of Heteroasociative Min Memory Robust to Acquisition Noise |
| title_full_unstemmed | New Model of Heteroasociative Min Memory Robust to Acquisition Noise |
| title_short | New Model of Heteroasociative Min Memory Robust to Acquisition Noise |
| title_sort | new model of heteroasociative min memory robust to acquisition noise |
| topic | associative memories noise kernel Fast Distance Transform |
| url | https://www.mdpi.com/2227-7390/10/1/148 |
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