Computing the 2-D image euler number by an Artificial Neural Network

Humberto Sossa, Ángel Carreón, Elizabeth Guevara, Raúl Santiago

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

4 Citas (Scopus)

Resumen

We describe for the first time how the Euler number of a 2-D binary image can be obtained by means of Artificial Neural Network (ANN). Calculating the Euler image number is treated as a pattern classification problem. To arrive at the specialized ANN architecture, we perform a partial results analysis provided by a known formulation to compute the Euler image number. We use this analysis for designing the desired ANN architecture. Due to its good functioning characteristics, outcomes with the so-called Morphological Neural Perceptron with Dendritic Processing (MNPDP) are presented. Numerical as well as experimental results with realistic images to demonstrate the operation and applicability of the proposed approach are reported. Initial results concerning the GPU implementation of the proposed ANN to show that the processing time can be effectively reduced are also provided.

Idioma originalInglés
Título de la publicación alojada2016 International Joint Conference on Neural Networks, IJCNN 2016
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas1609-1616
Número de páginas8
ISBN (versión digital)9781509006199
DOI
EstadoPublicada - 31 oct. 2016
Evento2016 International Joint Conference on Neural Networks, IJCNN 2016 - Vancouver, Canadá
Duración: 24 jul. 201629 jul. 2016

Serie de la publicación

NombreProceedings of the International Joint Conference on Neural Networks
Volumen2016-October

Conferencia

Conferencia2016 International Joint Conference on Neural Networks, IJCNN 2016
País/TerritorioCanadá
CiudadVancouver
Período24/07/1629/07/16

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