Clasificación de imágenes invariantes a la rotación utilizando una novedosa CNN en 1D y Momentos exactos de Bessel-Fourier
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https://doi.org/10.29057/icbi.v10iEspecial3.8874Palabras clave:
Momentos de Bessel-Fourier, aprendizaje profundo, morfologías de galaxias, características de invariante rotaciónResumen
Este trabajo presenta una propuesta para utilizar momentos de Bessel-Fourier como entradas a redes neuronales convolucionales 1D de tal manera que aprovechen las características inherentes de los descriptores de tipo de momento, como la invariancia rotacional y la mínima redundancia de información. Los resultados presentados muestran que la propuesta tiene un mejor desempeño que la red neuronal profunda con invariancia de rotacion.
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