Segmentación rápida de imágenes con múltiples características
DOI:
https://doi.org/10.32870/recibe.v13i1.358Palabras clave:
Segmentación de imagen, Algoritmo de cambio medio, Estimador de densidad del kernel (EDK)Resumen
La segmentación de características múltiples es superior a los enfoques unidimensionales en escalas de grises. El algoritmo del cambio medio (CM) se utiliza comúnmente para esta tarea. A pesar de sus interesantes resultados, el CM sigue siendo computacionalmente prohibitivo para escenarios de segmentación en los que el mapa de funciones está formado por características multidimensionales. El enfoque propuesto considera un mapa de características bidimensional que incluye el valor en escala de grises y la varianza local de cada píxel de la imagen. Para reducir el coste computacional, se modifica el algoritmo de CM clásico para que opere sobre un número menor de puntos. En tales condiciones, se diferencian dos conjuntos de elementos: datos implicados (el conjunto reducido de datos considerados en la operación CM) y datos no implicados (el resto de datos disponibles). A diferencia del CM clásico, que emplea funciones gaussianas, en nuestro enfoque el proceso de estimación del mapa de características se lleva a cabo utilizando un enfoque más preciso, como la función kernel de Epanechnikov. Una vez obtenidos los resultados del CM, se generalizan para incluir los datos no utilizados. Así, cada característica no utilizada se asigna al mismo clúster de datos utilizados más cercano. Por último, los grupos con menos características se fusionan con otros grupos vecinos. El método de segmentación propuesto se ha comparado con otros algoritmos del estado de la técnica usando de la base de datos de Berkeley. Los resultados experimentales confirman que el esquema propuesto produce imágenes segmentadas con un 50% más de calidad de percepción visual.Citas
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