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Segmentación multinivel de imágenes por umbralización basada en un novedoso mecanismo mejorado del algoritmo de

Jiang Liu1, Siyu Yang2, Wencheng Liu2

  • 1Business School, University of Shanghai for Science and Technology, Shanghai, 200093, China. liujiang@usst.edu.cn.

Scientific reports
|February 24, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta ENCOA, un optimizador mejorado para la segmentación de imágenes multinivel por umbralización. ENCOA mejora el equilibrio de búsqueda global y local, evitando la convergencia prematura para una segmentación precisa de imágenes en escala de grises y color en niveles de umbral altos.

Palabras clave:
mecanismo de búsqueda adaptativaalgoritmo de optimización coatisegmentación de imágenes multinivel por umbralizaciónmúltiples estrategiasalgoritmo de optimización de enjambre de salpas

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Área de la Ciencia:

  • Visión por Computadora
  • Inteligencia Artificial
  • Procesamiento de Imágenes

Sus antecedentes:

  • Los algoritmos metaheurísticos destacan en la segmentación de imágenes multinivel por umbralización.
  • Los métodos existentes tienen dificultades para equilibrar la búsqueda global/local, la convergencia prematura y la segmentación multitarea.

Objetivo del estudio:

  • Desarrollar un optimizador mejorado para la segmentación de imágenes multinivel por umbralización.
  • Abordar las limitaciones en el manejo de funciones objetivo múltiples, diversos tipos de imágenes (escala de grises/color) y niveles de umbral altos.
  • Mejorar el equilibrio entre exploración global y explotación local y evitar la convergencia prematura.

Principales métodos:

  • Se propuso un novedoso mecanismo de búsqueda, ASSM (inspirado en el Algoritmo de Optimización de Enjambre de Salpas), para algoritmos DP.
  • Se desarrolló el marco ENsemble Collaborative Optimizer (ENCOA) utilizando Hierarchical Vertical-Horizontal Search (HVHS).
  • Se integraron en ENCOA el mapeo caótico circular mejorado, el aprendizaje basado en la oposición y las estrategias de vuelo de Lévy.

Principales resultados:

  • ENCOA demostró un rendimiento superior en los problemas de referencia CEC2017 y de ingeniería.
  • Aplicado para segmentar imágenes en escala de grises y color utilizando la entropía de Kapur y la varianza de Otsu.
  • Logró una mayor precisión de convergencia y calidad de segmentación, especialmente para niveles de umbral altos (4-32).

Conclusiones:

  • ENCOA supera eficazmente las limitaciones de los algoritmos metaheurísticos existentes para la segmentación de imágenes.
  • El marco propuesto ofrece mejoras significativas en precisión y eficiencia para tareas de segmentación complejas.
  • ENCOA muestra un gran potencial para aplicaciones avanzadas de segmentación de imágenes.