Una Revisión Sistemática de Métodos para Localizar Automáticamente Objetos en Imágenes

Deisy Chaves, Surajit Saikia, Laura Fernández-Robles, Enrique Alegre, Maria Trujillo

Resumen

Actualmente, muchas aplicaciones requieren localizar de forma precisa los objetos que aparecen en una imagen, para su posterior procesamiento. Este es el caso de la inspección visual en la industria, los sistemas de diagnóstico clínico asistido por computador, la detección de obstáculos en vehículos o en robots, entre otros. Sin embargo, diversos factores como la calidad de la imagen y la apariencia de los objetos a detectar, dificultan la localización automática. En este artículo realizamos una revisión sistemática de los principales métodos utilizados para localizar objetos, considerando desde los métodos basados en ventanas deslizantes, como el detector propuesto por Viola y Jones, hasta los métodos actuales que usan redes de aprendizaje profundo, tales como Faster-RCNNo Mask-RCNN. Para cada propuesta, describimos los detalles relevantes, considerando sus ventajas y desventajas, así como sus aplicaciones en diversas áreas. El artículo pretende proporcionar una revisión ordenada y condensada del estado del arte de estas técnicas, su utilidad y sus implementaciones a fin de facilitar su conocimiento y uso por cualquier investigador que requiera localizar objetos en imágenes digitales. Concluimos este trabajo resumiendo las ideas presentadas y discutiendo líneas de trabajo futuro.


Palabras clave

Algoritmos de detección; Aprendizaje máquina; Procesamiento de imágenes; Reconocimiento de objetos; Reconocimiento de patrones

Clasificación por materias

Visión por computador; Inteligencia computacional y técnicas de supervisión y detección de fallos; Filtrado, estimación y análisis y tratamiento de señales e imágenes

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