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Reducing overlapped pixels: a multi-objective color thresholding approach

  • Salvador HinojosaEmail author
  • Diego Oliva
  • Erik Cuevas
  • Gonzalo Pajares
  • Daniel Zaldivar
  • Marco Pérez-Cisneros
Methodologies and Application
  • 49 Downloads

Abstract

This paper proposes a general multi-objective thresholding segmentation methodology for color images and a quality metric designed to prevent and quantify the overlapping effect of segmented images. Multi-level thresholding (MTH) has been used to segment color images in recent years; this process considers each channel as a single grayscale image and applies the MTH independently. Although this method provides competitive results, the inherent relationship among color channels is disregarded. Such approaches generate spurious classes on overlapping regions, where new colors are generated, especially on the borders of the objects. The proposed multi-objective color thresholding (MOCTH) approach performs image segmentation while preserving the relationship between image channels. MOCTH is aimed to reduce the overlapping effect on segmented color images without performing additional post-processing. To measure the overlapping classes on a thresholded color image, the overlapping index is proposed to quantify the pixels affected. The presented approach is analyzed on two color spaces (RGB and CIE L*a*b*) using three multi-objective algorithms; they are NSGA-III, SPEA-2, and MOPSO. Results provide evidence pointing out to a better segmentation from MOCTH over the traditional single-objective approaches while reducing overlapped areas on the image.

Keywords

Multi-level thresholding Evolutionary algorithms Multi-objective optimization Overlapping Index 

Notes

Acknowledgements

The first author acknowledges The National Council of Science and Technology of Mexico (CONACyT) for the doctoral Grant Number 298285 for supporting this research.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Dpto. Ingeniería del Software e Inteligencia Artificial, Facultad InformáticaUniversidad Complutense de MadridMadridSpain
  2. 2.Departamento de Electrónica, CUCEIUniversidad de GuadalajaraGuadalajaraMexico

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