Color Adaptive Neighborhood Mathematical Morphology and its application to pixel-level classification

Victor Gonzalez-Castro*, Johan Debayle, Jean-Charles Pinoli

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper spatially adaptive Mathematical Morphology (MM) is studied for color images. More precisely, the General Adaptive Neighborhood Image Processing (GANIP) approach is generalized to color images. The basic principle is to define a set of locally Color Adaptive Neighborhoods (CAN), one for each point of the image, and to use them as adaptive structuring elements (ASE) for morphological operations. These operators have been applied to images in different color spaces and compared with other kinds of ASEs extended to color images. Results show that the proposed method is more respectful with the borders of the objects, as well as with the color transitions within the image. Finally, the proposed adaptive morphological operators are applied to the classification of color texture images. (C) 2014 Elsevier B. V. All rights reserved.

Original languageEnglish
Pages (from-to)50-62
Number of pages13
JournalPattern Recognition Letters
Volume47
DOIs
Publication statusPublished - 1 Oct 2014

Keywords

  • Mathematical Morphology
  • Color Adaptive Neighborhoods
  • Color spaces
  • Classification
  • Neural Networks
  • THEORETICAL FOUNDATIONS
  • TEXTURE CLASSIFICATION
  • STRUCTURING ELEMENTS
  • PART-II
  • IMAGES
  • SEGMENTATION
  • OPERATORS
  • SPACES

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