Color image segmentation based on adaptive local thresholds

Ety Navon, Ofer Miller, Amir Averbuch

Research output: Contribution to journalArticlepeer-review

Abstract

The goal of still color image segmentation is to divide the image into homogeneous regions. Object extraction, object recognition and object-based compression are typical applications that use still segmentation as a low-level image processing. In this paper, we present a new method for color image segmentation. The proposed algorithm divides the image into homogeneous regions by local thresholds. The number of thresholds and their values are adaptively derived by an automatic process, where local information is taken into consideration. First, the watershed algorithm is applied. Its results are used as an initialization for the next step, which is iterative merging process. During the iterative process, regions are merged and local thresholds are derived. The thresholds are determined one-by-one at different times during the merging process. Every threshold is calculated by local information on any region and its surroundings. Any statistical information on the input images is not given. The algorithm is found to be reliable and robust to different kind of images.

Original languageEnglish
Pages (from-to)69-85
Number of pages17
JournalImage and Vision Computing
Volume23
Issue number1
DOIs
StatePublished - 1 Jan 2005

Keywords

  • Homogeneity
  • Image segmentation
  • Local thresholds
  • Merging
  • Splitting

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