Research Article Open Access

Range Image Segmentation by Randomized Region Growing and Bayesian Edge Regularization

Smaine Mazouzi and Mohamed Batouche

Abstract

We presented and evaluated a new Bayesian method for range image segmentation. The method proceeds in to stages. First, an initial segmentation was produced by a randomized region growing technique. The produced segmentation was considered as a degraded version of the ideal segmentation, which should be then refined. In the second stage, image pixels not labeled in the first stage were labeled by using a Bayesian estimation, based on some prior assumptions on the regions in the image. Image priors were modeled by a new Markov Random Field (MRF) model. Contrary to most of the authors in range image segmentation, who use only surface smoothness MRF models, our MRF model takes into account also the smoothness of region boundaries. Tests performed with real images from the ABW database show the great potential of the proposed method for significantly improving the segmentation results.

Journal of Computer Science
Volume 3 No. 5, 2007, 310-317

DOI: https://doi.org/10.3844/jcssp.2007.310.317

Submitted On: 28 March 2007 Published On: 31 May 2007

How to Cite: Mazouzi, S. & Batouche, M. (2007). Range Image Segmentation by Randomized Region Growing and Bayesian Edge Regularization . Journal of Computer Science, 3(5), 310-317. https://doi.org/10.3844/jcssp.2007.310.317

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Keywords

  • Image segmentation
  • range image
  • randomized region growing
  • bayesian estimation
  • markov random field