Multiresolution-based watersheds for efficient image segmentation

  • Authors:
  • Jong-Bae Kim;Hang-Joon Kim

  • Affiliations:
  • Artificial Intelligence (AI) Laboratory, Department of Computer Engineering, Kyungpook National University, 1370 Sangyuk-dong, Puk-gu, Taegu 702-701, South Korea;Artificial Intelligence (AI) Laboratory, Department of Computer Engineering, Kyungpook National University, 1370 Sangyuk-dong, Puk-gu, Taegu 702-701, South Korea

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2003

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Abstract

This paper presents an efficient method for image segmentation based on a multiresolution application of a wavelet transform and watershed segmentation algorithm. The procedure toward complete segmentation consists of four steps: pyramid representation, image segmentation, region merging and region projection. First, pyramid representation creates multiresolution images using a wavelet transform. Second, image segmentation segments the lowest-resolution image of the pyramid using a watershed segmentation algorithm. Third, region merging merges the segmented regions using the third-order moment values of the wavelet coefficients. Finally, the segmented low-resolution image with label is projected into a full-resolution image (original image) by inverse wavelet transform. Experimental results of the presented method can be applied to the segmentation of noise or degraded images as well as reduce over-segmentation. In addition, we applied our method to human face detection with accurate and closed boundaries.