Morphological Object Picking Based on the Color Tree of Shapes

From LRDE

Abstract

The Tree of Shapes is a self-dual and contrast invariant morphological tree that provides a high-level hierarchical representation of images, suitable for many image processing tasks. Despite its powerfulness and its simplicity, it is still under-exploited in pattern recognition and computer vision. In this paper, we show that both interactive and automatic image segmentation can be achieved with some simple tree processings. To that aimwe rely on the “Color Tree of Shapes”, recently defined. We propose a method for interactive segmentation that does not involve any statistical learning, yet yielding results that compete with state-of-the-art approaches. We further extend this algorithm to unsupervised segmentation and give some results. Although they are preliminary, they highlight the potential of such an approach that works in the shape space.

Documents

Bibtex (lrde.bib)

@InProceedings{	  carlinet.15.ipta,
  author	= {Edwin Carlinet and Thierry G\'eraud},
  title		= {Morphological Object Picking Based on the Color Tree of
		  Shapes},
  booktitle	= {Proceedings of 5th International Conference on Image
		  Processing Theory, Tools and Applications (IPTA'15)},
  year		= {2015},
  address	= {Orl{\'e}ans, France},
  pages		= {125--130},
  month		= nov,
  abstract	= {The Tree of Shapes is a self-dual and contrast invariant
		  morphological tree that provides a high-level hierarchical
		  representation of images, suitable for many image
		  processing tasks. Despite its powerfulness and its
		  simplicity, it is still under-exploited in pattern
		  recognition and computer vision. In this paper, we show
		  that both interactive and automatic image segmentation can
		  be achieved with some simple tree processings. To that aim,
		  we rely on the ``Color Tree of Shapes'', recently defined.
		  We propose a method for interactive segmentation that does
		  not involve any statistical learning, yet yielding results
		  that compete with state-of-the-art approaches. We further
		  extend this algorithm to unsupervised segmentation and give
		  some results. Although they are preliminary, they highlight
		  the potential of such an approach that works in the shape
		  space.}
}