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A Tree of Shapes for Multivariate Images

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Abstract

Nowadays, the demand for multi-scale and region-based analysis in many computer vision and pattern recognition applications is obvious. No one would consider a pixelbased approach as a good candidate to solve such problems. To meet this need, the Mathematical Morphology (MM) framework has supplied region-based hierarchical representations of images such as the Tree of Shapes (ToS). The ToS represents the image in terms of a tree of the inclusion of its level-lines. The ToS is thus self-dual and contrastchange invariant which make it well-adapted for high-level image processing. Yet, it is only defined on grayscale images and most attempts to extend it on multivariate images - e.g. by imposing an ``arbitrary total ordering - are not satisfactory. In this dissertation, we present the Multivariate Tree of Shapes (MToS) as a novel approach to extend the grayscale ToS on multivariate images. This representation is a mix of the ToS's computed marginally on each channel of the image; it aims at merging the marginal shapes in a ``sensible way by preserving the maximum number of inclusion. The method proposed has theoretical foundations expressing the ToS in terms of a topographic map of the curvilinear total variation computed from the image border; which has allowed its extension on multivariate data. In addition, the MToS features similar properties as the grayscale ToS, the most important one being its invariance to any marginal change of contrast and any marginal inversion of contrast (a somewhat ``self-duality in the multidimensional case). As the need for efficient image processing techniques is obvious regarding the larger and larger amount of data to processwe propose an efficient algorithm that can build the MToS in quasi-linear time w.r.t. the number of pixels and quadratic w.r.t. the number of channels. We also propose tree-based processing algorithms to demonstrate in practice, that the MToS is a versatile, easy-to-use, and efficient structure. Eventually, to validate the soundness of our approach, we propose some experiments testing the robustness of the structure to non-relevant components (e.g. with noise or with low dynamics) and we show that such defaults do not affect the overall structure of the MToS. In addition, we propose many real-case applications using the MToS. Many of them are just a slight modification of methods employing the ``regular ToS and adapted to our new structure. For example, we successfully use the MToS for image filtering, image simplification, image segmentation, image classification and object detection. From these applications, we show that the MToS generally outperforms its ToS-based counterpart, demonstrating the potential of our approach.

Documents

Bibtex (lrde.bib)

@PhDThesis{	  carlinet.15.phd,
  author	= {Edwin Carlinet},
  title		= {A Tree of Shapes for Multivariate Images},
  school	= {Universit\'e Paris Est},
  year		= 2015,
  address	= {Paris, France},
  month		= nov,
  abstract	= {Nowadays, the demand for multi-scale and region-based
		  analysis in many computer vision and pattern recognition
		  applications is obvious. No one would consider a pixelbased
		  approach as a good candidate to solve such problems. To
		  meet this need, the Mathematical Morphology (MM) framework
		  has supplied region-based hierarchical representations of
		  images such as the Tree of Shapes (ToS). The ToS represents
		  the image in terms of a tree of the inclusion of its
		  level-lines. The ToS is thus self-dual and contrastchange
		  invariant which make it well-adapted for high-level image
		  processing. Yet, it is only defined on grayscale images and
		  most attempts to extend it on multivariate images - e.g. by
		  imposing an ``arbitrary'' total ordering - are not
		  satisfactory. In this dissertation, we present the
		  Multivariate Tree of Shapes (MToS) as a novel approach to
		  extend the grayscale ToS on multivariate images. This
		  representation is a mix of the ToS's computed marginally on
		  each channel of the image; it aims at merging the marginal
		  shapes in a ``sensible'' way by preserving the maximum
		  number of inclusion. The method proposed has theoretical
		  foundations expressing the ToS in terms of a topographic
		  map of the curvilinear total variation computed from the
		  image border; which has allowed its extension on
		  multivariate data. In addition, the MToS features similar
		  properties as the grayscale ToS, the most important one
		  being its invariance to any marginal change of contrast and
		  any marginal inversion of contrast (a somewhat
		  ``self-duality'' in the multidimensional case). As the need
		  for efficient image processing techniques is obvious
		  regarding the larger and larger amount of data to process,
		  we propose an efficient algorithm that can build the MToS
		  in quasi-linear time w.r.t. the number of pixels and
		  quadratic w.r.t. the number of channels. We also propose
		  tree-based processing algorithms to demonstrate in
		  practice, that the MToS is a versatile, easy-to-use, and
		  efficient structure. Eventually, to validate the soundness
		  of our approach, we propose some experiments testing the
		  robustness of the structure to non-relevant components
		  (e.g. with noise or with low dynamics) and we show that
		  such defaults do not affect the overall structure of the
		  MToS. In addition, we propose many real-case applications
		  using the MToS. Many of them are just a slight modification
		  of methods employing the ``regular'' ToS and adapted to our
		  new structure. For example, we successfully use the MToS
		  for image filtering, image simplification, image
		  segmentation, image classification and object detection.
		  From these applications, we show that the MToS generally
		  outperforms its ToS-based counterpart, demonstrating the
		  potential of our approach.}
}