Publications/xu.13.phd

Authors

Yongchao Xu

Place

Marne-la-Vallée, France

Type

phdthesis

Date

2014-03-17

Abstract

The classical framework of connected filters relies on the removal of some connected components of a graph. To apply those filters, it is often useful to transform an image into a component tree, and to prune the tree to simplify the original image. Those trees have some remarkable properties for computer vision. A first illustration of their usefulness is the proposition of a local feature detector, truly invariant to change of contrast. which allows us to obtain the state-of-the-art results in image registration and in multi-view 3D reconstruction. Going further in the use of those trees, we propose to expand the classical framework of connected filters. For this, we introduce the notion of tree-based shape spaces: instead of filtering the connected components of the graph corresponding to the image, we propose to filter the connected components of the graph given by the component tree of the image. This general framework, which we call shape-based morphology can be used for object detection and segmentation, hierarchical segmentation, and image filtering. Many applications and illustrations show the usefulness of the proposed framework.

Documents

• Paper

Bibtex (lrde.bib)

@PhDThesis{	  xu.13.phd,
  author	= {Yongchao Xu},
  title		= {Tree-based shape spaces: Definition and applications in
		  image processing and computer vision},
  school	= {Universit\'e Paris-Est},
  year		= 2013,
  address	= {Marne-la-Vall\'ee, France},
  month		= dec,
  abstract	= {The classical framework of connected filters relies on the
		  removal of some connected components of a graph. To apply
		  those filters, it is often useful to transform an image
		  into a component tree, and to prune the tree to simplify
		  the original image. Those trees have some remarkable
		  properties for computer vision. A first illustration of
		  their usefulness is the proposition of a local feature
		  detector, truly invariant to change of contrast. which
		  allows us to obtain the state-of-the-art results in image
		  registration and in multi-view 3D reconstruction. Going
		  further in the use of those trees, we propose to expand the
		  classical framework of connected filters. For this, we
		  introduce the notion of tree-based shape spaces: instead of
		  filtering the connected components of the graph
		  corresponding to the image, we propose to filter the
		  connected components of the graph given by the component
		  tree of the image. This general framework, which we call
		  shape-based morphology can be used for object detection and
		  segmentation, hierarchical segmentation, and image
		  filtering. Many applications and illustrations show the
		  usefulness of the proposed framework.}
}