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The Contracting Curve Density Algorithm: Fitting Parametric Curve Models to Images Using Local Self-adapting Separation Criteria (bibtex)
@ARTICLE{hanek_contracting_2004,
  author = {Hanek, Robert and Beetz, Michael},
  title = {The Contracting Curve Density Algorithm: Fitting Parametric Curve
	Models to Images Using Local Self-adapting Separation Criteria},
  journal = {International Journal of Computer Vision},
  year = {2004},
  volume = {59},
  pages = {233--258},
  number = {3},
  abstract = {The task of fitting parametric curve models to the boundaries of perceptually
	meaningful image regions is a key problem in computer vision with
	numerous applications, such as image segmentation, pose estimation,
	object tracking, and 3-D reconstruction. In this article, we propose
	the Contracting Curve Density ({CCD)} algorithm as a solution to
	the curve-fitting problem. The {CCD} algorithm extends the state-of-the-art
	in two important ways. First, it applies a novel likelihood function
	for the assessment of a fit between the curve model and the image
	data. This likelihood function can cope with highly inhomogeneous
	image regions, because it is formulated in terms of local image statistics.
	The local image statistics are learned on the fly from the vicinity
	of the expected curve. They provide therefore locally adapted criteria
	for separating the adjacent image regions. These local criteria replace
	often used predefined fixed criteria that rely on homogeneous image
	regions or specific edge properties. The second contribution is the
	use of blurred curve models as efficient means for iteratively optimizing
	the posterior density over possible model parameters. These blurred
	curve models enable the algorithm to trade-off two conflicting objectives,
	namely heaving a large area of convergence and achieving high accuracy.
	We apply the {CCD} algorithm to several challenging image segmentation
	and 3-D pose estimation problems. Our experiments with {RGB} images
	show that the {CCD} algorithm achieves a high level of robustness
	and subpixel accuracy even in the presence of severe texture, shading,
	clutter, partial occlusion, and strong changes of illumination.}
}
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Last edited 29.01.2013 17:37 by Quirin Lohr