by R Hanek and M Beetz
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.
Reference:
The Contracting Curve Density Algorithm: Fitting Parametric Curve Models to Images Using Local Self-adapting Separation Criteria (R Hanek and M Beetz), In International Journal of Computer Vision, volume 59, 2004.
Bibtex Entry:
@article{hanek_contracting_2004,
author = {R Hanek and M Beetz},
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.},
}