Proceedings ArticleDOI
Diffusion-snakes: combining statistical shape knowledge and image information in a variational framework
Daniel Cremers,Christoph Schnörr,Joachim Weickert +2 more
- pp 137-144
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TLDR
In this article, a modification of the Mumford-Shah functional and its cartoon limit is presented, which allows the incorporation of statistical shape knowledge in a single energy functional for image segmentation.Abstract:
We present a modification of the Mumford-Shah functional and its cartoon limit which allows the incorporation of statistical shape knowledge in a single energy functional. We show segmentation results on artificial and real-world images with and without prior shape information. In the case of occlusion and strongly cluttered background the shape prior significantly improves segmentation. Finally we compare our results to those obtained by a level-set implementation of geodesic active contours.read more
Citations
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Book ChapterDOI
Shape Priors for Level Set Representations
Mikael Rousson,Nikos Paragios +1 more
TL;DR: This paper proposes a novel energetic form to introduce shape constraints to level set representations and exploits all advantages of these representations resulting on a very elegant approach that can deal with a large number of parametric as well as continuous transformations.
Journal ArticleDOI
Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration
TL;DR: This paper shows how the concept of statistical deformation models (SDMs) can be used for the construction of average models of the anatomy and their variability and demonstrates that SDMs can be constructed so as to minimize the bias toward the chosen reference subject.
Journal ArticleDOI
Using Prior Shapes in Geometric Active Contours in a Variational Framework
Yunmei Chen,Hemant D. Tagare,Sheshadri Thiruvenkadam,Feng Huang,David C. Wilson,Kaundinya S. Gopinath,Richard W. Briggs,Edward A. Geiser +7 more
TL;DR: An active contour algorithm that is capable of using prior shapes is reported that is able to find boundaries that are similar in shape to the prior, even when the entire boundary is not visible in the image.
Journal ArticleDOI
Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional
TL;DR: A modification of the Mumford-Shah functional and its cartoon limit is presented which facilitates the incorporation of a statistical prior on the shape of the segmenting contour and a closed-form, parameter-free solution for incorporating invariance with respect to similarity transformations in the variational framework is proposed.
Book ChapterDOI
Object detection by contour segment networks
TL;DR: An extensive experimental evaluation on detecting five diverse object classes over hundreds of images demonstrates that the proposed method works in very cluttered images, allows for scale changes and considerable intra-class shape variation, is robust to interrupted contours, and is computationally efficient.
References
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Journal ArticleDOI
Snakes : Active Contour Models
TL;DR: This work uses snakes for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest, and uses scale-space continuation to enlarge the capture region surrounding a feature.
Journal ArticleDOI
Active shape models—their training and application
TL;DR: This work describes a method for building models by learning patterns of variability from a training set of correctly annotated images that can be used for image search in an iterative refinement algorithm analogous to that employed by Active Contour Models (Snakes).
Journal ArticleDOI
Geodesic active contours
TL;DR: A novel scheme for the detection of object boundaries based on active contours evolving in time according to intrinsic geometric measures of the image, allowing stable boundary detection when their gradients suffer from large variations, including gaps.
Journal ArticleDOI
Optimal approximations by piecewise smooth functions and associated variational problems
David Mumford,Jayant Shah +1 more
TL;DR: In this article, the authors introduce and study the most basic properties of three new variational problems which are suggested by applications to computer vision, and study their application in computer vision.
Journal ArticleDOI
Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation
Song-Chun Zhu,Alan L. Yuille +1 more
TL;DR: A novel statistical and variational approach to image segmentation based on a new algorithm, named region competition, derived by minimizing a generalized Bayes/minimum description length (MDL) criterion using the variational principle is presented.