Free-form deformation of solid geometric models
SIGGRAPH '86 Proceedings of the 13th annual conference on Computer graphics and interactive techniques
Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Matching Distance Functions: A Shape-to-Area Variational Approach for Global-to-Local Registration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Uncertainty Modeling and Model Selection for Geometric Inference
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion-based background subtraction using adaptive kernel density estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A Novel Algorithm for Automatic Brain Structure Segmentation from MRI
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Confidence of model based shape reconstruction from sparse data
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Hi-index | 0.00 |
In this paper we propose a novel variational technique for the knowledge based segmentation of two dimensional objects. One of the elements of our approach is the use of higher order implicit polynomials to represent shapes. The most important contribution is the estimation of uncertainties on the registered shapes, which can be used with a variable bandwidth kernel-based non-parametric density estimation process to model prior knowledge about the object of interest. Such a non-linear model with uncertainty measures is integrated with an adaptive visual-driven data term that aims to separate the object of interest from the background. Promising results obtained for the segmentation of the corpus callosum in MR mid-sagittal brain slices demonstrate the potential of such a framework.