Visual reconstruction
Variational methods in image segmentation
Variational methods in image segmentation
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Regularization, Scale-Space, and Edge Detection Filters
Journal of Mathematical Imaging and Vision
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional
International Journal of Computer Vision
A Variational Framework for Active and Adaptative Segmentation of Vector Valued Images
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Natural Image Statistics for Natural Image Segmentation
International Journal of Computer Vision
Three-dimensional shape knowledge for joint image segmentation and pose estimation
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Brain MR Image Segmentation Using Local and Global Intensity Fitting Active Contours/Surfaces
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
International Journal of Computer Vision
Narrow band region-based active contours and surfaces for 2D and 3D segmentation
Computer Vision and Image Understanding
Mumford-Shah regularizer with contextual feedback
Journal of Mathematical Imaging and Vision
A Local Normal-Based Region Term for Active Contours
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Active contours driven by local Gaussian distribution fitting energy
Signal Processing
An efficient local Chan-Vese model for image segmentation
Pattern Recognition
Contrast Constrained Local Binary Fitting for Image Segmentation
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
The piecewise smooth Mumford-Shah functional on an arbitrary graph
IEEE Transactions on Image Processing
A brain MR image segmentation approach based on local intensity fitting curve evolution
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Integrating local distribution information with level set for boundary extraction
Journal of Visual Communication and Image Representation
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
A novel level set model based on local information
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Level set segmentation based on local gaussian distribution fitting
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
A robust patch-statistical active contour model for image segmentation
Pattern Recognition Letters
TouchCut: Fast image and video segmentation using single-touch interaction
Computer Vision and Image Understanding
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In region-based image segmentation, two models dominate the field: the Mumford-Shah functional and statistical approaches based on Bayesian inference. Whereas the latter allow for numerous ways to describe the statistics of intensities in regions, the first includes spatially smooth approximations. In this paper, we show that the piecewise smooth Mumford-Shah functional is a first order approximation of Bayesian a-posteriori maximization where region statistics are computed in local windows. This equivalence not only allows for a statistical interpretation of the full Mumford-Shah functional. Inspired by the Bayesian model, it also offers to formulate an extended Mumford-Shah functional that takes the variance of the data into account.