A multiresolution flow-based multiphase image segmentation

Authors:
C. A. Z. Barcelos;E. Z. Barcelos;J. A. Cuminato
Affiliations:
Department of Mathematics, Dept. of Electrical Engineering, Federal University of Uberlândia, Uberlândia, MG, Brazil;Department of Mathematics, Dept. of Electrical Engineering, Federal University of Uberlândia, Uberlândia, MG, Brazil;Department of Computer Science and Applied Mathematics, University of São Paulo, ICMC, São Carlos, Brazil
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

Citing 13
Cited 0

Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations

Journal of Computational Physics
Nonlinear total variation based noise removal algorithms

Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation

International Journal of Computer Vision
DREAM2S: Deformable Regions Driven by an Eulerian Accurate Minimization Method for Image and Video Segmentation

International Journal of Computer Vision
Nonlinear Shape Statistics in Mumford-Shah Based Segmentation

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape

International Journal of Computer Vision
Towards recognition-based variational segmentation using shape priors and dynamic labeling

Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Fuzzy region competition: a convex two-phase segmentation framework

SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
A probabilistic multi-phase model for variational image segmentation

DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Soft Color Segmentation and Its Applications

IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
A nonparametric statistical method for image segmentation using information theory and curve evolution

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work a variational model is proposed for simultaneous smoothing and multiphase image segmentation. By assuming that the pixel intensities are independent samples from a mixture of Gaussians, and by interpreting the phase fields as probabilities of pixels belonging to a certain phase, the model formulation is obtained by maximizing the mutual information between image features and phase fields. The proposed energy functional J∈ consists of three parts: the smoothing term for the reconstructed image, the regularization for the boundaries in hard segmentation, and a likelihood estimator based on the density function. The segmentation and image denoising are performed simultaneously through the flow equation obtained by minimizing the energy functional with respect to the mixture of Gaussian coefficients and variance. Some experimental results on segmenting synthetic and natural color images are presented to illustrate the effectiveness of the proposed model.