Unsupervised Texture Segmentation Using Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
A level set approach for computing solutions to incompressible two-phase flow
Journal of Computational Physics
A variational level set approach to multiphase motion
Journal of Computational Physics
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Region Snake-Based Segmentation Adapted to Different Physical Noise Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Level Set Model for Image Classification
International Journal of Computer Vision
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Oscillating Patterns in Image Processing and Nonlinear Evolution Equations: The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional
International Journal of Computer Vision
International Journal of Computer Vision
Modeling Textures with Total Variation Minimization and Oscillating Patterns in Image Processing
Journal of Scientific Computing
Texture Segmentation by Multiscale Aggregation of Filter Responses and Shape Elements
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
Dual Norms and Image Decomposition Models
International Journal of Computer Vision
Image Decomposition into a Bounded Variation Component and an Oscillating Component
Journal of Mathematical Imaging and Vision
A logic framework for active contours on multi-channel images
Journal of Visual Communication and Image Representation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Simultaneous structure and texture image inpainting
IEEE Transactions on Image Processing
Wavelet-based level set evolution for classification of textured images
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
In this paper, we propose a framework to carry out supervised classification of images containing both textured and non textured areas. Our approach is based on active contours. Using a decomposition algorithm inspired by the recent work of Y. Meyer, we can get two channels from the original image to classify: one containing the geometrical information, and the other the texture. Using the logic framework by Chan and Sandberg, we can then combine the information from both channels in a user definable way. Thus, we design a classification algorithm in which the different classes are characterized both from geometrical and textured features. Moreover, the user can choose different ways to combine information.