IEEE Transactions on Pattern Analysis and Machine Intelligence
Multichannel Texture Analysis Using Localized Spatial Filters
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
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Handbook of pattern recognition & computer vision
Oscillating Patterns in Image Processing and Nonlinear Evolution Equations: The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures
Modeling Textures with Total Variation Minimization and Oscillating Patterns in Image Processing
Journal of Scientific Computing
Image Denoising and Decomposition with Total Variation Minimization and Oscillatory Functions
Journal of Mathematical Imaging and Vision
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
Image Decomposition into a Bounded Variation Component and an Oscillating Component
Journal of Mathematical Imaging and Vision
Smooth minimization of non-smooth functions
Mathematical Programming: Series A and B
Image decomposition combining staircase reduction and texture extraction
Journal of Visual Communication and Image Representation
A multiscale approach to texture-based image retrieval
Pattern Analysis & Applications
Efficient Schemes for Total Variation Minimization Under Constraints in Image Processing
SIAM Journal on Scientific Computing
Image decomposition application to SAR images
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Texture representation and retrieval using the causal autoregressive model
Journal of Visual Communication and Image Representation
Fast cartoon + texture image filters
IEEE Transactions on Image Processing
A novel approach for multi-scale texture segmentation based on fractional differential
International Journal of Computer Mathematics
Integro-Differential Equations Based on $(BV, L^1)$ Image Decomposition
SIAM Journal on Imaging Sciences
High resolution spectral analysis of images using the pseudo-Wignerdistribution
IEEE Transactions on Signal Processing
Fractal-Based Description of Natural Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture classification using spectral histograms
IEEE Transactions on Image Processing
Image decomposition via the combination of sparse representations and a variational approach
IEEE Transactions on Image Processing
Adaptive scale fixing for multiscale texture segmentation
IEEE Transactions on Image Processing
Texture segmentation using Gaussian-Markov random fields and neural oscillator networks
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
In this paper, we first present a hierarchical (BV,G p ,L 2) variational decomposition model and then use it to achieve multiscale texture extraction which offers a hierarchical, separated representation of image texture in different scales. The starting point is the use of the variational (BV,G p ,L 2) decomposition; a given image f驴L 2(驴) is decomposed into a sum of u 0+v 0+r 0, where (u 0,v 0)驴(BV(驴),G p (驴)) is the minimizer of an energy functional E(f,驴 0;u,v) and r 0 is the residual (i.e. r 0=f驴u 0驴v 0). In this decomposition, v 0 represents the fixed scale texture of f, which is measured by the parameter 驴 0. To achieve a multiscale representation, we proceed to capture essential textures of f which have been absorbed by the residuals. Such a goal can be achieved by iterating a refinement decomposition to the residual of the previous step, i.e. r i =u i+1+v i+1+r i+1, where (u i+1,v i+1) is the minimizer of E(r i ,驴 0/2 i+1;u,v). In this manner, we can obtain a hierarchical representation of f. In addition, we discuss some theoretical properties of the hierarchical (BV,G p ,L 2) decomposition and give its numerical implementation. Finally, we apply this hierarchical decomposition to the multiscale texture extraction. The performance of this method is demonstrated with both synthetic and real images.