Segmentation of Color Textures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
An MRF-Based Approach to Generation of Super-Resolution Images from Blurred Observations
Journal of Mathematical Imaging and Vision
The application of Markov random field models to wavelet-based image denoising
Imaging and vision systems
Combining Belief Networks and Neural Networks for Scene Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Class of Discrete Multiresolution Random Fields and Its Application to Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Supervised Texture Segmentation by Maximising Conditional Likelihood
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Segmentation of MRI trabecular-bone images using network of synchronised oscillators
Machine Graphics & Vision International Journal
An approach to computational microtexture perceptual detection with management of uncertainty
Technologies for constructing intelligent systems
Nonparametric Multiscale Energy-Based Model and Its Application in Some Imagery Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
MRF-MAP-MFT visual object segmentation based on motion boundary field
Pattern Recognition Letters
Proceedings of the 43rd annual Southeast regional conference - Volume 1
Environmental Modelling & Software
Content-based image retrieval methods
Programming and Computing Software
IEEE Transactions on Image Processing
Hierarchical multiple Markov chain model for unsupervised texture segmentation
IEEE Transactions on Image Processing
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Color texture analysis using the wavelet-based hidden Markov model
Pattern Recognition Letters
Selective extraction of entangled textures via adaptive PDE transform
Journal of Biomedical Imaging - Special issue on Mathematical Methods for Images and Surfaces 2011
Fast reduction of speckle noise in real ultrasound images
Signal Processing
Hi-index | 0.01 |
This paper presents multiresolution models for Gauss-Markov random fields (GMRFs) with applications to texture segmentation. Coarser resolution sample fields are obtained by subsampling the sample field at fine resolution. Although the Markov property is lost under such resolution transformation, coarse resolution non-Markov random fields can be effectively approximated by Markov fields. We present two techniques to estimate the GMRF parameters at coarser resolutions from the fine resolution parameters, one by minimizing the Kullback-Leibler distance and another based on local conditional distribution invariance. We also allude to the fact that different GMRF parameters at the fine resolution can result in the same probability measure after subsampling and present the results for first- and second-order cases. We apply this multiresolution model to texture segmentation. Different texture regions in an image are modeled by GMRFs and the associated parameters are assumed to be known. Parameters at lower resolutions are estimated from the fine resolution parameters. The coarsest resolution data is first segmented and the segmentation results are propagated upward to the finer resolution. We use the iterated conditional mode (ICM) minimization at all resolutions. Our experiments with synthetic, Brodatz texture, and real satellite images show that the multiresolution technique results in a better segmentation and requires lesser computation than the single resolution algorithm