A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Markov Random Field Model-Based Approach to Image Interpretation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised segmentation of noisy and textured images using Markov random fields
CVGIP: Graphical Models and Image Processing
Gibbs Random Fields, Cooccurrences, and Texture Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
A systematic way for region-based image segmentation based on Markov Random Field model
Pattern Recognition Letters
An Integration Scheme for Image Segmentation and Labeling Based on Markov Random Field Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quantitative evaluation of color image segmentation results
Pattern Recognition Letters
A Markov Pixon Information Approach for Low-Level Image Description
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Color Image Segmentation
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
Pixon-based image segmentation with Markov random fields
IEEE Transactions on Image Processing
A Novel Pixon-Based Approach for Image Segmentation Using Wavelet Thresholding Method
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Learning non-coplanar scene models by exploring the height variation of tracked objects
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
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In this paper, a pixon-based image representation is proposed, which is a set of disjoint regions with variable shape and size, named pixon. These pixons combined with their attributes and adjacencies construct a graph, which represents the observed image. A Markov random field (MRF) model-based image segmentation approach using pixon-representation is then proposed. Compared with previous work on region-based and pixon-based segmentation methods, the present method has some remarkable improvements over them. Firstly, a set of significant attributes of pixons and edges are introduced into the pixon-representation. These attributes are integrated into the MRF model and the Bayesian framework to obtain a weighted pixon-based algorithm. Secondly, a criterion of GOOD pixon-representation is presented and a fast QuadTree combination (FQTC) algorithm is proposed to extract the good pixon-representation. The experimental results demonstrate that our pixon-based algorithm performs fairly well while reduces the computational cost sharply compared with the pixel-based method.