Portrait beautification: A fast and robust approach
Image and Vision Computing
A novel pixon-representation for image segmentation based on Markov random field
Image and Vision Computing
Segmentation of cDNA microarray images by kernel density estimation
Journal of Biomedical Informatics
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
Shape analysis of human brain with cognitive disorders
ICDHM'07 Proceedings of the 1st international conference on Digital human modeling
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
An improved anisotropic diffusion model for detail- and edge-preserving smoothing
Pattern Recognition Letters
Multivariate image segmentation using semantic region growing with adaptive edge penalty
IEEE Transactions on Image Processing
Advances on medical imaging and computing
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
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Image segmentation is an essential processing step for many image analysis applications. We propose a novel pixon-based adaptive scale method for image segmentation. The key idea of our approach is that a pixon-based image model is combined with a Markov random field (MRF) model under a Bayesian framework. We introduce a new pixon scheme that is more suitable for image segmentation than the "fuzzy" pixon scheme. The anisotropic diffusion equation is successfully used to form the pixons in our new pixon scheme. Experimental results demonstrate that our algorithm performs fairly well and computational costs decrease dramatically compared with the pixel-based MRF algorithm.