Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gibbs Random Fields, Cooccurrences, and Texture Modeling
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
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
Digital Image Processing
Fuzzy c-means clustering methods for symbolic interval data
Pattern Recognition Letters
A novel pixon-representation for image segmentation based on Markov random field
Image and Vision Computing
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
Pixon-based image segmentation with Markov random fields
IEEE Transactions on Image Processing
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In this paper a novel pixon-based method is proposed for image segmentation, which uses the combination of wavelet transform (WT) and the pixon concept. In our method, a wavelet thresholding technique is successfully used to smooth the image and prepare it to form the pixons. Utilizing the wavelet thresholding leads to elimination of some unnecessary details and results in a fewer pixon number, faster performance and more robustness against unwanted environmental noises. As the next step, the appropriate pixons are extracted and eventually we segment the image with the use of a hierarchical clustering method. The results of applying the proposed method on several different images indicate its better performance in image segmentation compared to the other methods.