Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Real-time detection of steam in video images
Pattern Recognition
Graph Cuts via $\ell_1$ Norm Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiscale fusion of wavelet-domain hidden Markov tree through graph cut
Image and Vision Computing
Boundary refinements for wavelet-domain multiscale texture segmentation
Image and Vision Computing
Color texture analysis using the wavelet-based hidden Markov model
Pattern Recognition Letters
Automatic inspection of tobacco leaves based on MRF image model
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
Multiscale image segmentation using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
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This paper proposed a novel method for global continuous optimization of maximum a posterior(MAP) during wavelet-domain hidden Markov tree-based(WHMT) multiscale information fusion process. We start with calculating the multiscale classification likelihoods of wavelet coefficients by expectation-maximization(EM) algorithm. Energy function is then generated by combining boundary term estimated by classification likelihoods with regional term obtained by both pixel information and approximation coefficients. Through energy minimization through graph cut via convex optimization, objects are segmented accurately from the images in a global optimization sense. A performance measure for tobacco leaf inspection is used to evaluate our algorithm, the localization accuracy of weak boundary by fusing multiscale information via convex optimization is encouraging.