Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multilevel Banded Graph Cuts Method for Fast Image Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic Approach
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
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
Bayesian tree-structured image modeling using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
Multiscale image segmentation using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
Minimum classification error learning for sequential data in the wavelet domain
Pattern Recognition
Multiscale information fusion by graph cut through convex optimization
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Global optimization of wavelet-domain hidden Markov tree for image segmentation
Pattern Recognition
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Since object boundaries appear blurry, reduced localization accuracy of wavelet-domain hidden Markov tree-based (WHMT) method poses a problem during the object extraction process. A novel approach to improve localization accuracy by fusing multiscale information of the tree model is presented. 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 approximation coefficients. Through energy minimization via graph cuts, objects are extracted accurately from the images. A performance measure for tobacco leaf inspection is used to evaluate our algorithm.