Automatic categorization of figures in scientific documents
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
Probability in the Engineering and Informational Sciences
A Fuzzy Region-Based Hidden Markov Model for Partial-Volume Classification in Brain MRI
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Echocardiographic image sequence compression based on spatial active appearance model
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Image multi-scale edge detection using 3-D hidden Markov model based on the non-decimated wavelet
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A novel rotationally invariant region-based hidden Markov model for efficient 3-D image segmentation
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
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Statistical modeling methods are becoming indispensable in today's large-scale image analysis. In this paper, we explore a computationally efficient parameter estimation algorithm for two-dimensional (2-D) and three-dimensional (3-D) hidden Markov models (HMMs) and show applications to satellite image segmentation. The proposed parameter estimation algorithm is compared with the first proposed algorithm for 2-D HMMs based on variable state Viterbi. We also propose a 3-D HMM for volume image modeling and apply it to volume image segmentation using a large number of synthetic images with ground truth. Experiments have demonstrated the computational efficiency of the proposed parameter estimation technique for 2-D HMMs and a potential of 3-D HMM as a stochastic modeling tool for volume images.