MAP Disparity Estimation Using Hidden Markov Trees

2015 IEEE International Conference on Computer Vision (ICCV) ◽

10.1109/iccv.2015.256 ◽

2015 ◽

Cited By ~ 13

Author(s):

Eric T. Psota ◽

Jedrzej Kowalczuk ◽

Mateusz Mittek ◽

Lance C. Perez

Keyword(s):

Hidden Markov ◽

Disparity Estimation ◽

Hidden Markov Trees

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Image Denoising with Nonparametric Hidden Markov Trees

2007 IEEE International Conference on Image Processing ◽

10.1109/icip.2007.4379261 ◽

2007 ◽

Cited By ~ 6

Author(s):

Jyri J. Kivinen ◽

Erik B. Sudderth ◽

Michael I. Jordan

Keyword(s):

Image Denoising ◽

Hidden Markov ◽

Hidden Markov Trees

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Classification via Hidden Markov Trees for a Vision-Based Approach to Conveying Webpages to Users with Assistive Needs

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) ◽

10.1109/wi.2016.0124 ◽

2016 ◽

Cited By ~ 1

Author(s):

Michael Cormier ◽

Richard Mann ◽

Robin Cohen ◽

Kary Moffatt

Keyword(s):

Hidden Markov ◽

Hidden Markov Trees

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Multiscale texture segmentation of dip‐cube slices using wavelet‐domain hidden Markov trees

10.1190/1.1820808 ◽

1999 ◽

Author(s):

Ivan Magrin‐Chagnolleau ◽

Hyeokho Choi ◽

Rutger van Spaendonck ◽

Philippe Steeghs ◽

Richard G. Baraniuk

Keyword(s):

Hidden Markov ◽

Texture Segmentation ◽

Wavelet Domain ◽

Hidden Markov Trees

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Rotation-invariant texture retrieval using wavelet-based hidden Markov trees

Signal Processing ◽

10.1016/j.sigpro.2008.04.019 ◽

2008 ◽

Vol 88 (10) ◽

pp. 2593-2598 ◽

Cited By ~ 13

Author(s):

Venkateswara Rao Rallabandi ◽

V.P. Subramanyam Rallabandi

Keyword(s):

Hidden Markov ◽

Rotation Invariant ◽

Texture Retrieval ◽

Hidden Markov Trees

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Scattering convolutional hidden Markov trees

2016 IEEE International Conference on Image Processing (ICIP) ◽

10.1109/icip.2016.7532685 ◽

2016 ◽

Author(s):

J.B. Regli ◽

J. D. B. Nelson

Keyword(s):

Hidden Markov ◽

Hidden Markov Trees

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A framework based on hidden Markov trees for multimodal PET/CT image co-segmentation

Medical Physics ◽

10.1002/mp.12531 ◽

2017 ◽

Vol 44 (11) ◽

pp. 5835-5848 ◽

Cited By ~ 3

Author(s):

Houda Hanzouli-Ben Salah ◽

Jerome Lapuyade-Lahorgue ◽

Julien Bert ◽

Didier Benoit ◽

Philippe Lambin ◽

...

Keyword(s):

Hidden Markov ◽

Ct Image ◽

Pet Ct ◽

Hidden Markov Trees

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Hyperspectral image segmentation and unmixing using hidden Markov trees

2010 IEEE International Conference on Image Processing ◽

10.1109/icip.2010.5653020 ◽

2010 ◽

Cited By ~ 1

Author(s):

Roni Mittelman ◽

Alfred O. Hero

Keyword(s):

Image Segmentation ◽

Hyperspectral Image ◽

Hidden Markov ◽

Hidden Markov Trees

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Oil Spill in SAR Image Denoising Method Based on Contourlet HMT

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.500.545 ◽

2012 ◽

Vol 500 ◽

pp. 545-549 ◽

Cited By ~ 3

Author(s):

Kun Liu ◽

Xiao Feng Wang

Keyword(s):

Oil Spill ◽

Hidden Markov ◽

Research Work ◽

Speckle Reduction ◽

Gray Level ◽

Sar Image ◽

Denoising Method ◽

Image Denosing ◽

Information Detection ◽

Hidden Markov Trees

Contourlet domain hidden markov trees model was introduced in the SAR image denosing research work. Compared to the classical denosing method, the denoised SAR image can preserve information of oil spill and advantages for following up work. According to the gray level, this method segmented the SAR image to region of oil spill and ocean. Then using the method of Contourlet domain hidden markov trees model denoised the different regions. Experimental results show that the method represents better performance in speckle reduction and edges information detection.

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