NDLI: Single Loop Inference of Hidden Markov Tree for Multiscale Image Segmentation

Content Provider	IEEE Xplore Digital Library
Author	Zhang Yinhui Peng Jinhui He Zifen
Copyright Year	2013
Description	Author affiliation: Fac. of Mech. & Electr. Eng., Kunming Univ. of Sci. & Technol., Kunming, China (Zhang Yinhui; He Zifen) \|\| Emphasis Lab. of Minist. of Educ. of Unconventionality Metall., Kunming Univ. of Sci. & Technol., Kunming, China (Peng Jinhui)
Abstract	This paper addresses the problem of bottom-up and up-bottom multiscale segmentation of objects in the presence of dynamic backgrounds. Previous hidden Markov tree (HMT) based approaches have exploited an iterative inference scheme and each iteration consists of an two-stage segmentation mechanism, namely, parameter learning and multiscale fusion of likelihoods. In this paper, we propose a novel approach for recovering multiscale segmentation accurately in the absence of iterative multiscale fusion stage. This allows both inference and fusion of multiscale classification likelihoods to be computed in a single loop through bottom-up likelihood estimation and up-bottom posterior inference of HMT. Experimental results on a synthesized image in the presence of Gaussian white noise demonstrate the high robustness achieved by the proposed method.
Sponsorship	Central South Univ.
Starting Page	1005
Ending Page	1008
File Size	277452
Page Count	4
File Format	PDF
ISBN	9781467356527
DOI	10.1109/ICMTMA.2013.249
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Image segmentation Dynamic Image color analysis Hidden Markov models Object segmentation Inspection Noise measurement HMT Multiscale
Content Type	Text
Resource Type	Article

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