NDLI: Fast algorithm based on superpixel-level conditional triplet Markov field for successive-approximation resistor image segmentation

Content Provider	IET Digital Library
Author	Wu, Yan Wang, Fan Zhang, Qingjun Niu, Fanglong Li, Ming
Abstract	Conditional random field (CRF) is a useful tool for optical and remote sensing image segmentation for its ability of incorporating the feature and texture information. However, its application is restricted in successive-approximation resistor (SAR) image segmentation, since SAR images often contain complex non-stationary contents. The triplet Markov field (TMF) model improves the non-stationary image segmentation ability by introducing an auxiliary field to characterise different stationary parts in non-stationary image. Combining the advantages of CRF and TMF, the pixel-level conditional TMF (CTMF) had been proposed. To further improve the segmentation efficiency, a superpixel-level CTMF (SL-CTMF) is proposed in this study. The superpixel representation comes from the improved TurboPixels algorithm and the superpixel representation has better performance in edge location. The auxiliary field U in SL-CTMF is reconstructed on superpixels. With the superpixel-level feature and texture information, the unary and pairwise potentials are derived. Finally, SL-CTMF is applied to real SAR image segmentation with the maximum posterior marginal inference. The experimental results demonstrate the accuracy and the efficiency of the proposed method on SAR images.
Starting Page	1097
Ending Page	1105
Page Count	9
ISSN	17518784
Volume Number	9
e-ISSN	17518792
Issue Number	Issue 8, Oct (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/9/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2014.0442
Journal	IET Radar, Sonar & Navigation
Publisher Date	2015-06-03
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Complex Nonstationary Contents Computer Vision And Image Processing Technique Conditional Random Field CRF Image Reconstruction Image Representation Image Segmentation Image Texture Markov Processes Maximum Posterior Marginal Inference Nonstationary Image Segmentation Optical Image Segmentation Optical, Image And Video Signal Processing Remote Sensing Image Segmentation SAR Image SL-CTMF Successive-approximation Resistor Image Segmentation Superpixel Image Representation Superpixel-level Conditional Triplet Markov Field Texture Information TMF Model TurboPixel Algorithm
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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