Fast algorithm based on superpixel‐level conditional triplet Markov field for successive‐approximation resistor image segmentation. Issue 8 (1st October 2015)

• Record Type: Journal Article
• Authors: Wu, Yan; Wang, Fan; Zhang, Qingjun; Niu, Fanglong; Li, Ming
• Is Part Of: IET radar, sonar & navigation. Volume 9:Issue 8(2015)
• Page Start: 1097
• Page End: 1105
• Publication Date: 2015-10-01
• Subjects: image segmentation -- Markov processes -- image texture -- image representation -- image reconstruction; maximum posterior marginal inference -- superpixel image representation -- TurboPixels algorithm -- SL‐CTMF -- nonstationary image segmentation -- TMF model -- complex nonstationary contents -- SAR images -- texture information -- optical image segmentation -- remote sensing image segmentation -- CRF -- conditional random field -- successive‐approximation resistor image segmentation -- superpixel‐level conditional triplet Markov field; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2014.0442
• Languages: English
• ISSNs: 1751-8784

Abstract

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.