Feature based fuzzy inference system for segmentation of low-contrast infrared ship images

Bai, Xiangzhi; Liu, Miaoming; Wang, Tao; Chen, Zhiguo; Wang, Peng; Zhang, Yu

doi:10.1016/j.asoc.2016.05.004

Cited by 32 publications

(13 citation statements)

References 53 publications

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“…Bai et al [1], [24] improved the classical fuzzy c-means (FCM) clustering by adding nonlocal spatial information and the spatial shape information of the infrared ship target, which can efficiently segment the ship target in weakly textured backgrounds. In addition, the featurebased fuzzy inference system was suggested in [25] to segment infrared ship targets. In this system, the intensity feature, local spatial feature and global spatial feature are fuzzified using prior knowledge, and the ship targets are segmented and extracted by fuzzy inference.…”

Section: Related Workmentioning

confidence: 99%

“…Accordingly, the intensity-based ship target segmentation methods [10], [12], [16] will introduce those uneven parts into the segmentation result and cause under-segmentation. Furthermore, by staring at the appearance model of ship target in IR image, the existing methods commonly assume that the ship target is the uniform region against the sea background [22], [25], [26]. However, for IR ship image with low contrast or near-distance imaging, the intensity of ship target will be inhomogeneous and even the inner parts of the ship target with strong opposite intensities.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Infrared Ship Target Segmentation Based on Structure Tensor and Maximum Histogram Entropy

Ding³

et al. 2020

IEEE Access

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The existing infrared (IR) ship target segmentation methods may suffer serious performance degradation in the situation of diverse background clutters and ship targets. To cope with this problem, a novel ship target segmentation method is proposed in this paper. Initially, the IR image is transformed into the map of large eigenvalues of structure tensor (STLE), where the horizon line and ship target boundary can be explicitly characterized. According to the scene context clue, the automatic horizon line detection (AHLD) is proposed to efficiently judge the existence of horizon line and remove sky/land region clutters. Then, based on the intensity distribution of ship target and sea background, the adaptive maximum histogram entropy (AMHE) is presented to accurately perceive the brightness (dark or bright) of ship target, and coarsely segment the bright or dark ship target from sea background. After that, considering the ship target boundary information, the regions-of-interest (ROI) of ship target is located and the ship foreground map (SFM) is developed to address the under-segmentation. Finally, a new Watershed algorithm namely structure tensor and maximum histogram entropy modified Watershed transform (TEWT) is constructed to completely extract the whole ship target. Extensive experiments show that the proposed method outperforms the state-of-the-art methods, especially for IR images with intricate background clutter and heavy noise. Moreover, the proposed method can work stably for ship target with unknown brightness, uneven intensities, low contrast, variable quantities, sizes, and shapes.INDEX TERMS Infrared (IR) imaging, ship target segmentation, structure tensor, maximum histogram entropy, modified watershed transform.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Infrared Ship Target Segmentation Based on Structure Tensor and Maximum Histogram Entropy

Ding³

et al. 2020

IEEE Access

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“…Most studies [11,23,25] indicated that the background is the comparatively dark sea surface and the ship targets might be relatively local brighter regions. According to these above-mentioned methods and theories, the accurate bright ship target detection algorithm for TIR image is established.…”

Section: Proposed Bright Ship Target Detection In Tir Imagesmentioning

confidence: 99%

“…To conquer this problem, Liu et al [23,24] proposed an effective infrared ship target detection method based on saliency map fusion by exploiting multi-features of ship target, including local contrast, edge information, and brighter intensity. Following, Bai et al [25] presented a new detection method for low-contrast infrared ship targets by analyzing the fuzzy inference system that integrates both local saliency information and global spatial feature. The two methods can acquire excellent performance for the detection of infrared ships in complex background clutter.…”

Section: Introductionmentioning

confidence: 99%

Thermal Infrared Small Ship Detection in Sea Clutter Based on Morphological Reconstruction and Multi-Feature Analysis

Zhu

et al. 2019

Applied Sciences

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The existing thermal infrared (TIR) ship detection methods may suffer serious performance degradation in the situation of heavy sea clutter. To cope with this problem, a novel ship detection method based on morphological reconstruction and multi-feature analysis is proposed in this paper. Firstly, the TIR image is processed by opening- or closing-based gray-level morphological reconstruction (GMR) to smooth intricate background clutter while maintaining the intensity, shape, and contour features of ship target. Then, considering the intensity and contrast features, the fused saliency detection strategy including intensity foreground saliency map (IFSM) and brightness contrast saliency map (BCSM) is presented to highlight potential ship targets and suppress sea clutter. After that, an effective contour descriptor namely average eigenvalue measure of structure tensor (STAEM) is designed to characterize candidate ship targets, and the statistical shape knowledge is introduced to identify true ship targets from residual non-ship targets. Finally, the dual method is adopted to simultaneously detect both bright and dark ship targets in TIR image. Extensive experiments show that the proposed method outperforms the compared state-of-the-art methods, especially for infrared images with intricate sea clutter. Moreover, the proposed method can work stably for ship target with unknown brightness, variable quantities, sizes, and shapes.

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“…When detecting moving targets in an image sequence, there are differential image method, optical flow method and statistical model. In order to achieve the goal segmentation [8], proposed a feature based fuzzy inference system for infrared image segmentation. It used the unimodal threshold and morphological processing to extract the local spatial features of the target image, and then used the fuzzy rea-soning system to complete the target segmentation [9].…”

Section: Introductionmentioning

confidence: 99%

Effective detection by fusing visible and infrared images of targets for Unmanned Surface Vehicles

Dai

Zhao

2018

Automatika

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The research progress for Unmanned Surface Vehicle (USV) is of great significance to human offshore operations. Target detection is the foundation for USV applications. Ocean wave, frog, and illumination are the most important factors that affect exactness of target detection through visible and infrared images. This paper proposes an algorithm for weighted averaging fusion of visible/infrared images. Firstly, the visible light/infrared devices are required to collect the target surrounding information, perform feature analysis, and complete the anti-fog and de-noising preprocessing. These operations aim at improving the accuracy of image segmentation. Secondly, feature extractions of the visible and infrared target images are performed, respectively, and the recognition of the target image is further completed. Finally, image fusion is performed by weighted averaging of the targets detected by visible light and infrared images. The fusion uses a matching matrix to represent the similarity of the two images. When the two images are very similar, the image is fused by weighting pixels, which effectively improves the accuracy of the detection. Lots of simulations were conducted on MATLAB 2015a with a personal computer, and the results verified the success rate of target detection and recognition.

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Feature based fuzzy inference system for segmentation of low-contrast infrared ship images

Cited by 32 publications

References 53 publications

Automatic Infrared Ship Target Segmentation Based on Structure Tensor and Maximum Histogram Entropy

Automatic Infrared Ship Target Segmentation Based on Structure Tensor and Maximum Histogram Entropy

Thermal Infrared Small Ship Detection in Sea Clutter Based on Morphological Reconstruction and Multi-Feature Analysis

Effective detection by fusing visible and infrared images of targets for Unmanned Surface Vehicles

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