Fast and accuracy extraction of infrared target based on Markov random field

Liu, Ying; Mao, Xingjin; Feng, David Dagan; Zhang, Yanning

doi:10.1016/j.sigpro.2010.12.003

Cited by 19 publications

(13 citation statements)

References 20 publications

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“…Subsequently, the variance WIE concept is proposed and firstly applied to the ROIs extraction of infrared images. Due to the fact that objects with different radiation or reflection usually appear to have distinct gray value in real images, the variance WIE is proved to be an effective way to evaluate the complex degree of intensity distribution upon an image robustly [9,11]. For a 256 gray levels image, the variance WIE of the image is defined as follows:…”

Section: Variance Wiementioning

confidence: 99%

“…The strong dependence of the CFAR schemes on prior knowledge about ships and background observation limits their application. The variance weighted information entropy (WIE), recently being applied to the extraction of regions of interest (ROIs) from infrared and SAR images, has been proved to be a simple and effective quantitative description index for the complex degree of image background [8,9]. The variance WIE is particularly suitable for gray imagery to measure its non-uniformity.…”

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confidence: 99%

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Adaptive ship detection in SAR images using variance WIE-based method

Wang

Chen

2016

SIViP

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Automatic detection of ship targets from synthetic aperture radar (SAR) images is an important and challenging problem. Given the different nature of target returns in homogeneous and heterogeneous regions in SAR imagery, conventional detection algorithms fail to yield automatic and robust results. In support of automatic vessel monitoring, an adaptive detection framework designed for detecting ships from SAR imagery is proposed in this paper, and the variance weighted information entropy is introduced into the framework construction. Experimental results indicate that the proposed method can effectively detect the ship targets from various circumstances without any prior knowledge.

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Section: Variance Wiementioning

confidence: 99%

mentioning

confidence: 99%

Adaptive ship detection in SAR images using variance WIE-based method

Wang

Chen

2016

SIViP

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“…Briefly, this average WIE algorithm based on 'positioning-segmentation' is reflected in a 'rough-fine' structure: First of all, extract blocks pieces which may contain the target through ROIs [11]. Then, refine the ROIs and conduct the variational segmentation.…”

Section: Target Extraction By a Location-segmentation Strategymentioning

confidence: 99%

Fast target detection method for high-resolution SAR images based on variance weighted information entropy

Cao

Feng

2014

EURASIP J. Adv. Signal Process.

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Since the traditional CFAR algorithm is not suitable for high-resolution target detection of synthetic aperture radar (SAR) images, a new two-stage target detection method based on variance weighted information entropy is proposed in this paper. On the first stage, the regions of interest (ROIs) in SAR image is extracted based on the variance weighted information entropy (WIE), which has been proved to be a simple and effective quantitative description index for the complex degree of infrared image background. Considering that SAR images are nonuniform, an experiment is conducted ahead, in which the value of the variance WIE from a real SAR image in three areas with significant different uniform levels are tested and compared. The results preliminarily verified that the variance WIE is able to measure the complex degree of SAR images. After that, in order to make the segmentation efficient, the rough ROIs are further processed with a series of methods which adjust ROIs into regular pieces. On the second stage, for each of the ROIs, a variational segmentation algorithm based on the Split-Bregman algorithm is adopted to extract the target. In our experiment, the proposed method is tested on two kinds of SAR images, and its effectiveness is successfully demonstrated.

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“…During infrared image processing technics, infrared image segmentation is a paramount preprocessing step in infrared image analysis and computer vision 2,3 . It intends to separate an object from a background based on some pertinent characteristics such as gray level, gray gradient, texture and location 4 .…”

Section: Introductionmentioning

confidence: 99%