Fast and accuracy extraction of infrared target based on Markov random field
Introduction
Infrared images provide useful information for many applications, such as military navigation, automatic target recognition and tracking [1]. Extraction of infrared targets from an infrared image is particularly important, as it plays an important role in automatic infrared target recognition and infrared target tracking systems. As we know, many infrared imaging systems produce images with small signal-to-noise ratio (SNR) and low contrast, which reduces the detectability of targets and impedes further investigation of infrared images. It is a challenging task to extract the targets from an infrared image accurately and rapidly.
So far, numerous infrared image segmentation methods for extracting the target regions from background have been proposed, e.g., the method based on thresholding [2], [3], [4], [5], [6], the method based on clustering [7], the method based on edge detection [8], [9] and the method based on splitting and merging [10], the method based on level set [11] and the method based on graph cut [12]. Thresholding-based method is widely used in infrared target extraction, such as one-dimensional (1-D) Otsu [3], two-dimensional (2-D) Otsu [5], 2-D entropy [6], etc. However, the thresholding-based methods cannot achieve satisfactory results for the infrared images with complex background and low SNR, since they ignore the spatial relationship of the image pixels which leads to increased sensitivity to image noise, increasing the error rate. They are likely to classify some of the background into foreground (targets) for the infrared images.
In the past 20 years, Markov random field (MRF) has been developed as a powerful tool for image segmentation because it can capture the spatial constraints among pixels and merge the context information into segmentation process. There are various MRF-based segmentation models that have been developed. Cohen and Cooper [13] proposed a doubly MRF model for segmenting range images and natural scenes. Melas and Wilson [14] applied the double MRF model with modification to segment satellite images. Won and Derin [15] developed a hierarchical MRF model for segmenting noisy and textured images. Scherrer et al. [16] constructed the distributed local MRF models for tissue and structure segmentation of medical images. Poggi et al. [17] did research on supervised segmentation of remote sensing images based on a tree-structured MRF model. Panjwani and Healey [18] extended the coupled MRF models to segment textured color images. Deng and Clausi [19] proposed a segmentation of SAR sea-ice image using a simple MRF with a variable weighting parameter. Xu and Luo [20] proposed a new nonhomogeneous MRF model based on fuzzy membership for brain MRI segmentation. However, the application of MRF models to segment infrared imagery is not commonly represented in the research literature. It is mainly because for the infrared images with complex background and low SNR, MRF-based model is likely to classify some of the background into foreground. In addition, the classic MRF model is complicate and has low convergence speed, which impedes its application for infrared image segmentation.
The goal of this paper is to propose a fast and accurate segmentation method for infrared target extraction based on MRF. First, the regions of interests (ROIs) which include the entire targets regions and some background regions are detected by using the variance weighted information entropy (WIE) [21]. Then, the infrared image is modeled with MRF, and the ROIs are used as the target regions and the remaining region as background to do the initial segmentation. Finally, by searching solution space in the ROIs based on the fact that targets can only exist within the ROIs, the targets are accurately extracted by the energy minimization. This coarse-to-fine extraction method can reduce the interference of the background clutter and noise in a large degree and decrease the search space greatly.
The rest of the paper is organized as follows. Section 2 introduces ROIs detection based on the variance weighted information entropy. Section 3 first describes the MAP–MRF segmentation framework, and then introduces the accurate target extraction based on MRF. Section 4 is devoted to experimental results for infrared target extraction with the proposed and other techniques. Conclusions are drawn in Section 5.
Section snippets
ROIs detection based on variance WIE
Information entropy is an effective way to describe the information contained in an image. The classic information entropy of an image can express the average amount of information effectively, but it also ignores the importance of gray information. Therefore, it cannot veritably reflect the complex degree of image background [22], [23] made by the subjective judgments. To measure the complex degree of different infrared images, the weighted information entropy is introduced [22]. Subsequently,
MAP–MRF segmentation framework
Let S={s=(i,j)|1≤i≤H,1≤j≤W} be the set of two-dimensional image lattice sites, where H and W are the image height and width in pixels. X and Y are MRFs defined on neighborhood system η. Y stands for the observed random field and the observed image y is an instance of Y, x is a segmentation configuration of the ideal random field X. The task of image segmentation can be treated as estimating a segmentation configuration x through an instance of the observed field Y. According to the Bayesian
Experiments and results
The proposed target extraction approach is applied to the infrared images with three typical different backgrounds, which are sky–sea, the ground and sky background, to demonstrate its effectiveness. The experimental results are also compared with 2-D Otsu thresholding and the classic SMRF method. Table 1 shows the configuration of experimental parameters for the classic SMRF and the proposed method. All the methods are coded in Matlab 7.0 running on a Pentium4 Q8200 2.33 G CPU system with 2 G
Conclusions
A coarse-to-fine infrared target extraction algorithm is proposed. The ROIs are first detected by the variance WIE. Then the infrared image is modeled by Gaussian MRF using the ROIs as target regions (foreground) while the remaining region as background to perform the initial segmentation. At last, the targets are accurately extracted by the energy minimization using ICM by searching solution space within ROIs. Experiments show very promising results for our method.
Acknowledgments
The authors would like to thank the anonymous reviewers for their very helpful suggestions on the original version of the paper. This work was supported by the National Natural Science Foundation of China (Grant no. 60873086), the graduate starting seed fund of Northwestern Polytechnical University (Grant no. z2010075) and the Fundamental research Found of Northwestern Polytechnical University (Grant no. JC200942).
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