Abstract
In an effort to implement fast and effective tank segmentation from infrared images in complex background, the threshold of the maximum between-class variance method (i.e., the Otsu method) is analyzed and the working mechanism of the Otsu method is discussed. Subsequently, a fast and effective method for tank segmentation from infrared images in complex background is proposed based on the Otsu method via constraining the complex background of the image. Considering the complexity of background, the original image is firstly divided into three classes of target region, middle background and lower background via maximizing the sum of their between-class variances. Then, the unsupervised background constraint is implemented based on the within-class variance of target region and hence the original image can be simplified. Finally, the Otsu method is applied to simplified image for threshold selection. Experimental results on a variety of tank infrared images (880 × 480 pixels) in complex background demonstrate that the proposed method enjoys better segmentation performance and even could be comparative with the manual segmentation in segmented results. In addition, its average running time is only 9.22 ms, implying the new method with good performance in real time processing.
Highlights
In military fields, forward-looking infrared (FLIR) systems of long wave infrared (LWIR) are widely used to improve the night fighting capability, such as missile guidance and visual supervision systems
The classes division of an infrared image with complex background Based on the analysis result in “Threshold analysis of the Otsu method”, one can conclude that the Otsu method will not get satisfactory segmentation until the within-class variances (WCVs) of the background gets close to that of the target region
Background constraint based on the within‐class variance of the target region From the analysis results in “The Otsu method and its threshold analysis”, one can concluded that the Otsu method could not have better performance in image segmentation until the WCV of background gets close to that of the target region
Summary
Forward-looking infrared (FLIR) systems of long wave infrared (LWIR) are widely used to improve the night fighting capability, such as missile guidance and visual supervision systems. The Otsu method The maximum between-class variance method, namely the Otsu method, was proposed by Otsu, which is based on single dimension gray histogram of the image and makes maximizing the variance between classes of background and target regions as threshold selection criterion. The small difference between WCVs of the background and the target regions is necessary for the Otsu method to produce satisfying segmented results.
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