An Infrared Image Segmentation Method Based on Within-Class Absolute Difference and Chaotic Particle Swarm Optimization

Yiquan, 吴一全 Wu; Bichao, 占必超 Zhan; Jiaming, 吴加明 Wu

doi:10.3788/aos20103001.0079

Cited by 4 publications

(1 citation statement)

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“…Thresholding serves a variety of applications, such as biomedical image analysis, character identification and industrial inspection. Many thresholding approaches and their improvements have been developed over the last few years (Long et al 2012; Liu and Jin 2013; Wu et al 2010; Li and Tian 2009; Qiao et al 2013). The most popular thresholding algorithms include the maximum between-class variance method (namely the Otsu algorithm), the entropy based thresholding algorithm, the minimum error method, the paragenetic matrix method, the moments method, the probability relaxation method and so on.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised background-constrained tank segmentation of infrared images in complex background based on the Otsu method

Zhou¹,

Gao

Fang

et al. 2016

SpringerPlus

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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.

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Section: Introductionmentioning

confidence: 99%