纹理粗糙度在红外图像显著性检测中的应用

赵爱罡; 王宏力; 杨小冈; 陆敬辉; 姜伟

doi:10.3788/OPE.20162401.0220

您当前的位置：

首页 >

文章列表页 >

纹理粗糙度在红外图像显著性检测中的应用

信息科学 | 更新时间：2020-08-13

- 纹理粗糙度在红外图像显著性检测中的应用
- Application of texture coarseness in saliency detection of infrared image
- 光学精密工程 2016年24卷第1期页码：220-228
- 作者机构：
  
  1. 第二炮兵工程大学控制工程系,陕西西安,710025
  2. 第二炮兵工程大学士官学院, 山东青州,262500
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.61203189,No.61374054)()
- DOI：10.3788/OPE.20162401.0220
  中图分类号： TJ765.3;TP391.4
- 收稿日期：2015-11-02，
  
  修回日期：2015-12-05，
  
  纸质出版日期：2016-01-25
- 稿件说明：
移动端阅览
赵爱罡, 王宏力, 杨小冈等. 纹理粗糙度在红外图像显著性检测中的应用[J]. 光学精密工程, 2016,24(1): 220-228

ZHAO Ai-gang, WANG Hong-li, YANG Xiao-gang etc. Application of texture coarseness in saliency detection of infrared image[J]. Editorial Office of Optics and Precision Engineering, 2016,24(1): 220-228
赵爱罡, 王宏力, 杨小冈等. 纹理粗糙度在红外图像显著性检测中的应用[J]. 光学精密工程, 2016,24(1): 220-228 DOI： 10.3788/OPE.20162401.0220.

ZHAO Ai-gang, WANG Hong-li, YANG Xiao-gang etc. Application of texture coarseness in saliency detection of infrared image[J]. Editorial Office of Optics and Precision Engineering, 2016,24(1): 220-228 DOI： 10.3788/OPE.20162401.0220.

摘要

提出了基于纹理粗糙度的红外图像显著性检测算法

以解决红外图像对比度低

目标显著性检测难的问题。首先

研究了Tamura的粗糙度原理

对粗糙度进行分析和评价

提出了新的粗糙度计算方法。然后

将图像分解为超级像素集合

并计算超级像素的最大平均强度差;利用最大平均强度差定义超级像素的最佳尺度

作为纹理粗糙度的度量。最后

将超级像素区域均匀外延

利用粗糙度的局部对比度和灰度信息度量红外图像的显著性。通过实验验证了本文算法的有效性

结果表明:在10%的噪声水平下

本文粗糙度保持不变

粗糙度特征图一致性较好

而Tamura的粗糙度特征图中杂点明显增多。与其它显著性检测算法对比

本文算法击中率最高

为0.752。该算法挖掘了红外图像的纹理粗糙度特征

为红外图像显著性检测提供了新的特征选择。

Abstract

A saliency detection algorithm for infrared images based on texture coarseness was proposed to detect the saliency of targets owing to a low image contrast. Firstly

Tamura's principle of calculating coarseness was researched

and a new method to calculate the coarseness was presented by analysis and evaluation of the coarseness. Then the image was decomposed into a set of super pixels and the maximum mean intensity difference of the super pixels was calculated. Furthermore

the best scale of super pixels was defined by using maximum mean intensity difference to be a measure of the texture coarseness. Finally

the region of every super pixel was expanded isotropically and the saliency of infrared image was measured based on the local contrast and grey information of the texture coarseness with the weight of intensity. The effectiveness of algorithm was verified. Results show that coarseness based on the proposed method remains unchanged under a noise level of 10% and the homogeneity is better in the feature map of coarseness. Meanwhile

there are many miscellaneous points in Tamura's feature map of coarseness. Compared with other methods of saliency detection for infrared images

the proposed algorithm has the highest hit rate

up to 0.752. The algorithm exploits the feature of texture coarseness

and provides a new selection method for the saliency detection of infrared images.

关键词

Keywords

references

李一芒, 何昕, 魏仲慧, 等. 采用降维技术的红外目标检测与识别[J]. 光学精密工程, 2013, 21(5): 1297-1303. LI Y M, HE X, WEI ZH H, et al.. Infrared target detection and recognition using dimension reduction technology [J]. Opt. Precision Eng., 2013, 21(5): 1297-1303. (in Chinese)

王建平, 李俊山, 杨亚威, 等. 基于红外成像的乙烯气体泄漏检测[J]. 液晶与显示, 2014, 29(4): 623-628. WANG J P, LI J SH, YANG Y W, et al.. Ethylene gas leaking detection based on infrared imaging [J]. Liquid Crystals and Displays, 2014, 29(4): 623-628. (in Chinese)

孙占久, 聂宏, 黄伟. 无人机红外辐射特性计算与分析[J]. 红外与激光工程, 2014, 43(4): 1037-1046. SUN ZH J, NIE H, HUANG W. Calculation and analysis on infrared radiation characteristics of UAV [J]. Infrared and Laser Engineering, 2014, 43(4): 1037-1046. (in Chinese)

赵宏伟, 陈霄, 刘萍萍, 等. 视觉显著目标的自适应分割[J]. 光学精密工程, 2013, 21(2): 531-538. ZHAO H W, CHEN X, LIU P P, et al.. Adaptive segmentation for visual salient object [J]. Opt.Precision Eng., 2013, 21(2): 531-538. (in Chinese)

张来刚, 魏仲慧, 何昕, 等. 面向低纹理图像的快速立体匹配[J]. 液晶与显示, 2013, 28(3): 450-458. ZHANG L G, WEI ZH H, HE X, et al.. New stereo matching based edge extraction [J]. Liquid Crystals and Displays, 2013, 28(3): 450-458. (in Chinese)

邓丹, 吴谨, 朱磊, 等. 基于纹理抑制和连续性分布估计的显著性目标检测方法[J]. 液晶与显示, 2015, 30(1): 120-125. DENG D, WU J, ZHU L, et al.. Significant target detection method based on texture inhibition and continuous distribution estimation [J]. Liquid Crystals and Displays, 2015, 30(1): 120-125. (in Chinese)

ITTI L, KOCH C, NIEBUR E. A model of saliency-based visual attention for rapid scene analysis [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1998,20(11): 1254-1259.

LIU T, YUAN Z, SUN J, et al.. Learning to detect a salient object [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(2): 353-367.

ACHANTA R, HEMAMI S, ESTRADA F, et al.. Frequency-tuned salient region detection [C]. IEEE Conference on Computer Vision and Pattern Recognition, 2009: 1597-1604.

HOU X, ZHANG L. Saliency detection: A spectral residual approach [C]. IEEE Conference on Computer Vision and Pattern Recognition, 2007: 1-8.

RAO A R. A Taxonomy for Texture Description and Identification [M]. Springer Science & Business Media, 2012.

FELZENSZWALB P, HUTTENLOCHER D P. Efficient graph-based image segmentation [J]. International Journal of Computer Vision, 2004, 59(2): 167-181.

肖钟捷, 刘用麟. 基于清晰图像先验知识的盲复原算法[J]. 红外与激光工程, 2015, 44(5): 1666-1672. XIAO ZH J, LIU Y L. Image blind restoration using priors of sharp images [J]. Infrared and Laser Engineering, 2015, 44(5): 1666-1672. (in Chinese)

金左轮, 韩静, 张毅, 等. 基于纹理显著性的微光图像目标检测[J]. 物理学报, 2014, 63(6): 69501. JIN Z L, HAN J, ZHANG Y, et al.. Low light level image target detection based on texture saliency [J]. Acta Phys. Sin., 2014, 63(6): 69501. (in Chinese)

FAIRCHILD M D. Color Appearance Models [M]. New Jersey:John Wiley & Sons, 2013.

CHENG M, MITRA N J, HUANG X, et al.. Global contrast based salient region detection [C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015, 37(3):569-582.

浏览量

501

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

集成视觉显著性和群决策的航空零件孔特征检测

结合分数阶显著性检测及量子烟花算法的NSST域图像融合

结合Retinex校正和显著性的主动轮廓图像分割

光学遥感图像中复杂海背景下的舰船检测

基于元胞自动机的动态背景运动目标检测