引入梯度分布特征的图像背景杂波度量

肖传民; 史泽林; 刘云鹏

doi:10.3788/OPE.20152312.3472

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引入梯度分布特征的图像背景杂波度量

信息科学 | 更新时间：2020-08-13

- 引入梯度分布特征的图像背景杂波度量
- Metrics of image background clutter by introducing gradient features
- 光学精密工程 2015年23卷第12期页码：3472-3479
- 作者机构：
  
  1. 中国科学院大学北京,中国,100049
  2. 中国科学院沈阳自动化研究所,辽宁沈阳,110016
- 作者简介：
- 基金信息：
  
  中国科学院国防科技创新重点基金资助项目(No.Y4A4080401)()
- DOI：10.3788/OPE.20152312.3472
  中图分类号： TP391.4
- 修回日期：2015-06-18，
  
  纸质出版日期：2015-12-25
- 稿件说明：
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肖传民, 史泽林, 刘云鹏. 引入梯度分布特征的图像背景杂波度量[J]. 光学精密工程, 2015,23(12): 3472-3479

XIAO Chuan-min, SHI Ze-lin, LIU Yun-peng. Metrics of image background clutter by introducing gradient features[J]. Editorial Office of Optics and Precision Engineering, 2015,23(12): 3472-3479
肖传民, 史泽林, 刘云鹏. 引入梯度分布特征的图像背景杂波度量[J]. 光学精密工程, 2015,23(12): 3472-3479 DOI： 10.3788/OPE.20152312.3472.

XIAO Chuan-min, SHI Ze-lin, LIU Yun-peng. Metrics of image background clutter by introducing gradient features[J]. Editorial Office of Optics and Precision Engineering, 2015,23(12): 3472-3479 DOI： 10.3788/OPE.20152312.3472.

摘要

为提高图像背景杂波度量法对目标获取性能的预测精度

本文基于人眼视觉对物体边缘敏感的视觉特性

将区域梯度分布作为新的结构特征

提出了引入梯度分布特征的图像背景杂波度量法。首先

采用梯度方向直方图表征目标结构特征

选用巴氏系数度量图像目标和背景杂波在两个梯度方向直方图的相似性 ;然后

将基于图像结构相似性度量方法得到的结构相似性信息进行加权;最后采用D.L.Wilson提出的目标获取性能模型作为目标探测概率、虚警概率和搜索时间的预测模型对Search_2 数据库中的目标进行了获取性能预测。结果显示

提出的图像杂波度量法提高了目标获取性能模型的预测精度

得到的线性相关系数分别为0.870、0.845、0.897

均方根误差分别为0.0569、0.0469、2.129

与实际观察者获得的一致性较高

且没有明显的野点

预测性能明显优于现有其他杂波度量方法。

Abstract

To improve the metric precision of image background clutter for target acquisition performance

a metric method of image background clutter by introducing gradient features is proposed in this paper. The method is based on the visual properties of the human eyes sensitive to edges and regards the regional gradient distribution as a new structural characteristic. Firstly

the gradient direction histogram is used to represent goal structure characteristics and the Pap coefficients are selected for measuring the similarity between the image target and the background clutter gradient direction histogram. Then the structure similarity information is weighted with image structure similarity metrics. Finally

the D.L.Wilson target acquisition performance model is taken as prediction models for predicting the target detection probability

false alarm probability and search time to predict the target acquisition performance of the Search-2 database. The results show that the proposed metric method of image background clutter by introducing gradient feature has improved metric precision of prediction models

the linear correlation coefficients are 0.870

0.845

0.897 and root mean square errors are 0.056 9

0.046 9

2.129

respectively. These data means that the predicted results and the actual observer have good consistency

and the target acquisition performance is superior to those of other methods.

关键词

Keywords

references

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