| 红外图像显著目标检测算法 |
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| 引用本文： | 孙照蕾,惠斌,秦莫凡,常铮,罗海波,夏仁波.红外图像显著目标检测算法[J].红外与激光工程,2015,44(9):2633-2637. |
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| 作者姓名： | 孙照蕾 惠斌 秦莫凡 常铮 罗海波 夏仁波 |
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| 作者单位： | 1.中国科学院沈阳自动化研究所,辽宁 沈阳 110016; |
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| 基金项目： | 中国科学院国防科技长信重点基金(CXJJ-14-Z65) |
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| 摘 要： | 提出一种简单快速的红外图像显著目标检测算法,算法可以分为三步:首先,对原始红外图像进行预处理以增强目标与背景的对比度;然后,在log频谱中提取预处理后图像的频谱残差,通过相应的反变换及简单的阈值分割,可以得到显著目标的大致区域;最后,采用一个滑动窗口在目标候选区域内进行搜索确定显著目标的准确位置,这个过程采用由目标及其周围区域在原始图像中的灰度分布得到的半局部特征对比度的概率表达得到每个像素点的显著性值,进行阈值分割得到显著目标,改变滑动窗口的大小可以检测出不同尺度的目标。采用大量的红外图像对算法进行测试,实验结果表明该算法具有高效性和鲁棒性。
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| 关 键 词： | 目标检测 显著性检测 目标候选区域 半局部特征对比度 |
| 收稿时间： | 2015-01-12 |
Object detection method based on saliency measure for infrared radiation image |
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| Affiliation: | 1.Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China;2.University of Chinese Academy of Sciences,Beijing 100049,China;3.Key Laboratory of Opto-Electronic Information Processing,Chinese Academy of Sciences,Shenyang 110016,China;4.The Key Lab of Image Understanding and Computer Vision,Liaoning Province,Shenyang 110016,China;5.Jincheng College of Sichuan University,Chengdu 611731,China |
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| Abstract: | A simple and computationally efficient method was presented for detecting visually salient objects in infrared radiation images. The proposed method can be divided into three steps. Firstly, the infrared image was pre-processed to increase the contrast between objects and background. Secondly, the spectral residual of the pre-processed image was extracted in the log spectrum, then via corresponding inverse transform and threshold segmentation we could get the rough regions of the salient objects. Finally, a sliding window was applied to acquire the explicit position of the salient objects using the probabilistic interpretation of the semi-local feature contrast which was estimated by comparing the gray level distribution of the object and the surrounding area in the original image. And changing the size of the sliding window, different size of objects could be found out. The method was tested on abundant amount of infrared radiation images, and the results show that the saliency detection based object detection method is effective and robust. |
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