Yang Changcai, Ma Jiayi, Qi Shengxiang, Tian Jinwen, Zheng Sheng, Tian Xin
Appl Opt. 2015 Mar 20;54(9):2255-65. doi: 10.1364/AO.54.002255.
Robust small target detection is one of the key techniques in IR search and tracking systems for self-defense or attacks. In this paper we present a robust solution for small target detection in a single IR image. The key ideas of the proposed method are to use the directional support value of Gaussian transform (DSVoGT) to enhance the targets, and use the multiscale representation provided by DSVoGT to reduce the false alarm rate. The original image is decomposed into sub-bands in different orientations by convolving the image with the directional support value filters, which are deduced from the weighted mapped least-squares-support vector machines (LS-SVMs). Based on the sub-band images, a support value of Gaussian matrix is constructed, and the trace of this matrix is then defined as the target measure. The corresponding multiscale correlations of the target measures are computed for enhancing target signal while suppressing the background clutter. We demonstrate the advantages of the proposed method on real IR images and compare the results against those obtained from standard detection approaches, including the top-hat filter, max-mean filter, max-median filter, min-local-Laplacian of Gaussian (LoG) filter, as well as LS-SVM. The experimental results on various cluttered background images show that the proposed method outperforms other detectors.
鲁棒小目标检测是红外搜索与跟踪系统用于自卫或攻击的关键技术之一。本文提出了一种针对单幅红外图像中小目标检测的鲁棒解决方案。该方法的关键思想是利用高斯变换的方向支持值（DSVoGT）增强目标，并利用DSVoGT提供的多尺度表示降低误报率。通过将图像与从加权映射最小二乘支持向量机（LS-SVM）推导得到的方向支持值滤波器进行卷积，将原始图像分解为不同方向的子带。基于子带图像构建高斯矩阵的支持值，然后将该矩阵的迹定义为目标度量。计算目标度量的相应多尺度相关性，以增强目标信号同时抑制背景杂波。我们在真实红外图像上展示了所提方法的优势，并将结果与从标准检测方法（包括顶帽滤波器、最大均值滤波器、最大中值滤波器、高斯最小局部拉普拉斯（LoG）滤波器以及LS-SVM）获得的结果进行比较。在各种杂波背景图像上的实验结果表明，所提方法优于其他检测器。
