流形正则化多核模型的模糊红外目标提取

针对模糊边缘的红外目标提取问题，提出一种基于流形正则化多核半监督分类的提取方法。首先应用最大类间方差法计算初始分割阈值，获得确定化的目标和背景区域以及待确定化的模糊边缘区域；然后建立各区域内像素点邻域空间集，并通过多核函数特征映射获得邻域空间中灰度均值和方差信息特征值，通过流形正则获得邻域空间中位置信息特征值；在特征值基础上，建立半监督分类模型对模糊边缘区域像素点邻域空间集进行类别划分；最后计算最佳分割阈值。对比实验结果表明，该方法提取模糊边缘红外目标效果好且运算效率高。

Extraction of blurred infrared targets based on a manifold regularized multiple-kernel model

Specific to the problem of infrared target extraction with blurred edges, this article introduces an extraction method based on a manifold regularized multiple kernel semi-supervised classification model. Firstly, the maximum variance of inter-class ( OTSU) method is used to compute the initial segmentation threshold, and the certain target and background areas and the uncertain blurred edge area are determined. Then, local space sets of pixels are constructed in each area, the multiple-kernel functions are used to map the grayscale mean and variance in local space, and the location information feature in local space is obtained by manifold regu-larization ( MR) . On the basis of features, a semi-supervised classification model is established to classify the local space sets of pixels in the blurred edge area. Finally, the optimal segmentation threshold is computed. Experiments with comparisons show that this meth-od is efficient and less in time-consuming.