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

Abstract

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 regularization(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 method is efficient and less in time-consuming.

Keywords

• blurred edge
• target extraction
• kernel functions
• manifold regularization