Research on Real-time Images of Moving Targets Identification Based on the Fast Discrete Curvelet Transform

Bo Mao, Changjiang Feng
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引用次数: 2

Abstract

This paper presents an algorithm of real-time image of moving targets identification based on the fast discrete Curvelet transform. Curvelet, as a new multiscale analysis algorithm, is more appropriate for the analysis of the image edges such as curve and line characteristics than wavelet, and it has better approximation precision and sparsity description. Introduced the curvelet transform to image processing, characteristics of original images are taken better and information for feature extraction is obtained more. The proposal of the second generation curvelet theory makes it to be understood and implemented more easily. Then the fast discrete curvelet transform based image moving targets identification method is proposed. Firstly, the source images are decomposed using curvelet transform, then extract the feature using the edge detection, and finally, get the results through the morphological analysis. Results of simulation experiment show the new method is speedy and very robust.
基于快速离散曲线变换的实时运动目标图像识别研究
提出了一种基于快速离散曲线变换的实时运动目标图像识别算法。Curvelet作为一种新的多尺度分析算法,比小波更适合于分析图像的曲线、直线等边缘特征,具有更好的逼近精度和稀疏性描述。将曲线变换引入到图像处理中,更好地提取了原始图像的特征,获得了更多用于特征提取的信息。第二代曲线理论的提出使其更易于理解和实现。然后提出了基于离散曲线变换的快速图像运动目标识别方法。首先利用曲线变换对源图像进行分解,然后利用边缘检测提取特征,最后通过形态学分析得到结果。仿真实验结果表明,该方法速度快,鲁棒性好。
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