基于向量機(jī)的邊緣檢測(cè)算法優(yōu)化研究
Research of SVM-Based Edge Detection Algorithm Optimization
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摘要: 該文利用最小二乘向量機(jī)(LSSVM)對(duì)原始圖像每一像素的鄰域作灰度曲面的最佳擬合,并以徑向基核函數(shù)為例導(dǎo)出了圖像的梯度算子和零交叉算子。通過梯度和零交叉的綜合,實(shí)現(xiàn)了邊緣的定位和檢測(cè),提出了利用邊緣檢測(cè)性能指標(biāo)來優(yōu)化參數(shù)的方法。確定了高斯LSSVM的參數(shù)(2,)為(7,1),用所選參數(shù)進(jìn)行了圖像邊緣檢測(cè)實(shí)驗(yàn)。結(jié)果表明,基于支持向量機(jī)的邊緣檢測(cè)算法可靠性好、效率高。Abstract: In this paper, the image intensity surface for the neighborhood of every pixel is well-fitted by the Least Squares Support Vector Machine (LSSVM), and the gradient and the zero-crossing operators are deduced from the LSSVM with the Radial Basis Function (RBF) kernel function, as an example. The decision is made whether a pixel is an edge or not based on the combination results of the gradient and the zero-crossings. One method using the edge detection evaluating merit figure to optimize the LSSVM parameters is proposed. The optimal configuration of parameters (2,) for the LSSVM with RBF kernel is (7, 1). With the selected parameters, the computer edge detection experiments are carried out. The experimental results demonstrate the proposed algorithm is reliable and efficient.
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