【摘要】 针对如何提高图像边缘检测效率的问题,提出1种结合最小二乘支持向量机(LSSVM)和元胞自动机进行图像边缘检测的方法。首先,基于Gauss径向基核和多项式核构建出新的核函数,使得LSSVM对图像像素邻域的灰度值能够进行准确的曲面拟合。接着,推导出图像的梯度算子,并与图像灰度值进行卷积得到图像的梯度值。然后,元胞自动机按照所设计的局部规则对梯度值进行演化,实现图像边缘的定位和检测。仿真实验检测出的图像边缘定位准确,而且达到1个像素宽,表明新提出的边缘检测算法是有效的;同时,通过对比分析得知新算法具有比Sobel和Canny算法更高的检测性能。更多还原

【Abstract】 Aiming at how to establish the ideal standard for the edge detection,a new image edge detection method was proposed based on a combination of Least Squares Support Vector Machine(LSSVM) and cellular automata.Polynomial kernel function and Gaussian kernel function were deployed to construct a new kind of kernel function.LSSVM selected the new kernel function and fitted the image intensity surface for the neighborhood of every pixel.The gradient operators which were deduced from the above LSSVM convoluted with the image gray values to get the image gradient values.Gradient values were evolved out by cellular automata with the designed local rules in order to achieve the best edge detection performance.Simulation results showed that the edge width was a pixel and the edge positioning was accurate,therefore the proposed algorithm is feasible.Furthermore,the detection performance of the proposed algorithm is higher than Sobel and Canny algorithm.更多还原