利用极值梯度的通用亚像素边缘检测方法

针对传统边缘检测方法存在的通用性较差、精度不高等问题，提出一种利用极值梯度的通用亚像素边缘检测方法。该方法将极值梯度分解为正梯度和负梯度，并在八个方向上进行判断与求解，然后得到由局部灰度增加最大和减小最大的两类像点共同组成的初始边缘，最后根据初始边缘的特点，分别建立不同类型边缘的亚像素定位拟合模型。为验证该方法的性能，分别利用模拟影像和实际影像与传统方法进行对比试验。试验结果表明该方法对不同类型的边缘都能较好的检测，并且对包括角点在内的边缘有更高的定位精度。因此，该方法可有效应用于影像的边缘检测中。

Universal Sub-pixel Edge Detection Algorithm Base on Extremal Gradient

A universal sub-pixel edge detection algorithm is proposed based on extremal gradient, with the purpose of further improving the universal character and precision of traditional algorithms. Extremal gradient is disintegrated into positive and negative gradients that are solved respectively in eight directions. Then, initial edge composed of two types of pixels with local gray level maximum increase and decrease can be obtained. Finally, sub-pixel orientation fitting models are built for different types of edges separately according to the characteristic of initial edges. Experiments between the proposed algorithm and the others have been realized to verify its performance based on simulative and real images. The results indicate that the proposed algorithm has better applicability of different types of edges and higher precision including corner point than traditional algorithms. Therefore, this algorithm is effective in image edge detection.