边缘线上各向异性高斯核信息熵的角点检测  

Corner detection via anisotropic Gaussian kernels information entropy on edge contours

作　　者：章为川[1] 水鹏朗[1] 徐国靖[1] 

机构地区：[1]西安电子科技大学雷达信号处理国家重点实验室,陕西西安710071

出　　处：《西安电子科技大学学报》2013年第4期119-124,129,共7页Journal of Xidian University

基　　金：国家自然科学基金资助项目(60872139)

摘　　要：为了提高角点检测的准确率,提出了一种基于图像边缘和各向异性高斯方向导数信息熵的角点检测方法.首先利用Canny边缘检测器提取图像的边缘映射;然后,填充轮廓曲线间的小缺口.对于每一个边缘像素,根据边缘像素及邻近像素最大方向导数所对应的主方向来计算主方向的分布概率和它的信息熵.不同于传统的基于轮廓的角点检测方法,该方法通过计算边缘像素及邻近像素的最大强度变化方向所对应的熵来检测角点.相比计算轮廓曲线上曲率的方法,具有更好的稳健性.实验结果表明,与现有的方法相比,该文提出的检测方法具有更好的角点检测性能.To improve the accuracy of corner detection, a new corner detector is proposed based upon the information entropy which is derived by the anisotropic Gaussian directional derivatives （ANDDs） on edge contours of an image. Firstly, the edge map of an image is extracted by the Canny edge detector. Secondly, small gaps between contours are filled. Finally, on each contour pixel, the main direction corresponding to the maximal ANDDs at each contour pixel and its surrounding pixels are used to compute the main direction＇s probability density function and information entropy. Different from the traditional contour-based detectors, our detector uses the maximal intensity variation＇s directional information entropy on contours and surrounding pixels rather than the curvatures of the planar curves, which presents better robustness. Experimental results show that the proposed detector achieves a better corner detection performance than several state-of-the-art detectors.

关 键 词：边缘轮廓 各向异性高斯方向导数 信息熵 角点检测 

分 类 号：TN973.3[电子电信—信号与信息处理]