采用多结构元素模板的形态学边缘检测新算法

边缘检测是图像处理和计算机视觉中的基本问题。为了能有效地捕获目标的主要特征，提出了一种基于Ostu阈值分割和数学形态学的灰度图像边缘检测新算法。利用Ostu算法找到一个最佳的阈值，根据这个阈值把灰度图像二值化，构造四个不同方向的3×3或5×5的结构元素模板，采用数学形态学中的腐蚀算法利用元素模板来腐蚀二值图像。自适应地均衡腐蚀结果，检测出图像边缘。仿真和分析表明同传统边缘检测算法相比新算法运算量小，检测的边缘轮廓清晰，连续性好，可以很好地提取图像中富含的边缘信息，且抗噪声性能较好，假边较少，适用性强。

Novel morphology edge detection algorithm using multiple structuring element templates

The edge detection is the basic problem of image processing and computer vision. In order to capture objects’ main features effectively, this paper puts forward a novel edge detection algorithm using Ostu’s threshold selection method and mathematical morphology for gray-level images. The new algorithm uses Ostu’s method to select a optimal threshold, transforms gray image to binary image based on the threshold, then constructs four structuring elements of 3×3 and 5×5 templates of different directions, uses these templates to erode the binary image based on mathematical morphology. It balances the erosion results adaptively and detects the edge. The results and analysis indicate that the new algorithm is simple and detects a clear and continuous edge, extracts much edge information included in the images, meanwhile, the anti noise performance is better than the classical algorithm as well as false edges are less and it is great applicability.