改进的数学形态学图像边缘提取算法研究

研究图像边缘优化提取问题。由于图像在进行边缘提取过程中,容易受到外界信息的干扰,特别是当受到噪声等因素影响时,造成图像边缘提取困难。为此提出了一种新的采用两层数学形态学增强操作提取图像边缘的技术。首先将利用形态学对灰度图像进行增强,并以为基础利用形态学的膨胀操作单独对边缘进行增强,然后将图像切分成小块,针对不同的小块来区分边缘与非边缘;最终经过模板滤波,获得清晰的边缘结果图像。仿真结果表明,改进的算法快速有效,在提取完整的边缘的同时,能够有效的抑制噪声和背景因素对边缘的干扰,并优于其他传统边缘提取方法。

Edge Detection Using Morphological Algorithm

The performance of image segmentation depends on the output quality of the edge detection process.Typical edge detecting method is based on detecting pixels in an image with high gradient values,and then applies a global threshotd value to extract the edge points of the image.By these methods,some detected edge points may not belong to the edge and some thin edge points in dark regions of the image are being eliminated.These eliminated edges may contain with important features of the image.This paper proposed a new mathematical morphological edg detecting algorithm based on the morphological residue transformation derived from dilation operation to detect and preserve the thin edges.Moreover,this work adopted five bipolar oriented edge masks to prune the miss detected edge points.The experimental results show that the proposed algorithm is successfull to preserve the thin edges in the dark regions.