【摘要】 在图像测量等工程应用中,需要获得目标的高精度图像边缘信息.本文首先介绍了一种基于Facet模型的亚像素边缘检测算法,该方法具有抗噪能力强,定位精度高等优点,但是计算复杂度太高.针对此缺点,本文研究了一种改进算法,将其与Mallat的小波变换模极大算法有效的结合,不仅处理速度提高了10倍左右,而且所提取的边缘效果也有所改善.实验结果表明,本文的方法不仅能获得高精度的边缘信息,抗噪能力强,而且处理速度快.更多还原

【Abstract】 In the applications such as image measurement,edge information in high accuracy of the object is required.First,a sub-pixel edge detection method based on Facet model is introduced,the method can reduce noise and achieve high accuracy,but its computational complexity is too high.Aimed at making up this disadvantage,we studied an improved method,which combined the Facet model and Mallat’s maximum wavelet module approach effectively.The wavelet method is used to extract wide preparatory edge,while restrain some noise.Then the method based on Facet model merely processes preparatory edge points and further obtains sub-pixel edge.Experiments show that the improved method not only increases the speed of Facet model about 10 times,but also provides more continue accurate edge and reduces noise.更多还原