基于DOG模型的线条检测算法

DOG Model-Based Algorithm of Line Detection

在计算机视觉领域 ,线条检测是图像低层次处理中一个重要的问题 文中提出一种基于双高斯差 (DOG)模型的线条检测算法 分析比较了DOG检测器和高斯及高斯 拉普拉斯二阶微分检测器在线条检测方面的性能 ,解释了DOG函数和高斯及高斯 拉普拉斯二阶微分函数之间的关系 ,还给出了DOG检测器的线条检测范围并在二维空间中分析了其线条检测机理 实验中选用了指纹图像 。

Line detection is an important low-level operation in computer vision that has many applications. This paper proposes a new difference of Gaussian(DOG) model-based line detection algorithm in which its performance is compared with that of Gaussian kernels-based detector, and explains the relation between DOG function and Gaussian kernels. Furthermore, the line detection range and 2D mechanism of the presented new detector is discussed. Experiment result in fingerprint image shows that DOG model-based detector is able to extract the skeleton of line with a certain width.