一种随机角点检测算法

A Randomized Corner Detection Algorithm

角点特征没有参数化的描述方式,无法应用传统的Hough变换转换到参数空间进行检测.文中提出一种Monte Carlo框架下的随机角点检测方法,不是在参数空间中求解,而是将角点检测转换为交点累积空间中寻找局部极值的问题.交点累积空间是根据角点实质是直线交点的特征提出的一种概念.文中证明了算法的思想,推导了算法的具体步骤.本算法具有各向同性,对图像的旋转是鲁棒的,且对噪声不敏感,并可有效地避免斜边上伪角点的影响.大量实验表明,与Harris算法、Shen&Wang算法、SIFT特征等算法相比较,该算法具有一定的优越性.

There is no parametric formulation of comer feature. Therefore, the conventional Hough transform can not be employed to transform the comer detection into maximum search in parametric space. A randomized Hough transform in Monte Carlo framework is presented, which detects the comer by searching for the local maximum in the intersection point cumulative space instead of parametric space. The intersection point cumulative space is a concept based on the fact that the comer is the intersection point of two lines. The proposed algorithm is demonstrated and the computing procedures are given. The proposed algorithm is isotropic, robust to image rotation, insensitive to noise and not susceptible to diagonal edge. Experimental results show that it outperforms Harris detector, Shen ＆ Wang algorithm, and SIFT feature detection algorithm.