Abstract: Since the traditional SUSAN detector is only appropriate to detect corners in a single scale, a multi-scale SUSAN method on corner detection is presented which is based on Gaussian transform. This method employs the multi-scale property of Gaussian transform to create a Gaussian pyramid by implementing a different scales Gaussian transform to the original digital image. Then an improved SUSAN detector with an adaptive threshold is further employed to gain corner candidates in different multi-scales. Finally, after every candidate is relocated to a certain position in the original image, the real corners are selected with reference to their certain neighborhood information. Experimental results show that this method not only can detect corners effectively in different scales, but also is obviously superior to some existing methods in terms of the misdetection rate and accuracy rate.

Key words: corner detection, Small Univalue Segment Assimilating Nucleus（SUSAN） detector, Gaussian transform, multi-scale analysis

摘要： 针对传统SUSAN算子只能在单一尺度下检测图像中角点的不足，提出一种基于高斯变换的多尺度SUSAN角点检测方法。该方法利用高斯变换获得待检测图像的多尺度分层图像，以构建高斯金字塔，结合自适应阈值的SUSAN算子检测出不同尺度下的角点作为候选角点，将其还原到原始图像中的相应位置构成候选角点集，在候选角点集中经小邻域信息筛选获得最终角点。实验结果表明，该方法不仅能够在不同尺度下有效获取有用的角点信息，而且提高SUSAN算子正确率的同时，降低了角点的伪检率。

关键词: 角点检测, SUSAN算子, 高斯变换, 多尺度分析