基于协方差矩阵的B-样条多尺度表示的角点检测

在B.样条尺度空间下定义了平面轮廓在其支撑区域(ROS)内的协方差矩阵的多尺度表示,矩阵的最大特征值对应的向量表示轮廓切线方向.将各个尺度下的切线方向变化率的乘积定义为多尺度积.而角点就定义为多尺度积大于给定阈值的局部极大值所对应的点.由于多尺度乘积融合了轮廓在不同尺度下的特征信息,增强了特征点的响应,抑制了噪声的影响,因而所提出的算法既能精确定位,又对噪声有良好的鲁棒性.通过实验证明了算法具有旋转不变性,并对微小的尺度变化不敏感,而且与其他的角点检测器进行了对比.

Corner Detection Based on B-spline Multi-scale Representation of Covariance Matrix

The multi-scale representation of covariance matrix for planar counter over its Region of Support (ROS) was defined in the framework of B-spline scale space.The eigenvector corresponding to the largest eigenvalue of covariance matrix indicated contour tangent orientation.The multiplication of change rates of contour tangent orientation at different scales was defined as multi-scale product.A local extreme of the product was reported as corner when the value of the product exceeded a threshold.The features of contour at different scales were contained in multi-scale product which enhanced the responses of corners while suppressing the effect of noise,hence the proposed algorithm not only has good localization accuracy,but also has good robustness to noise.Experimental results demonstrate that our algorithm is rotation-invariant and insensitive to slight scale transform.