一种基于Zernike矩的局部特征检测方法

Zernike Moment Based Approach for Local Feature Detection

针对图像发生几何或质量畸变时局部特征区域提取效果不理想的问题,提出了一种基于Zernike矩的具有旋转不变性与尺度不变性的图像局部特征检测算子。该算法利用Zernike矩构建Hessian矩阵,以基于Zernike矩的Hessian矩阵的行列式与迹确定潜在兴趣点的位置,使用非极大值抑制获得多尺度模板下的最大角点响应,再经二维二次插值运算精确定位兴趣点位置,最后利用主曲率进行边缘响应抑制,利用梯度方向直方图确定兴趣点主方向,由兴趣点4×4邻域的8个方向构建描述算子。实验结果表明,该特征检测方法在视角变换、旋转缩放、图像模糊、图像压缩以及光照变化等图像畸变条件下是有效的,且具有良好的抗噪性能。

In order to obtain local feature region with better robustness against image geometric or quality deformation,a novel local feature detector based on Zernike moment with rotation invariance and scale invariance was proposed.For an input image,a Hessian matrix derived by Zernike moments(ZM-Hessian)is used to detect interest points.Firstly,the interest points are located by the difference of the determinate and trace of the ZM-Hessian matrix approximately.Then,the non-maximum-suppression method is applied to capture maximum corner response under multi-scale masks.After that,2D parabolic interpolation is employed to locate the interest points precisely at the sub-pixel level.At last,principal curvature is employed to eliminate edge points.Gradient histogram is employed to obtain dominant orientation.The vector of descriptors are constructed by 4-by-4 neighbors’8 directions of interest points.The proposed detector was compared with other traditional detectors based on Mikolajczyk’s framework.Experiment results prove that the proposed method is effective under various image deformations such as angle transformation,rotation&zoom,image blur,image compression,illumination change,and has good anti-noise performance.