基于Zernike矩的模糊与仿射混合不变量研究

Study on Blur and Affine Combined Invariants Based on Zernike Moment

Zernike矩作为形状描述子,其信息冗余度低且对噪声不敏感,在图像特征提取和模式识别中得到了广泛应用。为提高Zernike矩对含有模糊和仿射图像的形状描述能力,提出一种基于Zernike矩的形状描述子,该描述子使用规范化方法构造Zernike矩的仿射不变量,结合Zernike矩的模糊不变量得到Zernike矩的模糊和仿射混合不变量。将该矩混合不变量作为形状描述子描述图像的形状特征,并与几何矩模糊和仿射混合不变量进行对比实验,结果表明,Zernike矩的模糊和仿射混合不变量在混合形变下形状描述能力较强,具有不变性,并且对噪声的鲁棒性较好。

Zernike moment,as a shape descriptor,has been widely used in image characteristics extraction and pattern recognition. It is low information redundancy and not sensitive to noise. To improve the shape description capability of the images which are degraded by combined blur and affine transformation,a new shape descriptor based on Zernike moment is proposed. The normalization method is used to construct affine invariants of Zernike moment. The combined blur and affine moment invariants of Zernike moment is achieved by the help of the blur invariants. The combined moment invariants is used as the shape descriptor to describe the shape feature of images,and is implemented comparison with the combined affine and blur invariants based on geometric moment with relative error. Experimental results show that the combined blur and affine invariants of Zernike moment can get better shape description and invariance in combined degrades,and robustness to noise.