图像规格化的一种新方法

图像规格化是图像理解系统中的一种常用方法。通常来讲,二维模式有四种失真形式:平移、旋转、伸缩和歪斜。本文提出了一种新的图像规格化方法,该方法对上述四种形式的失真图像都能正确规格化,而已有的规格化算法至多只能处理三种形式的失真。本算法首先计算给定模式的协方差矩阵,然后根据协方差矩阵的特征向量旋转模式,并根据特征值沿特征向量伸缩模式。这时,模式已变换为最紧凑形式。经过上述处理后,结果模式对平移、伸缩和歪斜失真都是不变的。但是,旋转问题没有解决。本算法的最后一步是根据“图像椭圆倾角”旋转图像以使其对旋转变换也保持不变。这样,结果模式对平移、伸缩、旋转和歪斜变换都是不变的。对飞机图像的实验验证了这种新型的图像规格化方法的正确性和有效性。

A NEW MEIHOD OF IMAGE NORMALIZATION

Image normalization is very useful in image understanding systems. In general, there are four basic forms of distortion in the recognition of planar patterns .-translation, rotation, scaling and skew. In this paper, we provide a new image normalization technique which basically solves all the above problems in image normalization. In the algorithm, we first compute the covariance matrix of a given pattern.Then we rotate the pattern according to the eigenvectors of the covariance matrix,and scale the pattern along the two eigenvectors according to the eigenvalues.Thus we bring the pattern to its most compact form. After the process,the pattern is invariant to translation,scaling and skew.Only the rotation problem remains unsolved. In order to make the pattern invariant to rotation,the last step of our image normalization method is to rotate the image with respect to the image ellipse tilt angle, which is determined by the three second - order central moments. Finally, the resulting pattern is invariant to translation, rotation, scaling and skew. The correctness and effectiveness of the proposed image normalization method are approved by numerical experiments in aircraft images.