为了有效和正确地反映物体的形状特征,构造了一种基于物体轮廓曲线的新的不变量,即高斯描绘子。构造思路主要有两点:第一,定义一个具有平移、尺度不变性的函数——高斯势函数;第二,分别计算高斯势函数在8个同心圆上的平均值,从而获得8个不变量。相对其他的形状特征描述法,这些不变量不仅关于平移、旋转、尺度和反射不变,而且具有识别/匹配率高、计算量小、对适度的边缘变动和噪声不敏感以及适用范围广等优点。将这8个不变量应用于物体识别,实验结果表明,高斯描绘子的确具备上述优点。

Object recognition based on contour is a challenging problem in the field of pattern recognition and computer vision.This paper proposes a new attractive method that can be used in this field.Object features based on contour,called Gaussian descriptors,are introduced and their applications to object shape recognition are provided.The method includes defining a Gaussian Potential Function(GPF) and the reafter constructing 8 Gaussian descriptors by calculating the averages of GPF's along 8 circles with the same center.Gaussian descriptors are invariant to translation,rotation,scaling changes and reflection.Moreover,compared with the existing shape features,they are more robust against noise and slight edge variations,have lower computation complexity and higher recognition/retrieval rate,and are application-independent.These properties and advantages of Gaussian descriptors are studied and discussed,and numerical experiments are implemented to show encouraging results and illustrate the promising performance of Gaussian descriptors.