轮廓矩不变量及其在物体形状识别中的应用

为了有效地刻画物体的形状特征，在基于区域的Hu矩不变量的基础上，构造了一种基于物体轮廓曲线的新的矩不变量，即轮廓矩不变量。该不变量不仅独立于物体本身的颜色和灰度级，而且具有平移、旋转和尺度不变性，因此可将轮廓矩不变量应用于物体形状的识别，为了能快速地进行物体形状识别，还讨论了小波边缘检测和轮廓的获取问题及其算法。实验表明，基于这种轮廓矩的识别算法具有很好的识别率。

Contour-based Moment Invariants and Their Application to the Recognition of Object Shapes

Object recognition is a challenging problem in the field of pattern recognition and computer vision. Hu's moments are classical tool in the field, which are defined based on the colors or gray levels of objects. This paper is an improvement of Hu's moments. A series of novel moments, which are called contour moments, are constructed based on object contours and their applications to object shape recognition are given in this paper. Some properties of these new moments including the invariance on shift, rotation and scale transforms are studied and proved. A central advantage of the new moments over Hu's moments is that they are independent of the colors or gray levels of objects. They are defined completely by the contours of objects, namely, that they are completely the shape features of objects. To support our new theory, an algorithm for object shape recognition is designed based on the new moments and experiments are conducted. In our experiments, wavelet transforms are employed to extract the contours of objects, therefore, a brief introduction on the theory of wavelet transform as a multi scale edge detector is introduced. Considering that an object may have more than one contour, each of which is a close curve, this paper also gives detailed discussion on how to deal with several contours. Experiments give an encouraging high recognition rates.