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.