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.