A NN image understanding system for maps and animals recognition

The paper presents the structure and design principle of a neural network (NN) image understanding system which is used to recognize and analyze maps and animals with the features of translation-, scale-, and rotation-invariance. The utilized network is nonlinear continuous neural network using its associative memory function. We designed this neural network system using an optimal design method. The feature parameters which are inputted into the system to carry out the recognition task are Zernike moments. Through extensive experimentation with translation, scale, rotation as well as distortion (such as cutting off some parts of the inputted image), we can see this system is a high robustness and fault-tolerance image understanding system.<>