Although previous studies using artificial neural networks have been actively applied to object shape recognition, little attention has been paid to the recognition of spatial elements (e.g. position, rotation and size). In the present study, a rotation and size spreading associative neural network (RS-SAN net) is proposed and the efficacy of the RS-SAN net in object orientation (rotation), size and shape recognition is shown. The RS-SAN net pays attention to the fact that the spatial recognition system in the brain (parietal cortex) is involved in both the spatial (e.g. position, rotation and size) and shape recognition of an object. The RS-SAN net uses spatial spreading by spreading layers, generalized inverse learning and population vector methods for the recognition of the object. The information of the object orientation and size is spread by double spreading layers which have similar tuning characteristics to spatial discrimination neurons (e.g. axis orientation neurons and size discrimination neurons) in the parietal cortex. The RS-SAN net simultaneously recognizes the size of the object irrespective of its orientation and shape, the orientation irrespective of its size and shape, and the shape irrespective of its size and orientation.

A new neural network system for object recognition is proposed which is invariant to translation, scaling and rotation. The system consists of two parts. The first is a preprocessor which obtains projection from the input image plane such that the projection features are translation and scale invariant, and then adopts the Rapid Transform which makes the transformed outputs rotation invariant. The second part is a neural net classifier which receives the outputs of preprocessing part as the input signals. The most attractive feature of this system is that, by using only a simple shift invariant transformation (Rapid transformation) in conjunction with the projection of the input image plane, invariancy is achieved and the system is of reasonably small size. Experiments with six geometrical objects with different degrees of scaling and rotation shows that the proposed system performs excellent when the neural net classifier is trained by the Cascade-correlation learning algorithm proposed by Fahlman and Lebiere.

To investigate necessary conditions for the object recognition by simulations using neural network models is one of ways to acquire suggestions for understanding the neuronal representation of objects in the brain. In the present study, we trained a three layered neural network to form a geometrical feature representation in its output layer using back-propagation algorithm. After training using 73 learning examples, 65 testing patterns made by various combinations of above features could be recognized with the network at a rate of 95.3% appropriate response. We could classify four types of hidden layer units on the basis of effects on the output layer.