A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
Handwritten digit recognition with a back-propagation network
Advances in neural information processing systems 2
Neural networks and the bias/variance dilemma
Neural Computation
| Hi-index | 0.00 |
In this paper we investigate techniques for embedding 'domain specific' spatial invariances into 'highly constrained' neural networks. This information is used to drastically reduce the number of weights which have to be determined during the learning phase, thus allowing us to apply artificial neural networks to problems characterized by a relatively small number of available examples. As an application of the proposed technique, we study the problem of optical inspection of machined parts. More specifically, we have characterized the performance of a network, created according to this strategy, which accepts images of the parts under inspection at its input and issues at its output a flag which states whether the part is defective. The results obtained so far show that such a classifier provides a potentially relevant approach for the quality control of metallic objects since it offers at the same time accuracy and short software development time.