Decision-theoretic approach to visual inspection using neural networks

The Bayesian real-time network (BARTIN) is applied to solving a visual-inspection problem requiring translation, rotation and scale (TRS) invariance. The system is capable of classifying n-fold symmetric engineering parts from near-axial views which may contain more than one part. It is evaluated and compared with other approaches using real visual-inspection data. A novel TRS-invariant preprocessor, the polygon transform, which is optimised for near-circular objects, provides information about the line and circle structure in two-dimensional images. An integral part of the polygon transform is a new Hough transform for circle radii used for both scale invariance and image characterisation. The BARTIN formalism is presented from the viewpoint of subjective Bayesian analysis, and this approach demonstrates how the personal probabilities and utilities of BARTIN can be used to optimise an externally provided reward function. A method is given for adjusting the global level of caution. To handle sparse training data, parameter parsimony in the observer was achieved using a structure comprising a stripped-out Parzen-windows classifier followed by a softmax perceptron trim. For real-time operation, the system is initialised by pretraining it using data extracted from design drawings