COMPARISON OF IMAGE-PROCESSING ALGORITHMS AND NEURAL NETWORKS IN MACHINE VISION INSPECTION

Automated vision inspection has become a vital part of the computer integrated manufacturing systems. This paper compares the development and performance of two methodologies for a machine vision inspection system. The first method is developed through conventional image processing algorithms and the second method is based on the neural networks. A case study was conducted to benchmark these two methods. The results showed that the conventional image processing algorithms required less development time than the neural networks. A considerable amount of time was spend on training the neural networks. However, the neural networks performed better than the conventional image processing algorithms in term of accuracy.