Flexible industrial inspection of surface defects using a transputer image-processing system

This paper describes an image-based inspection system for surface defects, implemented on a transputer system with a high- performance transfer bus to provide fast access to large blocks of data. Data-level parallelism (the image pixel data is partitioned horizontally into slices) and task-level parallelism (the algorithms themselves can be parallelized) are utilized. Surface defects and anomalies are detected by a texture-based segmentation procedure. In the training phase the objects of interest are marked and all feature vectors implemented in the system are computed. The system uses simple statistical features, features calculated from the cooccurrence matrix, features based on texture spectrum and the fractal dimension. The time critical run phase is realized on a parallel computer. The implementation is done fully in software, allowing flexibility in the use of features and window sizes for pixel descriptors and freedom in the use of various classifier-methods. The processing speed of the segmentation system is easily scalable with the number of processing units. The performance of the system is demonstrated on an industrial visual inspection task, the recognition of surface defects of aluminium cast workpieces, where a connectionist classifier is used for pixel classification.