A general architecture for detecting and analyzing surface defects in aluminum strip is described.

Information concerning visual information from the aluminum surface, surface temperature and strip dimensions-profile thickness- is processed jointly by means of an expert system in order to determine the quality level ofeach aluminum coil produced; control actions over the casting process, derived from this information, are also

[1]  C. S. George Lee,et al.  Surface texture dependence on surface roughness by computer vision , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[2]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[3]  Roy Hall,et al.  Illumination and Color in Computer Generated Imagery , 1988, Monographs in Visual Communication.

[4]  R.M. Haralick,et al.  Statistical and structural approaches to texture , 1979, Proceedings of the IEEE.

[5]  E. Patrick,et al.  Fundamentals of Pattern Recognition , 1973 .

[6]  Herbert Freeman,et al.  Machine Vision for Inspection and Measurement , 1989 .

[7]  Anil K. Jain,et al.  Real-Time Object Measurement and Classification , 1988, NATO ASI Series.

[8]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[9]  Roland T. Chin,et al.  Automated visual inspection: 1981 to 1987 , 1988, Computer Vision Graphics and Image Processing.

[10]  Donald W. Braggins.  Computer Vision for Industry , 1993 .

[11]  Jerome A. Feldman,et al.  Some Decidability Results on Grammatical Inference and Complexity , 1972, Inf. Control..