Jan P. Allebach received his BSEE from the University of Delaware in 1972 and his Ph.D. from Princeton University in 1976. He was on the faculty at the University of Delaware from 1976 to 1983. Since 1983, he has been at Purdue University in the School of Electrical and Computer Engineering. His current research interests include image rendering, image quality, color imaging and color measurement, document management, and wireless applications of imaging and printing. Prof. Allebach has published over 50 articles in refereed journals and over 150 conference papers. The results of his research on image rendering algorithms have been licensed to major vendors of imaging products, and can be found in millions of units that have been sold worldwide.

Prof. Allebach is a member of the IEEE Signal Processing (SP) Society, the Society for Imaging Science and Technology (IS&T), and SPIE. He has been especially active with the IEEE SP Society and IS&T. He is a Fellow of both these societies, has served as Distinguished/Visiting Lecturer for both societies, and has served as an officer and on the Board of Directors of both societies. Prof. Allebach is a past Associate Editor for the IEEE Transactions on Signal Processing and the IEEE Transactions on Image Processing. He is presently Editor for the IS&T/SPIE Journal of Electronic Imaging. He received the Senior (best paper) Award from the IEEE Signal Processing Society and the Bowman Award from IS&T. He has also received several teaching awards at Purdue University.

Quality control by artificial vision plays an important role in a broad range of applications that include manufacturing, agriculture, medicine, military, and consumer products. What do these tasks have in common? The nature of these applications is so diverse that they would seem to defy description by any single model. However, I would argue that there are three critical steps to any quality control task based on artificial vision. The first is image acqusition. The second is image analysis to estimate key parameter values. The third is the use of this information to improve the target process or product. Thus we have essentially a closed loop system. A particularly important point is that in order for artificial vision techniques to be most effective, the overall system must be developed in an integrated manner. In this talk, I will give an overview of quality control by artificial vision in the context of the above framework. I will then describe in some detail specific applications of these concepts to the improvement of both laser (electrophotographic) and inkjet desktop printers.