Performance Measure as Feedback Variable in Image Processing

This paper extends the view of image processing performance measure presenting the use of this measure as an actual value in a feedback structure. The idea behind is that the control loop, which is built in that way, drives the actual feedback value to a given set point. Since the performance measure depends explicitly on the application, the inclusion of feedback structures and choice of appropriate feedback variables are presented on example of optical character recognition in industrial application. Metrics for quantification of performance at different image processing levels are discussed. The issues that those metrics should address from both image processing and control point of view are considered. The performance measures of individual processing algorithms that form a character recognition system are determined with respect to the overall system performance.

[1]  M.M. Trivedi,et al.  Performance evaluation of a vision based lane tracker designed for driver assistance systems , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[2]  Peter Waszkewitz,et al.  Industrial Image Processing: Visual Quality Control in Manufacturing , 1999 .

[3]  Sing-Tze Bow,et al.  Pattern recognition and image preprocessing , 1992 .

[4]  Bir Bhanu,et al.  Closed-Loop Object Recognition Using Reinforcement Learning , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Thomas S. Huang,et al.  Image processing , 1971 .

[6]  Ahmed S. Abutableb.  Automatic thresholding of gray-level pictures using two-dimensional entropy , 1989 .

[7]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[8]  Afreen Siddiqi,et al.  Control strategies and image processing , 2003, Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings..

[9]  John A. Marchant,et al.  Testing a measure of image quality for acquisition control , 2002, Image Vis. Comput..

[10]  Peter Corke,et al.  Visual Control of Robots: High-Performance Visual Servoing , 1996 .

[11]  S Bailey,et al.  A methodology for goal oriented image processing and performance evaluation , 1999 .

[12]  Choudhury A. Rahman,et al.  A real time vehicle's license plate recognition system , 2003, Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003..

[13]  Ivan Volosyak,et al.  Improvement of visual perceptual capabilities by feedback structures for robotic system FRIEND , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[14]  Majid Mirmehdi,et al.  Optimising the Complete Image Feature Extraction Chain , 1998, ACCV.

[15]  Henri Maitre.  Entropy, information (and image), part 1 , 1993 .

[16]  Katsuhiko Ogata,et al.  Modern Control Engineering , 1970 .

[17]  Madhukar Pandit,et al.  Active illumination for the segmentation of surface deformations , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[18]  Bernd Jähne,et al.  Practical handbook on image processing for scientific applications , 1997 .

[19]  Joachim Hornegger,et al.  Pattern Recognition and Image Processing in C++ , 1995, Vieweg+Teubner Verlag.

[20]  Gregory D. Hager,et al.  The confluence of vision and control, Block Island Workshop on Vision and Control, June 23-27, 1997, Block Island, Rhode Island, USA , 1998, Block Island Workshop on Vision and Control.

[21]  Josef Kittler,et al.  A Performance Measure for Boundary Detection Algorithms , 1996, Comput. Vis. Image Underst..

[22]  Dov Dori,et al.  A System for Performance Evaluation of Arc Segmentation Algorithms , 2001 .

[23]  Majid Mirmehdi,et al.  Feedback control strategies for object recognition , 1999, IEEE Trans. Image Process..