Automatic thresholding of gray-level pictures using two-dimensional entropy
Computer Vision, Graphics, and Image Processing
A performance measure for boundary detection algorithms
Computer Vision and Image Understanding
Modern control engineering (3rd ed.)
Modern control engineering (3rd ed.)
Closed-Loop Object Recognition Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optical character recognition
Visual Control of Robots: High-Performance Visual Serving
Visual Control of Robots: High-Performance Visual Serving
Practical Handbook on Image Processing for Scientific Applications
Practical Handbook on Image Processing for Scientific Applications
The Confluence of Vision and Control
The Confluence of Vision and Control
Handbook of Computer Vision and Applications with Cdrom
Handbook of Computer Vision and Applications with Cdrom
Industrial Image Processing: Visual Quality Control in Manufacturing
Industrial Image Processing: Visual Quality Control in Manufacturing
Optimising the Complete Image Feature Extraction Chain
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume II
A Real Time Vehicle's License Plate Recognition System
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Improvement of visual perceptual capabilities by feedback structures for robotic system FRIEND
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Feedback control strategies for object recognition
IEEE Transactions on Image Processing
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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.