Performance Measure as Feedback Variable in Image Processing

  • PDF / 1,461,787 Bytes
  • 12 Pages / 600.03 x 792 pts Page_size
  • 16 Downloads / 214 Views

DOWNLOAD

REPORT


Performance Measure as Feedback Variable in Image Processing ¨ Danijela Risti´c and Axel Graser Institute of Automation, University of Bremen, Otto-Hahn-Allee NW1, 28359 Bremen, Germany Received 28 February 2005; Revised 4 September 2005; Accepted 8 November 2005 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. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.

1.

INTRODUCTION

Throughout the development of image processing systems, nearly all research has been dedicated to design of new algorithms or to improvement of existing ones. In the last years, a significant effort also has been devoted to quantitative performance assessment of different image processing methods [1]. In that, image processing algorithms mostly have been considered on their own and developed performance measures have been used to evaluate the effectiveness of individual algorithms or to compare the different image processing algorithms [2, 3]. However, in practice an image processing system consists of serial image processing operations combined differently depending on the overall goal of the vision system. Depending on application, it can happen that a performance measure of an algorithm if considered on its own is not a suitable performance measure if the same algorithm is encapsulated within a larger system. Therefore, it is very important to measure the effectiveness of individual algorithm within a vision system. Recently, some results on performance measures that provide a step to building vision systems that automatically adjust algorithm parameters at each level of the system to improve overall performance were published [4, 5]. In this paper, such kind of performance measure is considered but throughout the consideration of inclusion of control techniques in standard image processing system. The inclusion of closed-loop control is suggested to overcome the problems of standard open-loop image processing. The motivation is the knowledge coming from control

theory, that closed-loop systems have the ability to provide a natural robustness against disturbances and system uncertainty [6]. When control techniques are discussed in connection with image processing, they are usually done so in the context of an