Performance Evaluation in Image Processing
- PDF / 345,464 Bytes
- 3 Pages / 600.03 x 792 pts Page_size
- 16 Downloads / 237 Views
Editorial Performance Evaluation in Image Processing Michael Wirth, Matteo Fraschini, Martin Masek, and Michel Bruynooghe Department of Computing and Information Science, University of Guelph, Guelph, ON, Canada N1G 2W1 Received 3 April 2006; Accepted 3 April 2006 Copyright © 2006 Michael Wirth et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The scanning and computerized processing of images had its birth in 1956 at the National Bureau of Standards (NBS, now National Institute of Standards and Technology (NIST)) [1]. Image enhancement algorithms were some of the first to be developed [2]. Half a century later, literally thousands of image processing algorithms have been published. Some of these have been specific to certain applications such as the enhancement of latent fingerprints, whilst others have been more generic in nature, applicable to all, yet master of none. The scope of these algorithms is fairly expansive, ranging from automatically extracting and delineating regions of interest such as in the case of segmentation, to improving the perceived quality of an image, by means of image enhancement. Since the early years of image processing, as in many subfields of software design, there has been a portion of the design process dedicated to algorithm testing. Testing is the process of determining whether or not a particular algorithm has satisfied its specifications relating to criteria such as accuracy and robustness. A major limitation in the design of image processing algorithms lies in the difficulty in demonstrating that algorithms work to an acceptable measure of performance. The purpose of algorithm testing is two-fold. Firstly it provides either a qualitative or a quantitative method of evaluating an algorithm. Secondly, it provides a comparative measure of the algorithm against similar algorithms, assuming similar criteria are used. One of the greatest caveats in designing algorithms incorporating image processing is how to conceive the criteria used to analyze the results. Do we design a criterion which measures sensitivity, robustness, or accuracy? Performance evaluation in the broadest sense refers to a measure of some required behavior of an algorithm, whether it is achievable accuracy, robustness, or adaptability. It allows the intrinsic characteristics of an algorithm to be emphasized, as well as the evaluation of its benefits and limitations. More often than not though, such testing has been limited in its scope. Part of this is attributable to the actual lack
of formal process used in performance evaluation of image processing algorithms, from the establishment of testing regimes, to the design of metrics. Selection of an appropriate evaluation methodology is dependent on the objective of the task. For example, in the context of image enhancement, requirements are essentially different for screen-based enhancement and enhanc
Data Loading...
