EURASIP Journal on Applied Signal Processing

  Special Issue on

  Performance Evaluation in Image Processing

  Call for Papers

The task of analyzing the results of an algorithm through testing is an essential
qualification of algorithm design. 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 twofold. Firstly, it provides eithera 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 criteria
which measure 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 evaluation
of its benefits and limitations.

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 enhancement which
is embedded within a subalgorithm. Screen-based enhancement is usually assessed
in a subjective manner, whereas when an algorithm is encapsulated within a larger
system, subjective evaluation is not available, and the algorithm itself must
determine the quality of a processed image. Very few approaches to the evaluation
of image processing algorithms can be found in the literature, although the concept
has been around for decades. A significant difficulty which arises in the evaluation
of algorithms is finding suitable metrics which provide an objective measure
of performance. A performance metric is a meaningful and computable measure used
for quantitatively evaluating the performance of any algorithm. There is no single
quantitative metric which correlates well with image quality as perceived by the
human visual system. The process of analyzing failure is intrinsically coupled with
the process of performance evaluation. In order to ascertain whether an algorithm
fails or not, the characteristics of success have to be defined. Failure analysis
is the process of determining why an algorithm fails during testing. The knowledge
generated is then fed back to the design process in order to engender refinements
in the algorithm. The goal of this special issue is to present an overview of current
methodologies related to performance evaluation, performance metrics, and failure
analysis of image processing algorithms.

This special issue will focus on such seamless integration of visual analysis
methods in, or joint design with, robust compression and transmission solutions.

Topics of interest include (but are not limited to):

    o Performance metrics for image processing, e.g. contrast enhancement and
      image segmentation
    o The use of performance indicators, e.g. robustness, accuracy, etc.
    o Failure assessment and postmortem analysis in algorithm testing
    o Performance evaluation methodologies
    o Intra-algorithm performance evaluation
    o Methods of reproducible qualitative assessment
    o Performance evaluation of image processing algorithms in applications
      such as medicine, biology, forensics, food industry, etc.
    o The use of ground truth data

Authors should follow the EURASIP JASP manuscript format
described at the journal site http://asp.hindawi.com/
Prospective authors should submit an electronic copy of their
complete manuscript through the EURASIP JASP's manuscript
tracking system at journal's web site, according
to the following timetable.

  Manuscript Due            March 1, 2005
  Acceptance Notification   July 1, 2005
  Final Manuscript Due      November 1, 2005
  Publication Date          1st Quarter, 2006

GUEST EDITORS:

Michael Wirth, Department of Computing and Information Science,
University of Guelph, Guelph, Ontario, Canada N1G 2W1;
mwirth@cis.uoguelph.ca

Matteo Fraschini, Medical Science Department, University of Cagliari,
09124 Cagliari, Italy; fraschin@unica.it

Martin Masek, Centre for Intelligent Information Processing Systems,
School of Electrical, Electronic and Computer Engineering,
The University of Western Australia, Crawley, WA 6009, Australia;
masek-m@ee.uwa.edu.au

Michel Bruynooghe, Laboratory of Photonics Systems, University
Louis Pasteur of Strasbourg, D-76185 Karlsruhe, Germany;
Michel.Bruynooghe@t-online.de

Chandrasekhar, Centre for Intelligent Information Processing Systems,
School of Electrical, Electronic & Computer Engineering,
The University of Western Australia, Crawley, WA 6009, Australia;
chandra@ee.uwa.edu.au

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