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This edited volume addresses a subject which has been discussed
inten sively in the computer vision community for several years.
Performance characterization and evaluation of computer vision
algorithms are of key importance, particularly with respect to the
configuration of reliable and ro bust computer vision systems as
well as the dissemination of reconfigurable systems in novel
application domains. Although a plethora of literature on this
subject is available for certain' areas of computer vision, the re
search community still faces a lack of a well-grounded, generally
accepted, and--eventually-standardized methods. The range of
fundamental problems encoIl passes the value of synthetic images in
experimental computer vision, the selection of a representative set
of real images related to specific domains and tasks, the
definition of ground truth given different tasks and applications,
the design of experimental test beds, the analysis of algorithms
with respect to general characteristics such as complexity,
resource consumption, convergence, stability, or range of
admissible input data, the definition and analysis of performance
measures for classes of algorithms, the role of statistics-based
performance measures, the generation of data sheets with
performance measures of algorithms sup porting the system engineer
in his configuration problem, and the validity of model assumptions
for specific applications of computer vision."
This edited volume addresses a subject which has been discussed
inten sively in the computer vision community for several years.
Performance characterization and evaluation of computer vision
algorithms are of key importance, particularly with respect to the
configuration of reliable and ro bust computer vision systems as
well as the dissemination of reconfigurable systems in novel
application domains. Although a plethora of literature on this
subject is available for certain' areas of computer vision, the re
search community still faces a lack of a well-grounded, generally
accepted, and--eventually-standardized methods. The range of
fundamental problems encoIl passes the value of synthetic images in
experimental computer vision, the selection of a representative set
of real images related to specific domains and tasks, the
definition of ground truth given different tasks and applications,
the design of experimental test beds, the analysis of algorithms
with respect to general characteristics such as complexity,
resource consumption, convergence, stability, or range of
admissible input data, the definition and analysis of performance
measures for classes of algorithms, the role of statistics-based
performance measures, the generation of data sheets with
performance measures of algorithms sup porting the system engineer
in his configuration problem, and the validity of model assumptions
for specific applications of computer vision."
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