Some Fuzzy Tools for Evaluation of Computer Vision Algorithms

In this article, some issues related to the performance evaluation of computer vision algorithms within the version of direct empirical supervised evaluation method developed at SRISA RAS are considered. This approach partly relies on the elements defined by using the fuzzy set theory, in particular...

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Published inInternational journal of computer vision and image processing Vol. 8; no. 1; pp. 1 - 14
Main Author Osipov, Andrey
Format Journal Article
LanguageEnglish
Published Hershey IGI Global 01.01.2018
Subjects
Algorithms
Biometry
Classification
Computer vision
Face recognition
Fuzzy set theory
Fuzzy sets
Image quality
Image segmentation
Localization
Machine vision
Methods
Performance evaluation
Remote sensing
Sensors
Set theory
Similarity
Similarity measures
Online AccessGet full text
ISSN2155-6997
2155-6989
2155-6997
2155-6989
DOI10.4018/IJCVIP.2018010101

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Abstract In this article, some issues related to the performance evaluation of computer vision algorithms within the version of direct empirical supervised evaluation method developed at SRISA RAS are considered. This approach partly relies on the elements defined by using the fuzzy set theory, in particular, fuzzy similarity measures and fuzzy reference ground truth images. Some known measures of segmentation quality are considered and their extensions, representing the fuzzy similarity measures, are offered. As an example, the author considers an application of fuzzy ground truth images and fuzzy similarity measures, including some newly introduced ones, to the evaluation of face recognition algorithms.
AbstractList In this article, some issues related to the performance evaluation of computer vision algorithms within the version of direct empirical supervised evaluation method developed at SRISA RAS are considered. This approach partly relies on the elements defined by using the fuzzy set theory, in particular, fuzzy similarity measures and fuzzy reference ground truth images. Some known measures of segmentation quality are considered and their extensions, representing the fuzzy similarity measures, are offered. As an example, the author considers an application of fuzzy ground truth images and fuzzy similarity measures, including some newly introduced ones, to the evaluation of face recognition algorithms.
Audience Academic
Author Osipov, Andrey
AuthorAffiliation Scientific-Research Institute of System Analysis, Russian Academy of Sciences (SRISA RAS), Moscow, Russia
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Cites_doi 10.1007/b106267
10.1109/36.843043
10.1155/2010/847680
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10.1016/B978-0-12-775260-0.50006-2
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10.1016/j.cnsns.2005.07.005
10.1155/2009/184617
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10.1016/j.cviu.2007.08.003
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10.1016/j.patrec.2004.09.024
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Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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SubjectTerms Algorithms
Biometry
Classification
Computer vision
Face recognition
Fuzzy set theory
Fuzzy sets
Image quality
Image segmentation
Localization
Machine vision
Methods
Performance evaluation
Remote sensing
Sensors
Set theory
Similarity
Similarity measures
Title Some Fuzzy Tools for Evaluation of Computer Vision Algorithms
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