Image reduction operators based on non-monotonic averaging functions

Wilkin, Tim 2013, Image reduction operators based on non-monotonic averaging functions, in FUZZ-IEEE 2013 : Proceedings of the IEEE International Conference on Fuzzy Systems, IEEE Computational Intelligence Society, Piscataway, N.J..

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Title Image reduction operators based on non-monotonic averaging functions
Author(s) Wilkin, Tim
Conference name IEEE International Conference on Fuzzy Systems (2013 : Hyderabad, India)
Conference location Hyderabad, India
Conference dates 7-10 Jul. 2013
Title of proceedings FUZZ-IEEE 2013 : Proceedings of the IEEE International Conference on Fuzzy Systems
Editor(s) [Unknown]
Publication date 2013
Conference series IEEE International Conference on Fuzzy Systems
Total pages 8
Publisher IEEE Computational Intelligence Society
Place of publication Piscataway, N.J.
Keyword(s) aggregation function
face recognition
image de-noising
image reduction
penalty function
Summary Image reduction is a crucial task in image processing, underpinning many practical applications. This work proposes novel image reduction operators based on non-monotonic averaging aggregation functions. The technique of penalty function minimisation is used to derive a novel mode-like estimator capable of identifying the most appropriate pixel value for representing a subset of the original image. Performance of this aggregation function and several traditional robust estimators of location are objectively assessed by applying image reduction within a facial recognition task. The FERET evaluation protocol is applied to confirm that these non-monotonic functions are able to sustain task performance compared to recognition using nonreduced images, as well as significantly improve performance on query images corrupted by noise. These results extend the state of the art in image reduction based on aggregation functions and provide a basis for efficiency and accuracy improvements in practical computer vision applications.
ISBN 9781479900220
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30060787

Document type: Conference Paper
Collection: School of Information Technology
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