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Image reduction operators based on non-monotonic averaging functions
conference contributionposted on 2013-01-01, 00:00 authored by Tim WilkinTim Wilkin
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.
EventIEEE International Conference on Fuzzy Systems (2013 : Hyderabad, India)
PublisherIEEE Computational Intelligence Society
Place of publicationPiscataway, N.J.
Publication classificationE1 Full written paper - refereed
Copyright notice2013, IEEE
Title of proceedingsFUZZ-IEEE 2013 : Proceedings of the IEEE International Conference on Fuzzy Systems
CategoriesNo categories selected