Increasing Classification Robustness with Adaptive Features

Christian Eitzinger; Manfred Gmainer; Wolfgang Heidl; Edwin Lughofer

doi:10.1007/978-3-540-79547-6_43

Increasing Classification Robustness with Adaptive Features

Christian Eitzinger, Manfred Gmainer, Wolfgang Heidl, Edwin Lughofer

Institute for Mathematical Methods in Medicine and Data Based Modeling

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

In machine vision features are the basis for almost any kind of high-level postprocessing such as classification. A new method is developed that uses the inherent flexibility of feature calculation to optimize the features for a certain classification task. By tuning the parameters of the feature calculation the accuracy of a subsequent classification can be significantly improved and the decision boundaries can be simplified. The focus of the methods is on surface inspection problems and the features and classifiers used for these applications.

Original language	English
Title of host publication	Proc. International Conference on Computer Vision Systems 2008
Editors	A. Gasteratos and M. Vincze and J.K. Tsotsos
Place of Publication	Berlin
Publisher	Springer
Pages	445-453
Number of pages	9
Volume	5008
ISBN (Print)	3540795464, 9783540795469
DOIs	https://doi.org/10.1007/978-3-540-79547-6_43
Publication status	Published - 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5008 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Fields of science

101 Mathematics
101004 Biomathematics
101027 Dynamical systems
101013 Mathematical logic
101028 Mathematical modelling
101014 Numerical mathematics
101020 Technical mathematics
101024 Probability theory
102001 Artificial intelligence
102003 Image processing
102009 Computer simulation
102019 Machine learning
102023 Supercomputing
202027 Mechatronics
206001 Biomedical engineering
206003 Medical physics
102035 Data science

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10.1007/978-3-540-79547-6_43

Cite this

Eitzinger, C., Gmainer, M., Heidl, W., & Lughofer, E. (2008). Increasing Classification Robustness with Adaptive Features. In A. Gasteratos and M. Vincze and J.K. Tsotsos (Ed.), Proc. International Conference on Computer Vision Systems 2008 (Vol. 5008, pp. 445-453). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5008 LNCS). Springer. https://doi.org/10.1007/978-3-540-79547-6_43

Eitzinger, Christian ; Gmainer, Manfred ; Heidl, Wolfgang et al. / Increasing Classification Robustness with Adaptive Features. Proc. International Conference on Computer Vision Systems 2008. editor / A. Gasteratos and M. Vincze and J.K. Tsotsos. Vol. 5008 Berlin : Springer, 2008. pp. 445-453 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Increasing Classification Robustness with Adaptive Features",

abstract = "In machine vision features are the basis for almost any kind of high-level postprocessing such as classification. A new method is developed that uses the inherent flexibility of feature calculation to optimize the features for a certain classification task. By tuning the parameters of the feature calculation the accuracy of a subsequent classification can be significantly improved and the decision boundaries can be simplified. The focus of the methods is on surface inspection problems and the features and classifiers used for these applications.",

author = "Christian Eitzinger and Manfred Gmainer and Wolfgang Heidl and Edwin Lughofer",

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Eitzinger, C, Gmainer, M, Heidl, W & Lughofer, E 2008, Increasing Classification Robustness with Adaptive Features. in A. Gasteratos and M. Vincze and J.K. Tsotsos (ed.), Proc. International Conference on Computer Vision Systems 2008. vol. 5008, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5008 LNCS, Springer, Berlin, pp. 445-453. https://doi.org/10.1007/978-3-540-79547-6_43

Increasing Classification Robustness with Adaptive Features. / Eitzinger, Christian; Gmainer, Manfred; Heidl, Wolfgang et al.
Proc. International Conference on Computer Vision Systems 2008. ed. / A. Gasteratos and M. Vincze and J.K. Tsotsos. Vol. 5008 Berlin: Springer, 2008. p. 445-453 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5008 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

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AB - In machine vision features are the basis for almost any kind of high-level postprocessing such as classification. A new method is developed that uses the inherent flexibility of feature calculation to optimize the features for a certain classification task. By tuning the parameters of the feature calculation the accuracy of a subsequent classification can be significantly improved and the decision boundaries can be simplified. The focus of the methods is on surface inspection problems and the features and classifiers used for these applications.

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Eitzinger C, Gmainer M, Heidl W, Lughofer E. Increasing Classification Robustness with Adaptive Features. In A. Gasteratos and M. Vincze and J.K. Tsotsos, editor, Proc. International Conference on Computer Vision Systems 2008. Vol. 5008. Berlin: Springer. 2008. p. 445-453. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-79547-6_43

Increasing Classification Robustness with Adaptive Features

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