Novelty detection on metallic surfaces by GMM learning in Gabor space

Yigitcan Savran; Bilge Gunsel

doi:10.1007/978-3-642-13775-4_33

Novelty detection on metallic surfaces by GMM learning in Gabor space

Yigitcan Savran^*, Bilge Gunsel

^*Corresponding author for this work

Department of Electronics and Communication Engineering

Istanbul Technical University

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

3 Citations (Scopus)

Abstract

Defect detection on painted metallic surfaces is a challenging task in inspection due to the varying illuminative and reflective structure of the surface. This paper proposes a novelty detection scheme that models the defect-free surfaces by using Gaussian Mixture Models (GMMs) trained in Gabor space. It is shown that training using the texture representations obtained by Gabor filtering takes the advantage of multiscale analysis while reducing the computational complexity. Test results reported on defected metallic surfaces including pinhole, crater, hav, dust, scratch, and mound type of abnormalities demonstrate the superiority of developed integrated system with respect to the stand alone Gabor filtering as well as the spatial domain GMM classification.

Original language	English
Title of host publication	Image Analysis and Recognition - 7th International Conference, ICIAR 2010, Proceedings
Pages	325-334
Number of pages	10
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-13775-4_33
Publication status	Published - 2010
Event	7th International Conference on Image Analysis and Recognition, ICIAR 2010 - Povoa de Varzim, Portugal Duration: 21 Jun 2010 → 23 Jun 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	6112 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Image Analysis and Recognition, ICIAR 2010
Country/Territory	Portugal
City	Povoa de Varzim
Period	21/06/10 → 23/06/10

Keywords

Gabor Filters
Gaussian Mixtures
Novelty Detection

Access to Document

10.1007/978-3-642-13775-4_33

Cite this

Savran, Y., & Gunsel, B. (2010). Novelty detection on metallic surfaces by GMM learning in Gabor space. In Image Analysis and Recognition - 7th International Conference, ICIAR 2010, Proceedings (PART 2 ed., pp. 325-334). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6112 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-13775-4_33

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abstract = "Defect detection on painted metallic surfaces is a challenging task in inspection due to the varying illuminative and reflective structure of the surface. This paper proposes a novelty detection scheme that models the defect-free surfaces by using Gaussian Mixture Models (GMMs) trained in Gabor space. It is shown that training using the texture representations obtained by Gabor filtering takes the advantage of multiscale analysis while reducing the computational complexity. Test results reported on defected metallic surfaces including pinhole, crater, hav, dust, scratch, and mound type of abnormalities demonstrate the superiority of developed integrated system with respect to the stand alone Gabor filtering as well as the spatial domain GMM classification.",

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Savran, Y & Gunsel, B 2010, Novelty detection on metallic surfaces by GMM learning in Gabor space. in Image Analysis and Recognition - 7th International Conference, ICIAR 2010, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6112 LNCS, pp. 325-334, 7th International Conference on Image Analysis and Recognition, ICIAR 2010, Povoa de Varzim, Portugal, 21/06/10. https://doi.org/10.1007/978-3-642-13775-4_33

Novelty detection on metallic surfaces by GMM learning in Gabor space. / Savran, Yigitcan; Gunsel, Bilge.
Image Analysis and Recognition - 7th International Conference, ICIAR 2010, Proceedings. PART 2. ed. 2010. p. 325-334 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6112 LNCS, No. PART 2).

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

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N2 - Defect detection on painted metallic surfaces is a challenging task in inspection due to the varying illuminative and reflective structure of the surface. This paper proposes a novelty detection scheme that models the defect-free surfaces by using Gaussian Mixture Models (GMMs) trained in Gabor space. It is shown that training using the texture representations obtained by Gabor filtering takes the advantage of multiscale analysis while reducing the computational complexity. Test results reported on defected metallic surfaces including pinhole, crater, hav, dust, scratch, and mound type of abnormalities demonstrate the superiority of developed integrated system with respect to the stand alone Gabor filtering as well as the spatial domain GMM classification.

AB - Defect detection on painted metallic surfaces is a challenging task in inspection due to the varying illuminative and reflective structure of the surface. This paper proposes a novelty detection scheme that models the defect-free surfaces by using Gaussian Mixture Models (GMMs) trained in Gabor space. It is shown that training using the texture representations obtained by Gabor filtering takes the advantage of multiscale analysis while reducing the computational complexity. Test results reported on defected metallic surfaces including pinhole, crater, hav, dust, scratch, and mound type of abnormalities demonstrate the superiority of developed integrated system with respect to the stand alone Gabor filtering as well as the spatial domain GMM classification.

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Savran Y, Gunsel B. Novelty detection on metallic surfaces by GMM learning in Gabor space. In Image Analysis and Recognition - 7th International Conference, ICIAR 2010, Proceedings. PART 2 ed. 2010. p. 325-334. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-13775-4_33

Novelty detection on metallic surfaces by GMM learning in Gabor space

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Keywords

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