Perceptually relevant pattern recognition applied to cork quality detection

Beatriz Paniagua; Patrick Green; Mike Chantler; Miguel A. Vega-Rodríguez; Juan A. Gómez-Pulido; Juan M. Sánchez-Pérez

doi:10.1007/978-3-642-02611-9_91

Perceptually relevant pattern recognition applied to cork quality detection

Beatriz Paniagua, Patrick Green, Mike Chantler, Miguel A. Vega-Rodríguez, Juan A. Gómez-Pulido, Juan M. Sánchez-Pérez

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

1 Citation (Scopus)

Abstract

This paper demonstrates significant improvement in the performance of a computer vision system by incorporating the results of an experiment on human visual perception. This system was designed to solve a problem existing in the cork industry: the automatic classification of cork samples according to their quality. This is a difficult problem because cork is a natural and heterogeneous material. An eye-tracker was used to analyze the gaze patterns of a human expert trained in cork classification, and the results identified visual features of cork samples used by the expert in making decisions. Variations in lightness of the cork surface proved to be a key feature, and this finding was used to select the features included in the final system: defects in the sample (thresholding), size of the biggest defect (morphological operations), and four Laws textural features, all working on a Neuro-Fuzzy classifier. The results obtained from the final system show lower error rates than previous systems designed for this application. © 2009 Springer Berlin Heidelberg.

Original language	English
Title of host publication	Image Analysis and Recognition - 6th International Conference, ICIAR 2009, Proceedings
Pages	927-936
Number of pages	10
Volume	5627 LNCS
DOIs	https://doi.org/10.1007/978-3-642-02611-9_91
Publication status	Published - 2009
Event	6th International Conference on Image Analysis and Recognition 2009 - Halifax, Canada Duration: 6 Jul 2009 → 8 Jul 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5627 LNCS
ISSN (Print)	0302-9743

Conference

Conference	6th International Conference on Image Analysis and Recognition 2009
Abbreviated title	ICIAR 2009
Country/Territory	Canada
City	Halifax
Period	6/07/09 → 8/07/09

Keywords

Automated visual inspection system
Cork industry
Eye tracking
Image processing
Perceptual features
Stopper quality
Vision science

Access to Document

10.1007/978-3-642-02611-9_91

Cite this

Paniagua, B., Green, P., Chantler, M., Vega-Rodríguez, M. A., Gómez-Pulido, J. A., & Sánchez-Pérez, J. M. (2009). Perceptually relevant pattern recognition applied to cork quality detection. In Image Analysis and Recognition - 6th International Conference, ICIAR 2009, Proceedings (Vol. 5627 LNCS, pp. 927-936). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5627 LNCS). https://doi.org/10.1007/978-3-642-02611-9_91

Paniagua, Beatriz ; Green, Patrick ; Chantler, Mike et al. / Perceptually relevant pattern recognition applied to cork quality detection. Image Analysis and Recognition - 6th International Conference, ICIAR 2009, Proceedings. Vol. 5627 LNCS 2009. pp. 927-936 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f274c9893ae2475f8c1353602c4d1212,

title = "Perceptually relevant pattern recognition applied to cork quality detection",

abstract = "This paper demonstrates significant improvement in the performance of a computer vision system by incorporating the results of an experiment on human visual perception. This system was designed to solve a problem existing in the cork industry: the automatic classification of cork samples according to their quality. This is a difficult problem because cork is a natural and heterogeneous material. An eye-tracker was used to analyze the gaze patterns of a human expert trained in cork classification, and the results identified visual features of cork samples used by the expert in making decisions. Variations in lightness of the cork surface proved to be a key feature, and this finding was used to select the features included in the final system: defects in the sample (thresholding), size of the biggest defect (morphological operations), and four Laws textural features, all working on a Neuro-Fuzzy classifier. The results obtained from the final system show lower error rates than previous systems designed for this application. {\textcopyright} 2009 Springer Berlin Heidelberg.",

keywords = "Automated visual inspection system, Cork industry, Eye tracking, Image processing, Perceptual features, Stopper quality, Vision science",

author = "Beatriz Paniagua and Patrick Green and Mike Chantler and Vega-Rodr{\'i}guez, {Miguel A.} and G{\'o}mez-Pulido, {Juan A.} and S{\'a}nchez-P{\'e}rez, {Juan M.}",

year = "2009",

doi = "10.1007/978-3-642-02611-9_91",

language = "English",

isbn = "3642026109",

volume = "5627 LNCS",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "927--936",

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note = "6th International Conference on Image Analysis and Recognition 2009, ICIAR 2009 ; Conference date: 06-07-2009 Through 08-07-2009",

}

Paniagua, B, Green, P, Chantler, M, Vega-Rodríguez, MA, Gómez-Pulido, JA & Sánchez-Pérez, JM 2009, Perceptually relevant pattern recognition applied to cork quality detection. in Image Analysis and Recognition - 6th International Conference, ICIAR 2009, Proceedings. vol. 5627 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5627 LNCS, pp. 927-936, 6th International Conference on Image Analysis and Recognition 2009, Halifax, Canada, 6/07/09. https://doi.org/10.1007/978-3-642-02611-9_91

Perceptually relevant pattern recognition applied to cork quality detection. / Paniagua, Beatriz; Green, Patrick; Chantler, Mike et al.
Image Analysis and Recognition - 6th International Conference, ICIAR 2009, Proceedings. Vol. 5627 LNCS 2009. p. 927-936 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5627 LNCS).

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

TY - GEN

T1 - Perceptually relevant pattern recognition applied to cork quality detection

AU - Paniagua, Beatriz

AU - Green, Patrick

AU - Chantler, Mike

AU - Vega-Rodríguez, Miguel A.

AU - Gómez-Pulido, Juan A.

AU - Sánchez-Pérez, Juan M.

PY - 2009

Y1 - 2009

N2 - This paper demonstrates significant improvement in the performance of a computer vision system by incorporating the results of an experiment on human visual perception. This system was designed to solve a problem existing in the cork industry: the automatic classification of cork samples according to their quality. This is a difficult problem because cork is a natural and heterogeneous material. An eye-tracker was used to analyze the gaze patterns of a human expert trained in cork classification, and the results identified visual features of cork samples used by the expert in making decisions. Variations in lightness of the cork surface proved to be a key feature, and this finding was used to select the features included in the final system: defects in the sample (thresholding), size of the biggest defect (morphological operations), and four Laws textural features, all working on a Neuro-Fuzzy classifier. The results obtained from the final system show lower error rates than previous systems designed for this application. © 2009 Springer Berlin Heidelberg.

AB - This paper demonstrates significant improvement in the performance of a computer vision system by incorporating the results of an experiment on human visual perception. This system was designed to solve a problem existing in the cork industry: the automatic classification of cork samples according to their quality. This is a difficult problem because cork is a natural and heterogeneous material. An eye-tracker was used to analyze the gaze patterns of a human expert trained in cork classification, and the results identified visual features of cork samples used by the expert in making decisions. Variations in lightness of the cork surface proved to be a key feature, and this finding was used to select the features included in the final system: defects in the sample (thresholding), size of the biggest defect (morphological operations), and four Laws textural features, all working on a Neuro-Fuzzy classifier. The results obtained from the final system show lower error rates than previous systems designed for this application. © 2009 Springer Berlin Heidelberg.

KW - Automated visual inspection system

KW - Cork industry

KW - Eye tracking

KW - Image processing

KW - Perceptual features

KW - Stopper quality

KW - Vision science

U2 - 10.1007/978-3-642-02611-9_91

DO - 10.1007/978-3-642-02611-9_91

M3 - Conference contribution

SN - 3642026109

SN - 9783642026102

VL - 5627 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 927

EP - 936

BT - Image Analysis and Recognition - 6th International Conference, ICIAR 2009, Proceedings

T2 - 6th International Conference on Image Analysis and Recognition 2009

Y2 - 6 July 2009 through 8 July 2009

ER -

Paniagua B, Green P, Chantler M, Vega-Rodríguez MA, Gómez-Pulido JA, Sánchez-Pérez JM. Perceptually relevant pattern recognition applied to cork quality detection. In Image Analysis and Recognition - 6th International Conference, ICIAR 2009, Proceedings. Vol. 5627 LNCS. 2009. p. 927-936. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-02611-9_91

Perceptually relevant pattern recognition applied to cork quality detection

Abstract

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Conference

Keywords

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