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MACHALÍK, S. JURÁNEK, R. ZEMČÍK, P.
Originální název
Research of Imgae Features for Classification of Wear Debris
Typ
článek v časopise - ostatní, Jost
Jazyk
angličtina
Originální abstrakt
The wear debris of various engineering equipment (such as combustion engines, gearboxes, etc.) consists of particles of metal which can be obtained from lubricants used in such machine parts. The analysis the wear particles is very important for early detection and prevention of failures in engineering equipment. The analysis is often done through classification of individual wear particles obtained by analytical ferrography. In this paper, we present a study of feature extraction methods for a classification of the wear particles based on visual similarity (using supervised machine learning). The main contribution of the paper is the comparison of nine selected feature types in the context of three state-of-the-art learning models. Another contribution is the large public database of binary images of particles which can be used for further experiments.
Klíčová slova
Wear Debris, Classification, Supervised Machine Learning, SVM, Linear Regression,Features, PCA, HOG, LBP
Autoři
MACHALÍK, S.; JURÁNEK, R.; ZEMČÍK, P.
Rok RIV
2012
Vydáno
1. 2. 2012
ISSN
1230-0535
Periodikum
Machine Graphics and Vision
Ročník
20
Číslo
1
Stát
Polská republika
Strany od
479
Strany do
493
Strany počet
15
BibTex
@article{BUT91470, author="Stanislav {Machalík} and Roman {Juránek} and Pavel {Zemčík}", title="Research of Imgae Features for Classification of Wear Debris", journal="Machine Graphics and Vision", year="2012", volume="20", number="1", pages="479--493", issn="1230-0535" }