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Real-time pixel based multiscaleanomaly detection inmultivariate images
KTH, School of Computer Science and Communication (CSC).
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Pixelbaserad multiskalsanomalidetektion imultivariata bilder i realtid (Swedish)
Abstract [en]

This thesis presents a method using machine vision for surface quality control in an

industrial manufacturing process. The method is based on the anomaly detection due

to challenging, large variety and low occurrence rate of possible defects that have to

be identified. The control is performed on a surface of the inspected product using

multiple light sources for better illumination which provides more information about

the exterior conditions. The method itself handles the images taken from the different

light sources and computes a statistical model of the obtained multivariate image at

multiple scales in order to comprehend signaling the possible defects. For the final

classification, a pixel based probability map is used to pinpoint possible defects. The

method has been implemented in Matlab and tested on a sample set provided from a

Swedish furniture factory. Experimental results show interesting properties as well as

an evaluation of the proposed method.

Abstract [sv]

I det här examensarbetet presenteras en metod för optiskt ytavsyning i en industriell

tillverkningsprocess. Metoden baseras på anomalidetektion eftersom defekterna som

ska identifieras förekommer i stor variation och med låg frekvens. Avsyningen baseras

på belysning med flera ljuskällor för att erhålla mer information om ytan. Metoden

använder sig av bilderna tagna från de olika ljuskällor och skapar en statistisk modell av

den erhållna multivariata bilden i multipla skalor för att omfatta de olika defekterna.

För slutlig klassificering används en pixelbaserad sannolikhetskarta. Metoden är

implementerad i Matlab och testad på en provkollektion från en svensk möbelfabrik.

Resultaten visar intressanta egenskaper såväl som en utvärdering av den presenterade


Place, publisher, year, edition, pages
National Category
Computer Science
URN: urn:nbn:se:kth:diva-142370OAI: diva2:699992
Educational program
Master of Science in Engineering - Computer Science and Technology
Available from: 2014-03-12 Created: 2014-03-03 Last updated: 2014-03-12Bibliographically approved

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