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Evaluation of methods for segmentation of 3D range image data
Linköping University, Department of Electrical Engineering, Computer Vision.
2011 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Utvärdering av metoder för segmentering av 3D-data (Swedish)
Abstract [en]

3D cameras delivering height data can be used for quality inspection of goods on a conveyor.

It is then of interest to distinguish the important parts of the image from background and noise and further to divide these interesting parts into segments that have a strong correlation to objects on the conveyor belt.

Segmentation can easily be done by thresholding in the simple case. However, in more complex situations, for example when objects touch or overlap, this does not work well.

In this thesis, research and evaluation of a few different methods for segmentation of height image data are presented. The focus is to find an accurate method for segmentation of smooth irregularly shaped organic objects such as vegetables or shellfish.

For evaluative purposes a database consisting of height images depicting a variety of such organic objects has been collected.

We show in the thesis that a conventional gradient magnitude method is hard to beat in the general case. If, however, the objects to be segmented are heavily non-convex with a lot of crests and valleys within themselves one could be better off choosing a normalized least squares method.

Abstract [sv]

3D-kameror som levererar höjddata kan användas för kvalitetskontroll av varor på ett löpande band.

Det är då av intresse att urskilja de viktiga delarna av bilden från bakgrund och brus samt även att dela upp dessa intressanta delar i segment med stark korrelans till objekten på bandet.

Segmentering kan utföras genom tröskling i det enkla fallet. I mer komplexa situationer då objekt vidrör eller överlappar varandra blir det svårare.

I detta examensarbete presenteras forskning och utvärdering av några olika metoder för segmentering av höjdbildsdata. Fokus ligger på att finna en noggrann metod för segmentering av mjuka släta oregelbundna objekt som grönsaker och skaldjur.

I utvärderingssyfte har en databas bestående höjdbilder föreställande lite olika typer av sådana organiska objekt samlats in.

Vi visar i uppstatsen att en konventionell gradientlängdsmetod är svår att slå i det generella fallet. Om objekten som ska segmenteras är kraftigt icke-konvexa å andra sidan, med en mängd krön och dalar inom varje objekt, kan man göra bättre i att välja en normaliserad minstakvadratfelsmetod.

Place, publisher, year, edition, pages
2011. , 55 p.
Keyword [en]
range image, height image, 3D, segmentation, surface
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-64657ISRN: LiTH-ISY-EX--11/4346--SEOAI: oai:DiVA.org:liu-64657DiVA: diva2:393236
Presentation
2011-01-18, Algoritmen, Linköpings Universitet, Linköping, 10:15 (Swedish)
Uppsok
Technology
Supervisors
Examiners
Available from: 2011-02-03 Created: 2011-01-29 Last updated: 2011-02-03Bibliographically approved

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CiteExportLink to record
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