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Bildanalys inom Machine Vision: Nyquists samplingsteorem vid digital fotografering
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
2017 (Swedish)Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
Abstract [sv]

Inom Machine Vision är det av stor vikt att kameran har möjlighet att detektera de detaljer som eftersöks. Aliasing är ett problem inom all

digital fotografering och beror på att kamerans upplösning är för låg i förhållande till de detaljer den försöker fånga. Det här arbetet analyserar kamerans begränsningar och orsaken till dessa. En enkel kamerarigg som används till försök inom Machine Vision konstrueras om från grunden för bättre kontroll och upplösning och en ny styrning skapas till denna efter beställarens specifikationer. Ett testmönster för ISO 12233:2000

fotograferas därefter i denna rigg. Resultatet analyseras och jämförs mot Nyquists samplingsteorem med avseende på digital fotografering.

Resultatet visar hur kamerans konstruktion och sätt att registrera färger genom ett filter framför bildsensorn och algoritmer för att beräkna färgen för varje enskild bildpunkt höjer sampelstorleken med en faktor 3 jämfört med det ursprungliga teoremet om dubbla samplingsfrekven-sen.

Abstract [en]

Within Machine Vision, it is very important that the camera can detect the details requested. Aliasing is a problem in all digital photography, and is because the camera's resolution is too low relative to the details it tries to capture. This work analyzes the camera's limitations and the cause of these. A simple camera rig used for Machine Vision tests is re-designed for better control and resolution, and a new control-system is created to this according to the client's specifications. A test pattern for ISO 12233: 2000 is then photographed in this rig. The result is analyzed and compared to Nyquist sampling theorem regarding digital photography. The result shows how the camera's design and way of registering colors through a filter in front of the image sensor and algorithms to calculate the color for each individual pixel increases the sample size by a factor of 3 compared with the original theorem with double sampling frequency.

Place, publisher, year, edition, pages
2017. , p. 34
Keywords [en]
Nyquist’s theory, Sample density, Sample size, Bayerfilter, Aliasing, Moiré
Keywords [sv]
Nyquists teorem, Samplingsdensitet, Sampelstorlek, Bayerfilter, Aliasing, Moaré
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:miun:diva-31469Local ID: ET-V17-G3-013OAI: oai:DiVA.org:miun-31469DiVA, id: diva2:1135333
Subject / course
Electrical Engineering ET2
Educational program
Automationsingenjör TAUMG 180 GR
Supervisors
Examiners
Available from: 2017-08-23 Created: 2017-08-23 Last updated: 2017-08-23Bibliographically approved

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CiteExportLink to record
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