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Implementing Object and Feature Detection Without Compromising the Performance
Linköping University, Department of Computer and Information Science, Software and Systems.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis will cover how some computationally heavy algorithms used in digital image processing and computer vision are implemented with WebGL and computed on the graphics processing unit by utilizing GLSL-shaders. This thesis is based on an already implemented motion detection plug-in used in web based games. This plug-in is enhanced with new features and some already implemented algorithms are improved. The motion detection is based on image subtraction and uses the delta image from previous frames to determine motion.

The plug-in is used in web based games so the performance is of utmost importance since bad performance leads to frustration and less immersion for the players

Techniques brought up are edge detection, Gaussian filter, features from accelerated segment test(FAST) and Harris corner detection. These techniques will be implemented by utilizing the parallel structure of the GPU. Both Harris corner detection and features from accelerated segment test can be run in real time but the result of the Harris corner detection is the better of the two. The thesis will also cover different color spaces, how they are implemented and why they were implemented

Place, publisher, year, edition, pages
2016. , 28 p.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-129276ISRN: LIU-IDA/LITH-EX-A--16/010--SEOAI: oai:DiVA.org:liu-129276DiVA: diva2:937337
Subject / course
Computer science
Available from: 2016-06-17 Created: 2016-06-15 Last updated: 2016-06-17Bibliographically approved

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CiteExportLink to record
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  • apa
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