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Putting things into context: segmenting photographs based on hand-drawn lines
KTH, School of Computer Science and Communication (CSC).
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Att sätta saker i sitt sammanhang: Segmentering av bilder utifrån handritade linjer (Swedish)
Abstract [en]

This report presents a method for finding areas of interest in an image, based on lines drawn in that image. The method is designed to work with photographic images of whiteboards, where the information on the whiteboard can be categorized based on the structure of what is drawn on it. Structurally, the method is divided into two main phases. The first phase processes a bitmap image and outputs a set of vectorized features representing strokes of a pen. The second phase filters and categorizes these features and matches them against pre-defined contextual models. The output from the second phase is a set of matching contextual models, each containing a set of area outlines representing contextually important areas of the image.

The method proves robust both to variations in the quality of input – such as lighting, angles and signal-to-noise ratio - as well as to the choice of parameters used by the algorithms internally.

Abstract [sv]

I den här rapporten presenteras en metod för att identifiera intressanta områden i en bild utifrån streck ritade i bilden. Metoden har designats för att hantera foton, specifikt av whiteboards, där informationen på whiteboarden kan delas in utifrån de streck som dragits på den. Strukturellt sett är metoden uppdelad i två faser. I den första fasen behandlas en bitmapbild och resultatet blir en mängd vektoriserade representationer av handritade linjer. Dessa behandlas sedan i den andra fasen, där de kategoriseras, filtreras och slutligen matchas mot fördefinierade kontextuella modeller. Resultatet av den andra fasen är den uppsättning kontextuella modeller som passar in, vardera med information om de kontextuellt intressanta områden i bilden som modellen identifierat.

Metoden visar sig robust både vad gäller kvalitén på indata – såsom ljusförhållanden, vinklar och signal-till-brus-förhållande – som valet av de parametrar som används av algoritmerna internt.

Place, publisher, year, edition, pages
2015.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-169655OAI: oai:DiVA.org:kth-169655DiVA: diva2:824251
External cooperation
Bontouch AB
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2015-06-29 Created: 2015-06-21 Last updated: 2015-06-29Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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