Change search
ReferencesLink to record
Permanent link

Direct link
Recognizing Art Pieces in Subway using Computer Vision
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2011 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

We present a mobile application that automatically recognizes art pieces in the subway. Users can take a photo of an art piece with their mobile phones, and by using image recognition our system retrieves information about that particular art piece. By combining the location with image data, we can delimit the dataset of photos of art pieces to speed up the image recognition. The image recognition is based on feature detection using SURF, and by matching feature points using kd-trees for storing the interest points of the training data. We propose a method for selecting good training images when creating the database. In addition, we also cluster the training interest points by the k-means algorithm, which reduces the space of the kd-tree and increase the matching speed. We demonstrate the effectiveness of our approach with an application that allows users to enjoy the art pieces at different subway stations through image recognition.

Place, publisher, year, edition, pages
IT, 11 034
URN: urn:nbn:se:uu:diva-156433OAI: diva2:431588
Educational program
Master Programme in Computer Science
Available from: 2011-07-21 Created: 2011-07-21 Last updated: 2011-07-21Bibliographically approved

Open Access in DiVA

fulltext(1560 kB)908 downloads
File information
File name FULLTEXT01.pdfFile size 1560 kBChecksum SHA-512
Type fulltextMimetype application/pdf

By organisation
Department of Information Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 908 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 334 hits
ReferencesLink to record
Permanent link

Direct link