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Object recognition using shape growth pattern
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. (BigData@BTH)ORCID iD: 0000-0002-4390-411x
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0001-7536-3349
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
2017 (English)In: Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis (ISPA), IEEE Computer Society Digital Library, 2017, 47-52 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a preprocessing stage to augment the bank of features that one can retrieve from binary images to help increase the accuracy of pattern recognition algorithms. To this end, by applying successive dilations to a given shape, we can capture a new dimension of its vital characteristics which we term hereafter: the shape growth pattern (SGP). This work investigates the feasibility of such a notion and also builds upon our prior work on structure preserving dilation using Delaunay triangulation. Experiments on two public data sets are conducted, including comparisons to existing algorithms. We deployed two renowned machine learning methods into the classification process (i.e., convolutional neural network-CNN- and random forests-RF-) since they perform well in pattern recognition tasks. The results show a clear improvement of the proposed approach's classification accuracy (especially for data sets with limited training samples) as well as robustness against noise when compared to existing methods.

Place, publisher, year, edition, pages
IEEE Computer Society Digital Library, 2017. 47-52 p.
National Category
Computer Systems Signal Processing
Identifiers
URN: urn:nbn:se:bth-15416DOI: 10.1109/ISPA.2017.8073567ISBN: 978-1-5090-4011-7 (electronic)OAI: oai:DiVA.org:bth-15416DiVA: diva2:1154115
Conference
10th International Symposium on Image and Signal Processing and Analysis (ISPA)
Projects
Scalable resource efficient systems for big data analytics
Available from: 2017-11-01 Created: 2017-11-01 Last updated: 2017-11-07Bibliographically approved

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fulltext(361 kB)132 downloads
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File name FULLTEXT01.pdfFile size 361 kBChecksum SHA-512
1d123a0d8e7c505b37d3caaf7a3004df9a7f58f24cdc2db1e772bb056c4642fa6fa26fc622c6f4f2890a6e7399c8511ada2e3e2e6c38b3b32a9419098cd0a933
Type fulltextMimetype application/pdf

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Publisher's full texthttp://ieeexplore.ieee.org/document/8073567/

Authority records BETA

Cheddad, AbbasKusetogullari, HüseyinGrahn, Håkan

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