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Object recognition using shape growth pattern
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik. (BigData@BTH)ORCID-id: 0000-0002-4390-411x
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.ORCID-id: 0000-0001-7536-3349
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.ORCID-id: 0000-0001-9947-1088
2017 (engelsk)Inngår i: Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis, ISPA, IEEE Computer Society Digital Library, 2017, s. 47-52, artikkel-id 8073567Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
IEEE Computer Society Digital Library, 2017. s. 47-52, artikkel-id 8073567
Emneord [en]
Binary image dilations, convolutional neural network, machine learning, pattern recognition, shape growth pattern
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-15416DOI: 10.1109/ISPA.2017.8073567ISI: 000442428600009ISBN: 978-1-5090-4011-7 (digital)OAI: oai:DiVA.org:bth-15416DiVA, id: diva2:1154115
Konferanse
10th International Symposium on Image and Signal Processing and Analysis (ISPA), Ljubljana
Prosjekter
Scalable resource efficient systems for big data analyticsTilgjengelig fra: 2017-11-01 Laget: 2017-11-01 Sist oppdatert: 2018-09-06bibliografisk kontrollert

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Cheddad, AbbasKusetogullari, HüseyinGrahn, Håkan

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