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Local N-ary Patterns: a local multi-modal descriptor for place categorization
Graduate School of Information Science and Electrical Engeneering, Kyushu University, Fukuoka, Japan.
School of Computer Science, University of Lincoln, Lincoln, England.ORCID iD: 0000-0002-3908-4921
Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan.
Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan.
2016 (English)In: Advanced Robotics, ISSN 0169-1864, E-ISSN 1568-5535, Vol. 30, no 6, p. 402-415Article in journal (Refereed) Published
Abstract [en]

This paper presents an effective integration method of multiple modalities such as depth, color, and reflectance for place categorization. To achieve better performance with integrated multi-modalities, we introduce a novel descriptor, local N-ary patterns (LTP), which can perform robust discrimination of place categorization. In this paper, the LNP descriptor is applied to a combination of two modalities, i.e. depth and reflectance, provided by a laser range finder. However, the LNP descriptor can be easily extended to a larger number of modalities. The proposed LNP describes relationships between the multi-modal values of pixels and their neighboring pixels. Since we consider the multi-modal relationship, our proposed method clearly demonstrates more effective classification results than using individual modalities. We carried out experiments with the Kyushu University Indoor Semantic Place Dataset, which is publicly available. This data-set is composed of five indoor categories: corridors, kitchens, laboratories, study rooms, and offices. We confirmed that our proposed method outperforms previous uni-modal descriptors.

Place, publisher, year, edition, pages
Taylor & Francis, 2016. Vol. 30, no 6, p. 402-415
Keywords [en]
place categorization, reflectance image, depth image, Local N-ary patterns, laser range finder
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:oru:diva-83666DOI: 10.1080/01691864.2015.1120242ISI: 000372841900004Scopus ID: 2-s2.0-84954096136OAI: oai:DiVA.org:oru-83666DiVA, id: diva2:1447635
Note

The present study was supported in part by a Grant-in-Aid for Scientific Research (A) (26249029).

Available from: 2020-06-26 Created: 2020-06-26 Last updated: 2020-08-03Bibliographically approved

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Martinez Mozos, Oscar

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