Exportar Publicação

A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Proença, P., Gaspar, F. & Dias, J. (2015). Good Appearance and 3D Shape Descriptors for Object Category Recognition. International Journal on Artificial Intelligence Tools. 24 (4), 1540017
Exportar Referência (IEEE) 
P. F. Proenca et al.,  "Good Appearance and 3D Shape Descriptors for Object Category Recognition", in Int. Journal on Artificial Intelligence Tools, vol. 24, no. 4, pp. 1540017, 2015
Exportar BibTeX 
@article{proenca2015_1734978195307,
	author = "Proença, P. and Gaspar, F. and Dias, J.",
	title = "Good Appearance and 3D Shape Descriptors for Object Category Recognition",
	journal = "International Journal on Artificial Intelligence Tools",
	year = "2015",
	volume = "24",
	number = "4",
	doi = "10.1142/s0218213015400175",
	pages = "1540017",
	url = "http://www.scopus.com/inward/record.url?eid=2-s2.0-84940062420&partnerID=MN8TOARS"
}
Exportar RIS 
TY  - JOUR
TI  - Good Appearance and 3D Shape Descriptors for Object Category Recognition
T2  - International Journal on Artificial Intelligence Tools
VL  - 24
IS  - 4
AU  - Proença, P.
AU  - Gaspar, F.
AU  - Dias, J.
PY  - 2015
SP  - 1540017
SN  - 0218-2130
DO  - 10.1142/s0218213015400175
UR  - http://www.scopus.com/inward/record.url?eid=2-s2.0-84940062420&partnerID=MN8TOARS
AB  - For the problem of object category recognition, we have studied different families of descriptors exploiting RGB and 3D information. We have proven practically that 3D shape-based descriptors are more suitable for this type of recognition due to low shape intra-class variance, as opposed to texture-based. Performance evaluation on training-set subsampling, suggests that the viewpoint
invariance characteristics of 3D descriptors, favors significantly these descriptors while invariant SIFT descriptors can be ambiguous. In addition, we have also shown how an efficient Naive Bayes Nearest Neighbor (NBNN) classifier can scale to a large hierarchical RGB-D Object Dataset and achieve, with a single descriptor type, an accuracy close to state-of-the-art learning-based approaches using combined descriptors.
ER  -