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Export Reference (APA) 
Proença, P., Gaspar, F. & Dias, J. (2013). Good appearance and shape descriptors for object category recognition. In George Bebis, Richard Boyle, Bahram Parvin, Darko Koracin, Baoxin Li, Fatih Porikli, Victor Zordan, James Klosowski, Sabine Coquillart, Xun Luo, Min Chen, David Gotz (Ed.), Advances in visual computing: 9th International Symposium, ISVC 2013, Proceedings. (pp. 385-394). Crete: Springer.
Export Reference (IEEE) 
P. F. Proenca et al.,  "Good appearance and shape descriptors for object category recognition", in Advances in visual computing: 9th Int. Symp., ISVC 2013, Proc., George Bebis, Richard Boyle, Bahram Parvin, Darko Koracin, Baoxin Li, Fatih Porikli, Victor Zordan, James Klosowski, Sabine Coquillart, Xun Luo, Min Chen, David Gotz, Ed., Crete, Springer, 2013, vol. 8033, pp. 385-394
Export BibTeX 
@inproceedings{proenca2013_1716167597224,
	author = "Proença, P. and Gaspar, F. and Dias, J.",
	title = "Good appearance and shape descriptors for object category recognition",
	booktitle = "Advances in visual computing: 9th International Symposium, ISVC 2013, Proceedings",
	year = "2013",
	editor = "George Bebis, Richard Boyle, Bahram Parvin, Darko Koracin, Baoxin Li, Fatih Porikli, Victor Zordan, James Klosowski, Sabine Coquillart, Xun Luo, Min Chen, David Gotz",
	volume = "8033",
	number = "",
	series = "Lecture Notes in Artificial Intelligence",
	pages = "385-394",
	publisher = "Springer",
	address = "Crete",
	organization = "",
	url = "http://link.springer.com/chapter/10.1007%2F978-3-642-41914-0_38"
}
Export RIS 
TY  - CPAPER
TI  - Good appearance and shape descriptors for object category recognition
T2  - Advances in visual computing: 9th International Symposium, ISVC 2013, Proceedings
VL  - 8033
AU  - Proença, P.
AU  - Gaspar, F.
AU  - Dias, J.
PY  - 2013
SP  - 385-394
SN  - 0302-9743
CY  - Crete
UR  - http://link.springer.com/chapter/10.1007%2F978-3-642-41914-0_38
AB  - In the problem of object category recognition, we have studied
different families of descriptors exploiting RGB and 3D information.
Furthermore, 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 image texture-based. 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 [2] and achieve, with a single descriptor
type, an accuracy close to state-of-art learning based approaches using
combined descriptors.
ER  -