TY - GEN
T1 - Affordance-based 3D feature for generic object recognition
AU - Iizuka, M.
AU - Akizuki, S.
AU - Hashimoto, M.
N1 - Funding Information:
This work was partially supported by the New Energy and Industrial Technology Development Organization (NEDO).
Publisher Copyright:
© 2017 SPIE.
PY - 2017
Y1 - 2017
N2 - Techniques for generic object recognition, which targets everyday objects such as cups and spoons, and techniques for approach vector estimation (e.g. estimating grasp position), which are needed for carrying out tasks involving everyday objects, are considered necessary for the perceptual system of service robots. In this research, we design feature for generic object recognition so they can also be applied to approach vector estimation. To carry out tasks involving everyday objects, estimating the function of the target object is critical. Also, as the function of holding liquid is found in all cups, so a function is shared in each type (class) of everyday objects. We thus propose a generic object recognition method that can estimate the approach vector by expressing an object's function as feature. In a test of the generic object recognition of everyday objects, we confirmed that our proposed method had a 92% recognition rate. This rate was 11% higher than the mainstream generic object recognition technique of using convolutional neural network (CNN).
AB - Techniques for generic object recognition, which targets everyday objects such as cups and spoons, and techniques for approach vector estimation (e.g. estimating grasp position), which are needed for carrying out tasks involving everyday objects, are considered necessary for the perceptual system of service robots. In this research, we design feature for generic object recognition so they can also be applied to approach vector estimation. To carry out tasks involving everyday objects, estimating the function of the target object is critical. Also, as the function of holding liquid is found in all cups, so a function is shared in each type (class) of everyday objects. We thus propose a generic object recognition method that can estimate the approach vector by expressing an object's function as feature. In a test of the generic object recognition of everyday objects, we confirmed that our proposed method had a 92% recognition rate. This rate was 11% higher than the mainstream generic object recognition technique of using convolutional neural network (CNN).
KW - Affordance
KW - Generic object recognition
UR - http://www.scopus.com/inward/record.url?scp=85020285814&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85020285814&partnerID=8YFLogxK
U2 - 10.1117/12.2266917
DO - 10.1117/12.2266917
M3 - Conference contribution
AN - SCOPUS:85020285814
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Thirteenth International Conference on Quality Control by Artificial Vision 2017
A2 - Yamashita, Atsushi
A2 - Nagahara, Hajime
A2 - Umeda, Kazunori
PB - SPIE
T2 - 13th International Conference on Quality Control by Artificial Vision, QCAV 2017
Y2 - 14 May 2017 through 16 May 2017
ER -