TY - GEN
T1 - Bayesian viewpoint-dependent robust classification under model and localization uncertainty
AU - Feldman, Yuri
AU - Indelman, Vadim
N1 - Publisher Copyright: © 2018 IEEE.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - We propose an algorithm for robust visual classification of an object of interest observed from multiple views using a black-box Bayesian classifier which provides a measure of uncertainty, in the presence of significant ambiguity and classifier noise, and of localization error. The fusion of classifier outputs takes into account viewpoint dependency and spatial correlation among observations, as well as pose uncertainty when these observations are taken and a measure of confidence provided by the classifier itself. Our experiments confirm an improvement in robustness over state-of-the-art.
AB - We propose an algorithm for robust visual classification of an object of interest observed from multiple views using a black-box Bayesian classifier which provides a measure of uncertainty, in the presence of significant ambiguity and classifier noise, and of localization error. The fusion of classifier outputs takes into account viewpoint dependency and spatial correlation among observations, as well as pose uncertainty when these observations are taken and a measure of confidence provided by the classifier itself. Our experiments confirm an improvement in robustness over state-of-the-art.
UR - http://www.scopus.com/inward/record.url?scp=85048745916&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2018.8461127
DO - 10.1109/ICRA.2018.8461127
M3 - منشور من مؤتمر
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 3221
EP - 3228
BT - 2018 IEEE International Conference on Robotics and Automation, ICRA 2018
T2 - 2018 IEEE International Conference on Robotics and Automation, ICRA 2018
Y2 - 21 May 2018 through 25 May 2018
ER -