TY - CHAP
T1 - Urban object recognition from informative local features
AU - Fritz, Gerald
AU - Seifert, Christin
AU - Paletta, Lucas
PY - 2005
Y1 - 2005
N2 - Autonomous mobile agents require object recognition for high level interpretation and localization in complex scenes. In urban environments, recognition of buildings might play a dominant role in robotic systems that need object based navigation, that take advantage of visual feedback and multimodal information for self-localization, or that enable association to related information from the identified semantics. We present a new method – the informative local features approach – based on an information theoretic saliency measure that is rapidly derived from a local Parzen window density estimation in feature subspace. From the learning of a decision tree based mapping to informative features, it enables attentive access to discriminative information and thereby significantly speeds up the recognition process. This approach is highly robust with respect to severe degrees of partial occlusion, noise, and tolerant to some changes in scale and illumination. We present performance evaluation on our publicly available reference object database (TSG-20) that demonstrates the efficiency of this approach, case wise even outperforming the SIFT feature approach [1]. Building recognition will be advantageous in various application domains, such as, mobile mapping, unmanned vehicle navigation, and systems for car driver assistance.
AB - Autonomous mobile agents require object recognition for high level interpretation and localization in complex scenes. In urban environments, recognition of buildings might play a dominant role in robotic systems that need object based navigation, that take advantage of visual feedback and multimodal information for self-localization, or that enable association to related information from the identified semantics. We present a new method – the informative local features approach – based on an information theoretic saliency measure that is rapidly derived from a local Parzen window density estimation in feature subspace. From the learning of a decision tree based mapping to informative features, it enables attentive access to discriminative information and thereby significantly speeds up the recognition process. This approach is highly robust with respect to severe degrees of partial occlusion, noise, and tolerant to some changes in scale and illumination. We present performance evaluation on our publicly available reference object database (TSG-20) that demonstrates the efficiency of this approach, case wise even outperforming the SIFT feature approach [1]. Building recognition will be advantageous in various application domains, such as, mobile mapping, unmanned vehicle navigation, and systems for car driver assistance.
KW - Object recognition
KW - Outdoor computer vision
KW - Urban environments
KW - Visual attention
UR - http://www.mendeley.com/research/urban-object-recognition-informative-local-features
U2 - 10.1109/ROBOT.2005.1570108
DO - 10.1109/ROBOT.2005.1570108
M3 - Chapter
SN - 0-7803-8914-X
T3 - Proceedings IEEE International Conference on Robotics and Automation (ICRA)
SP - 131
EP - 137
BT - Proceedings of the 2005 IEEE International Conference on Robotics and Automation
PB - IEEE
T2 - 2005 IEEE International Conference on Robotics and Automation, ICRA 2005
Y2 - 18 April 2005 through 22 April 2005
ER -