TY - JOUR
T1 - Heterogeneous multi-view information fusion
T2 - Review of 3-D reconstruction methods and a new registration with uncertainty modeling
AU - Aliakbarpour, Hadi
AU - Prasath, V. B.Surya
AU - Palaniappan, Kannappan
AU - Seetharaman, Guna
AU - Dias, Jorge
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2016
Y1 - 2016
N2 - We consider a multisensor network fusion framework for 3-D data registration using inertial planes, the underlying geometric relations, and transformation model uncertainties. We present a comprehensive review of 3-D reconstruction methods and registration techniques in terms of the underlying geometric relations and associated uncertainties in the registered images. The 3-D data registration and the scene reconstruction task using a set of multiview images are an essential goal of structure-from-motion algorithms that still remains challenging for many applications, such as surveillance, human motion and behavior modeling, virtual-reality, smart-rooms, health-care, teleconferencing, games, human-robot interaction, medical imaging, and scene understanding. We propose a framework to incorporate measurement uncertainties in the registered imagery, which is a critical issue to ensure the robustness of these applications but is often not addressed. In our test bed environment, a network of sensors is used where each physical node consists of a coupled camera and associated inertial sensor (IS)/inertial measurement unit. Each camera-IS node can be considered as a hybrid sensor or fusion-based virtual camera. The 3-D scene information is registered onto a set of virtual planes defined by the IS. The virtual registrations are based on using the homography calculated from 3-D orientation data provided by the IS. The uncertainty associated with each 3-D point projected onto the virtual planes is modeled using statistical geometry methods. Experimental results demonstrate the feasibility and effectiveness of the proposed approach for multiview reconstruction with sensor fusion.
AB - We consider a multisensor network fusion framework for 3-D data registration using inertial planes, the underlying geometric relations, and transformation model uncertainties. We present a comprehensive review of 3-D reconstruction methods and registration techniques in terms of the underlying geometric relations and associated uncertainties in the registered images. The 3-D data registration and the scene reconstruction task using a set of multiview images are an essential goal of structure-from-motion algorithms that still remains challenging for many applications, such as surveillance, human motion and behavior modeling, virtual-reality, smart-rooms, health-care, teleconferencing, games, human-robot interaction, medical imaging, and scene understanding. We propose a framework to incorporate measurement uncertainties in the registered imagery, which is a critical issue to ensure the robustness of these applications but is often not addressed. In our test bed environment, a network of sensors is used where each physical node consists of a coupled camera and associated inertial sensor (IS)/inertial measurement unit. Each camera-IS node can be considered as a hybrid sensor or fusion-based virtual camera. The 3-D scene information is registered onto a set of virtual planes defined by the IS. The virtual registrations are based on using the homography calculated from 3-D orientation data provided by the IS. The uncertainty associated with each 3-D point projected onto the virtual planes is modeled using statistical geometry methods. Experimental results demonstrate the feasibility and effectiveness of the proposed approach for multiview reconstruction with sensor fusion.
KW - 3D reconstruction
KW - coupled sensors
KW - geometric uncertainty
KW - heterogeneous information fusion
KW - homography
KW - image registration
KW - inertial measurement unit (IMU)
KW - sensor network
KW - Structure-from-motion
KW - virtual reality
UR - http://www.scopus.com/inward/record.url?scp=85012979726&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2016.2629987
DO - 10.1109/ACCESS.2016.2629987
M3 - Article
AN - SCOPUS:85012979726
SN - 2169-3536
VL - 4
SP - 8264
EP - 8285
JO - IEEE Access
JF - IEEE Access
M1 - 7755753
ER -