Three dimensional spatial memory and learningin real and virtual environments
Spatial Cognition and Computation
Selecting Canonical Views for View-Based 3-D Object Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A Learning Approach to 3D Object Representation for Classification
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Unsupervised learning of 3D object models from partial views
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Validating vision and robotic algorithms for dynamic real world environments
SIMPAR'10 Proceedings of the Second international conference on Simulation, modeling, and programming for autonomous robots
Free-Shaped object recognition method from partial views using weighted cone curvatures
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
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This paper deals with attention in 3D environments based upon knowledge-driven cues. Using learned 3D scenes as top-down influence, the proposed system is able to mark high saliency to locations occupied by objects that are new, changed, or even missing from their location as compared to the already learned situation. The proposal addresses a system level solution covering learning of 3D objects and scenes using visual, range and odometry sensors, storage of spatial knowledge using multiple-view theory from psychology, and validation of scenes using recognized objects as anchors. The proposed system is designed to handle the complex scenarios of recognition with partially visible objects during revisit to the scene from an arbitrary direction. Simulation results have shown success of the designed methodology under ideal sensor readings from range and odometry sensors.