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Probabilistic and Voting Approaches to Cue Integration for Figure-Ground Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Depth Discontinuities by Pixel-to-Pixel Stereo
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Statistical Cue Integration for Foveated Wide-Field Surveillance
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Putting Objects in Perspective
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Probabilistic Fusion of Stereo with Color and Contrast for Bilayer Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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We present an architecture for detecting generic objects in unstructured scenes for an embodied visual system. The proposed architecture integrates the contributions of a collection of loosely coupled processes, each supplying a different type of information derived from a robot's sensors, including vision and kinesthesia. The core of the architecture is a probabilistic global workspace, which is used to incrementally build a representation of the scene, and whose contents are made available to the whole cohort of processes. The loosely coupled nature of the architecture facilitates parallelisation, and makes it easy to incorporate additional processes providing new sources of information. We provide an instantiation of this architecture using five processes on an upper-torso humanoid robot. Preliminary results show that the system can classify the elements of a scene well enough for the robot to be able to detect and touch a variety of movable objects within its reach.