Statistical object recognition
Statistical object recognition
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Relative 3D reconstruction using multiple uncalibrated images
International Journal of Robotics Research
OBBTree: a hierarchical structure for rapid interference detection
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Lines and Points in Three Views and the Trifocal Tensor
International Journal of Computer Vision
What can be seen in three dimensions with an uncalibrated stereo rig
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Trilinear Tensor: The Fundamental Construct of Multiple-view Geometry and Its Applications
AFPAC '97 Proceedings of the International Workshop on Algebraic Frames for the Perception-Action Cycle
Learning to recognize objects in images: acquiring and using probabilistic models of appearance
Learning to recognize objects in images: acquiring and using probabilistic models of appearance
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This paper can be divided into two main parts. Both of them describe one of those parts of an intelligent robot control system, which makes the system capable to interact with its environment via visual information. The two parts can be handled as complementary parts of each other. The first part shows the solution of the data extraction from the visual information. This is a well known machine vision problem, in this case, the applied method is a passive stereo type. The second part explains the method of visual representation of the data. This is a visualization problem, in this case, a virtual reality system is used. With the use of the detailed graphic models of VR, efficient off-line robot programming and simulation is available.