Robot vision
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Robotics: control, sensing, vision, and intelligence
Robotics: control, sensing, vision, and intelligence
Motion stereo using ego-motion complex logarithmic mapping
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Computer Vision
A Theory of Human Stereo Vision
A Theory of Human Stereo Vision
Adaptive camera calibration in an industrial robotic environment
IEA/AIE '90 Proceedings of the 3rd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 1
Real Aperture Axial Stereo: Solving for Correspondences in Blur
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Robust 3d face data acquisition using a sequential color-coded pattern and stereo camera system
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part II
Shape from Sharp and Motion-Blurred Image Pair
International Journal of Computer Vision
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The location of a scene element can be determined from the disparity of two of its depicted entities (each in a different image). Prior to establishing disparity, however, the correspondence problem must be solved. It is shown that for the axial-motion stereo camera model the probability of determining unambiguous correspondence assignments is significantly greater than that for other stereo camera models. However, the mere geometry of the stereo camera system does not provide sufficient information for uniquely identifying correct correspondences. Therefore, additional constraints derived from justifiable assumptions about the scene domain and from the scene radiance model are utilized to reduce the number of potential matches. The measure for establishing the correct correspondence is shown to be a function of the geometrical constraints, scene constraints, and scene radiance model.