Three-dimensional object recognition
ACM Computing Surveys (CSUR) - Annals of discrete mathematics, 24
ERNEST: A Semantic Network System for Pattern Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Rubik's cube reconstruction from single view for Service robots
Machine Graphics & Vision International Journal
3D semantic map computation based on depth map and video image
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
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A general (application independent) framework for the recognition of partially hidden 3-D objects in images is presented. It views the model-to-image matching as a constraint satisfaction problem (CSP) supported by Bayesian net-based evaluation of partial variable assignments. A modified incremental search for CSP is designed that allows partial solutions and calls for stochastic inference in order to provide judgments of partial states. Hence the detection of partial occlusion of objects is handled consistently with Bayesian inference over evidence and hidden variables. A particular problem of passing different objects to a machine by a human hand is solved while applying the general framework. The conducted experiments deal with the recognition of three objects: a simple cube, a Rubik cube and a tea cup.