Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
Multiresolution surface modeling based on hierarchical triangulation
Computer Vision and Image Understanding
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation and recovery of superquadrics: computational imaging and vision
Segmentation and recovery of superquadrics: computational imaging and vision
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
A constraint satisfaction framework with Bayesian inference for model-based object recognition
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part II
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A model-based object recognition in video and depth images is proposed for the purpose of semantic map creation in mobile robotics. Three types of objects are modeled: a human silhouette, a chair/table and corridor walls. A bi-driven hypothesis generation and verification strategy is outlined. The object model includes a hierarchic semantic nets, combined with a graph of constraints and a Bayesian network for hypothesis generation and evaluation. For the purpose of model-to-image matching we define an incomplete constraint satisfaction problem and solve it. Our CSP-search allows partial assignment solutions and uses a stochastic inference to provide judgments of such solutions. The verification of hypotheses is due to a top-down occlusion propagation process, that explains why some object parts are hidden or occluded.