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Detection and Recognition of Compound 3D Models by Hypothesis Generation

  • Artur Wilkowski
  • Maciej Stefańczyk
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 440)

Abstract

In the paper there is proposed an integrated object detection and recognition system, based on object description given in semantic form [5]. The objects are described in a generic way in terms of parts and relations between them. The Bayesian inference system is utilized, so each object detection and recognition score has probabilistic interpretation. There are designed basic 3D models founded on the inference framework. Object instances are then detected and recognized in real-world Kinect RGBD images.

Keywords

3-D objects recognition Point cloud RGBD image analysis Constraint satisfaction 

Notes

Acknowledgments

The authors gratefully acknowledge the support of the National Centre for Research and Development (Poland), grant no. PBS1/A3/8/2012.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Industrial Research Institute for Automation and MeasurementsWarsawPoland
  2. 2.Institute of Control and Computation EngineeringWarsawPoland

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