Detection and Recognition of Compound 3D Models by Hypothesis Generation
Artur Wilkowski , Maciej Stefańczyk
AbstractIn the paper there is proposed an integrated object detection and recognition system, based on object description given in semantic form . 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.
|Publication size in sheets||0.5|
|Book||Szewczyk Roman, Kaliczyńska Małgorzata, Zieliński Cezary: Challenges in Automation, Robotics and Measurement Techniques. Proceedings of AUTOMATION-2016, March 2-4, 2016, Warsaw, Poland, Advances in Intelligent Systems and Computing, vol. 440, 2016, Springer International Publishing, ISBN 978-3-319-29356-1, [978-3-319-29357-8], 919 p., DOI:10.1007/978-3-319-29357-8|
|Keywords in English||3-D objects recognition Point cloud RGBD image analysis Constraint satisfaction|
|project||RobREx: Autonomy for rescue and exploration robots. Project leader: Zieliński Cezary,
, Phone: 5102, start date 12-12-2012, end date 30-11-2015, 513/1031, Completed
|Score|| = 15.0, 27-03-2017, BookChapterSeriesAndMatConf|
= 15.0, 27-03-2017, BookChapterSeriesAndMatConf
|Citation count*||1 (2018-02-20)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.