Bingham procrustean alignment for object detection in clutter

IEEE International Conference on Intelligent Robots and Systems

Volumn , Issue , 2013, Pages 2158-2165

  Glover, Jared ^a   Popovic, Sanja ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALIGNMENT PROBLEMS; DETECTION SYSTEM; LEAST SQUARE; OBJECT DETECTION; ORIENTED FEATURES; POINT CORRESPONDENCE; POINT FEATURES; POINT SET;

INTELLIGENT ROBOTS; OBJECT RECOGNITION; PROBABILITY DISTRIBUTIONS;

ALIGNMENT;

EID: 84893776515     PISSN: 21530858     EISSN: 21530866     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2013.6696658     Document Type: Conference Paper

References (12)

1. Aitor Aldoma, Federico Tombari, Luigi Di Stefano, and Markus Vincze. A global hypotheses verification method for 3d object recognition. In ECCV 2012, pages 511-524. Springer, 2012.
2. Matthew E Antone. Robust camera pose recovery using stochastic geometry. PhD thesis, Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.
3. Dana H Ballard. Generalizing the hough transform to detect arbitrary shapes. Pattern recognition, 13(2):111-122, 1981.
4. P.J. Besl and N.D. McKay. A method for registration of 3-d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14:239-256, 1992.
5. Christopher Bingham. An antipodally symmetric distribution on the sphere. The Annals of Statistics, 2(6):1201-1225, 1974.
6. Jared Glover, Radu Rusu, and Gary Bradski. Monte carlo pose estimation with quaternion kernels and the bingham distribution. In Proceedings of Robotics: Science and Systems, Los Angeles, CA, USA, June 2011.
7. W Eric L Grimson and Tomas Lozano-Perez. Localizing overlapping parts by searching the interpretation tree. Pattern Analysis and Machine Intelligence, IEEE Transactions on, (4):469-482, 1987.
8. Berthold KP Horn. Closed-form solution of absolute orientation using unit quaternions. JOSA A, 4(4):629-642, 1987.
9. David G Lowe. Object recognition from local scale-invariant features. In Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, volume 2, pages 1150-1157. Ieee, 1999.
10. Radu Bogdan Rusu, Nico Blodow, and Michael Beetz. Fast Point Feature Histograms (FPFH) for 3D Registration. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Kobe, Japan, May 12-17 2009.
11. Jie Tang, Stephen Miller, Arjun Singh, and Pieter Abbeel. A textured object recognition pipeline for color and depth image data. In Robotics and Automation (ICRA), 2012 IEEE International Conference on, pages 3467-3474. IEEE, 2012.
12. Federico Tombari, Samuele Salti, and Luigi Di Stefano. Unique signatures of histograms for local surface description. Computer Vision-ECCV 2010, pages 356-369, 2010.