Primitive Shape Recognition via Superquadric Representation using Large Margin Nearest Neighbor Classifier

Ryo Hachiuma, Yuko Ozasa, Hideo Saito

Abstract

It is known that humans recognize objects using combinations and positional relations of primitive shapes. The first step of such recognition is to recognize 3D primitive shapes. In this paper, we propose a method for primitive shape recognition using superquadric parameters with a metric learning method, large margin nearest neighbor (LMNN). Superquadrics can represent various types of primitive shapes using a single equation with few parameters. These parameters are used as the feature vector of classification. The real objects of primitive shapes are used in our experiment, and the results show the effectiveness of using LMNN for recognition based on superquadrics. Compared to the previous methods, which used k-nearest neighbors (76.5%) and Support Vector Machines (73.5%), our LMNN method has the best performance (79.5%).

References

  1. Barr, A. H. (1981). Superquadrics and angle-preserving transformations. IEEE Computer graphics and Applications, 1(1):11-23.
  2. Biederman, I. (1987). Recognition-by-components: a theory of human image understanding. Psychological review, 94(2):115.
  3. Burges, C. J. (1998). A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery, 2(2):121-167.
  4. Chevalier, L., Jaillet, F., and Baskurt, A. (2003). Segmentation and superquadric modeling of 3d objects. In WSCG.
  5. Drews Jr., P., Trujillo, P. N., Rocha, R. P., Campos, M. F. M., and Dias, J. (2010). Novelty detection and 3d shape retrieval using superquadrics and multi-scale sampling for autonomous mobile robots. In ICRA, pages 3635-3640.
  6. Garcia, S. (2009). Fitting primitive shapes to point clouds for robotic grasping. Master of Science Thesis. School of Computer Science and Communication, Royal Institute of Technology, Stockholm, Sweden.
  7. Leonardis, A., Jaklic, A., and Solina, F. (1997). Superquadrics for segmenting and modeling range data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(11):1289-1295.
  8. Marr D, V. (1982). A computational investigation into the human representation and processing of visual information.
  9. Moustakas, K., Tzovaras, D., and Strintzis, M. G. (2007). Sq-map: Efficient layered collision detection and haptic rendering. IEEE Transactions on Visualization and Computer Graphics, 13(1):80-93.
  10. Nieuwenhuisen, M., St├╝ckler, J., Berner, A., Klein, R., and Behnke, S. (2012). Shape-primitive based object recognition and grasping. In Robotics; Proceedings of ROBOTIK 2012; 7th German Conference on, pages 1-5. VDE.
  11. Pentland, A. P. (1986). Perceptual organization and the representation of natural form. Artificial Intelligence, 28(3):293-331.
  12. Press, W. H., Flannery, B. P., Teukolsky, S. A., and Vetterling, W. T. (1986). Numerical recipes: the art of scientific computing. Cambridge U. Press, Cambridge, MA.
  13. Raja, N. S. and Jain, A. K. (1992). Recognizing geons from superquadrics fitted to range data. Image and vision computing, 10(3):179-190.
  14. Rusu, R. B., Blodow, N., and Beetz, M. (2009). Fast point feature histograms (fpfh) for 3d registration. In Robotics and Automation, 2009. ICRA'09. IEEE International Conference on, pages 3212-3217. IEEE.
  15. Rusu, R. B. and Cousins, S. (2011). 3d is here: Point cloud library (pcl). In Robotics and Automation (ICRA), 2011 IEEE International Conference on, pages 1-4. IEEE.
  16. Saito, H. and Kimura, M. (1996). Superquadric modeling of multiple objects from shading images using genetic algorithms. In Industrial Electronics, Control, and Instrumentation, 1996., Proceedings of the 1996 IEEE IECON 22nd International Conference on, volume 3, pages 1589-1593. IEEE.
  17. Schnabel, R., Wahl, R., and Klein, R. (2007). Efficient RANSAC for point-cloud shape detection. In Computer graphics forum, volume 26, pages 214-226. Wiley Online Library.
  18. Solina, F. and Bajcsy, R. (1987). Range image interpretation of mail pieces with superquadrics.
  19. Solina, F. and Bajcsy, R. (1990). Recovery of parametric models from range images: The case for superquadrics with global deformations. IEEE transactions on pattern analysis and machine intelligence, 12(2):131-147.
  20. Somani, N., Cai, C., Perzylo, A., Rickert, M., and Knoll, A. (2014). Object recognition using constraints from primitive shape matching. In International Symposium on Visual Computing, pages 783-792. Springer.
  21. Strand, M., Xue, Z., Zoellner, M., and Dillmann, R. (2010). Using superquadrics for the approximation of objects and its application to grasping. In Information and Automation (ICIA), 2010 IEEE International Conference on, pages 48-53. IEEE.
  22. Suykens, J. A. and Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural processing letters, 9(3):293-300.
  23. Tang, S., Wang, X., Lv, X., Han, T. X., Keller, J., He, Z., Skubic, M., and Lao, S. (2012). Histogram of oriented normal vectors for object recognition with a depth sensor. In Asian conference on computer vision, pages 525-538. Springer.
  24. Tombari, F., Salti, S., and Di Stefano, L. (2010). Unique signatures of histograms for local surface description. In European conference on computer vision, pages 356- 369. Springer.
  25. Varadarajan, K. M. and Vincze, M. (2011). Object part segmentation and classification in range images for grasping. In Advanced Robotics (ICAR), 2011 15th International Conference on, pages 21-27. IEEE.
  26. Weinberger, K. Q. and Saul, L. K. (2009). Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research, 10(Feb):207-244.
  27. Xing, W., Liu, W., and Yuan, B. (2004). Superquadricbased geons recognition utilizing support vector machines. In Signal Processing, 2004. Proceedings. ICSP'04. 2004 7th International Conference on, volume 2, pages 1264-1267. IEEE.
  28. Zhang, Z. (2012). Microsoft kinect sensor and its effect. IEEE multimedia, 19(2):4-10.
Download


Paper Citation


in Harvard Style

Hachiuma R., Ozasa Y. and Saito H. (2017). Primitive Shape Recognition via Superquadric Representation using Large Margin Nearest Neighbor Classifier . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-226-4, pages 325-332. DOI: 10.5220/0006153203250332


in Bibtex Style

@conference{visapp17,
author={Ryo Hachiuma and Yuko Ozasa and Hideo Saito},
title={Primitive Shape Recognition via Superquadric Representation using Large Margin Nearest Neighbor Classifier},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={325-332},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006153203250332},
isbn={978-989-758-226-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)
TI - Primitive Shape Recognition via Superquadric Representation using Large Margin Nearest Neighbor Classifier
SN - 978-989-758-226-4
AU - Hachiuma R.
AU - Ozasa Y.
AU - Saito H.
PY - 2017
SP - 325
EP - 332
DO - 10.5220/0006153203250332