Depth kernel descriptors for object recognition

IEEE International Conference on Intelligent Robots and Systems

Volumn , Issue , 2011, Pages 821-826

  Bo, Liefeng ^a   Ren, Xiaofeng ^b   Fox, Dieter ^a  

^a University of Washington   (United States)

^b INTEL CORPORATION   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

3-D SHAPE; DEPTH CAMERA; DEPTH EDGES; DEPTH IMAGE; DEPTH VALUE; DESCRIPTORS; LOCAL DESCRIPTORS; LOCAL FEATURE; MICROSOFT; MODEL SIZE; REAL TIME; SPIN IMAGES; STATE OF THE ART;

CAMERAS; FEATURE EXTRACTION; INTELLIGENT ROBOTS; OBJECT RECOGNITION; REAL TIME SYSTEMS; ROBOTICS;

THREE DIMENSIONAL;

EID: 84455172978     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2011.6048717     Document Type: Conference Paper

References (26)

1. L. Bo, K. Lai, X. Ren, and D. Fox. Object recognition with hierarchical kernel descriptors. In IEEE International Conference on Computer Vision and Pattern Recognition, 2011.
2. L. Bo, X. Ren, and D. Fox. Kernel Descriptors for Visual Recognition. In Advances in Neural Information Processing Systems, 2010.
3. L. Bo and C. Sminchisescu. Efficient Match Kernel between Sets of Features for Visual Recognition. In Advances in Neural Information Processing Systems, 2009.
4. Leo Breiman. Random forests. Machine Learning, 45(1):5-32, 2001.
5. Chih-Chung Chang and Chih-Jen Lin. LIBSVM: a library for support vector machines, 2001.
6. A. Coates and A. Ng. The importance of encoding versus training with sparse coding and vector quantization. In ICML, 2011.
7. J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L. Fei-fei. ImageNet: A Large-Scale Hierarchical Image Database. In IEEE International Conference on Computer Vision and Pattern Recognition, 2009.
8. B. Drost, M. Ulrich, N. Navab, and S. Ilic. Model globally, match locally: Efficient and robust 3d object recognition. In IEEE International Conference on Computer Vision and Pattern Recognition, 2010.
9. F. Endres, C. Plagemann, C. Stachniss, and W. Burgard. Unsupervised discovery of object classes from range data using latent Dirichlet allocation. In Robotics: Science and Systems Conference, 2009.
10. R. Fan, K. Chang, C. Hsieh, X. Wang, and C. Lin. Liblinear: A library for large linear classification. Journal of Machine Learning Research (JMLR), 9:1871-1874, 2008.
11. G. Hinton, S. Osindero, and Y. Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18(7):1527-1554, 2006. (Pubitemid 44024729)
12. A. Johnson and M. Hebert. Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(5), 1999.
13. Microsoft Kinect. http://www.xbox.com/en-us/kinect.
14. K. Lai, L. Bo, X. Ren, and D. Fox. A Large-Scale Hierarchical Multi-View RGB-D Object Dataset. In IEEE International Conference on on Robotics and Automation, 2011.
15. K. Lai and D. Fox. Object Recognition in 3D Point Clouds Using Web Data and Domain Adaptation. The International Journal of Robotics Research, 2010.
16. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In IEEE International Conference on Computer Vision and Pattern Recognition, 2006.
17. H. Lee, R. Grosse, R. Ranganath, and A. Ng. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In International Conference on Machine Learning, 2009.
18. Y. Lin, F. Lv, S. Zhu, M. Yang, T. Cour, K. Yu, L. Cao, and T. Huang. Large-scale image classification: fast feature extraction and svm training. In IEEE Conference on Computer Vision and Pattern Recognition, 2011.
19. D. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60:91-110, 2004.
20. PrimeSense. http://www.primesense.com/.
21. R. Bogdan Rusu, N. Blodow, and M. Beetz. Fast point feature histograms (fpfh) for 3d registration. In IEEE International Conference on Robotics and Automation, 2009.
22. S. Shalev-Shwartz, Y. Singer, and N. Srebro. Pegasos: Primal estimated sub-gradient solver for svm. In International Conference on Machine Learning, pages 807-814. ACM, 2007.
23. J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, and A. Blake. Real-time human pose recognition in parts from a single depth image. In IEEE International Conference on Computer Vision and Pattern Recognition, 2011.
24. B. Steder, R. B. Rusu, K. Konolige, and W. Burgard. Point feature extraction on 3d range scans taking into account object boundaries. In IEEE International Conference on on Robotics and Automation, 2011.
25. J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Guo. Locality-constrained linear coding for image classification. In IEEE International Conference on Computer Vision and Pattern Recognition, 2010.
26. Y. Bengio Y. LeCun, L. Bottou and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.