Object representation based on gabor wave vector binning: An application to human head pose detection

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2011, Pages 2198-2204

  Dahmane, Mohamed ^a   Meunier, Jean ^a  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

BASE LEARNERS; COMPUTER VISION PROBLEMS; DESCRIPTORS; DISCRIMINATIVE FEATURES; FEATURE SETS; HUMAN HEAD; OBJECT DETECTION; OBJECT REPRESENTATIONS; PERFORMANCE GAIN; VISUAL OBJECT RECOGNITION; WAVE VECTOR;

ELECTRICAL ENGINEERING;

OBJECT RECOGNITION;

EID: 84856686377     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCVW.2011.6130520     Document Type: Conference Paper

References (21)

1. A. Albiol, D. Monzo, A. Martin, and J. Sastre. Face recognition using HOG-EBGM. Pattern Recognition Letters, 29(10) : 1537-1543, 2008.
2. S. Belongie, J. Malik, and J. Puzicha. Matching shapes. In 8th ICCV, pages 454-461, 2001.
3. N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge, UK, 2000.
4. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. pages 886-893, 2005.
5. Y. Fu and T. Huang. Graph embedded analysis for head pose estimation. pages 3-8, 2006.
6. N. Gourier, D. Hall, and J. L. Crowley. Estimating face orientation from robust detection of salient facial features. In in Proceedings of POINTING '04 International Workshop on Visual Observation of Deictic Gestures, 2004.
7. N. Gourier, J. Maisonnasse, D. Hall, and J. L. Crowley. Head pose estimation on low resolution images. In Multimodal Technologies for Perception of Humans: Proc. First IntI Workshop Classification of Events, Activities and Relationships, pages 270-280, 2007.
8. D. G. Lowe. Distinctive image features from scaleinvariant keypoints. IJCV, 60(2):91-110, 2004.
9. B. Ma, S. Shan, X. Chen, and W. Gao. Head yaw estimation from asymmetry of facial appearance. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 38(6):1501-1512, 2008.
10. Y. Mian-Shui and L. Shao-Fa. Tracking facial feature points with statistical models and gabor wavelet. In Fifth Mexican International Coriference on Artificial Intelligence, pages 61-67, Nov. 2006.
11. E. Murphy-Chutorian and M. M. Trivedi. Head pose estimation in computer vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(4):607-626, 2009.
12. S. Niyogi and W. Freeman. Example-based head tracking. pages 374-378, 1996.
13. E. Ricci and J. M. Odobez. Learning large margin likelihoods for realtime head pose tracking. In In Proceedings of the IEEE International Conference of Image Processing, 2009.
14. J. Tu, Y. Fu, Y. Hu, and T. Huang. Evaluation of head pose estimation for studio data. In Multimodal Technologies for Perception of Humans: Proc. First IntI Workshop Classification of Events, Activities and Relationships, pages 281-290, 2007.
15. V. N. Vapnik. Statistical Learning T heory. Wiley, New York, NY (USA), 1998.
16. M. Voit, K. Nickel, and R. Stiefelhagen. Neural network-based head pose estimation and multi-view fusion. In Multimodal Technologies for Perception of Humans: Proc. First IntI Workshop Classification of Events, Activities and Relationships, pages 291-298, 2007.
17. J. G. Wang and E. Sung. Em enhancement of 3d head pose estimated by point at infinity. Image and Vision Computing, 25(12):1864-1874, 2007.
18. J. Wu, J. Pedersen, D. Putthividhya, D. Norgaard, and M. M. Trivedi. A two-level pose estimation framework using majority voting of gabor wavelets and bunch graph analysis. Proc. ICPR Workshop Visual Observation of Deictic Gestures, 2004.
19. J. Xiao, S. Baker, I. Matthews, and T. Kanade. Real-time combined 2d+ 3d active appearance models. 2:535-542, 2004.
20. G. Zhao, L. Chen, J. Song, and G. Chen. Large head movement tracking using sift-based registration. pages 807-810, 2007.
21. Z. Zhiwei and J. Qiang. Robust pose invariant facial feature detection and tracking in real-time. In 18th International Conference on Pattern Recognition, pages 1092-1095, 2006.