Non-negative sparse coding for motion extraction

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2013, Pages

  Guthier, T ^a   Willert, V ^a   Schnall, A ^a   Kreuter, K ^a   Eggert, J ^b  

^a DARMSTADT UNIVERSITY OF TECHNOLOGY   (Germany)

^b HONDA RESEARCH INSTITUTE EUROPE GMBH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

APERTURE PROBLEMS; BRIGHTNESS CONSTANCIES; CURRENT ESTIMATION; DENSE OPTICAL FLOW; NON-NEGATIVE SPARSE CODING; OPTICAL FLOW ESTIMATION; SURFACE DEFORMATION; TRANSLATION INVARIANTS;

FEATURE EXTRACTION; NEURAL NETWORKS; OPTICAL FLOWS; TIME AND MOTION STUDY;

ESTIMATION;

EID: 84893545146     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2013.6706856     Document Type: Conference Paper

References (18)

1. T. Guthier, J. Eggert and V. Willert, "Unsupervised learning of motion patterns", European Symposium on Artificial Neural Networks (ESANN), 2012.
2. K. Jia, X. Wang and X. Tang, "Optical flow estimation using learned sparse model", IEEE Conf. on Computer Vision (ICCV), pp. 2391-2398, 2011.
3. C. Nieuwenhuis, D. Kondermann and C. S. Garbe, "Complex motion models for simple optical flow estimation", Proc. of the 32nd DAGM conf. on Pattern recognition, 2010.
4. C. F. Cadieu and B. A. Olshausen, "Learning intermediate-level representations of form and motion from natural movies", Neural computation, 24.4, pp. 827-866, 2012.
5. B. Horn and B. Schunck, "Determining optical flow", Artificial Intelligence, 16, pp. 185-203, 1981.
6. B. Lucas and I. Kanade, "An iterative image registration technique with an application to stereo vision", DARPA lmage Understanding Workshop, pp. 121-130, 1981.
7. D. Sun, S. Roth and M.J. Black, "Secrets of optical flow estimation and their principles", IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 2432-2439, 2010.
8. C.C. Chang and C.J. Lin., "LIBSVM : A library for support vector machines", ACM Transactions on Intelligent Systems and Technology, 2011.
9. D.D. Lee and S. Seung, "Learning the parts of objects by non-negative matrix factorization", Nature, vol. 401, pp. 788-791, 1999.
10. J. Eggert and E. Koerner, "Sparse coding and NMF", IEEE Int. Joint Conf. on Neural Networks (IJCNN), vol. 4, pp. 2529-2533, 2004.
11. J. Eggert, H. Wersing and E. Koerner, "Transformation-invariant representation and NMF", IEEE Int. Joint Conf. on Neural Networks (IJCNN), pp. 2535-2539 vol. 4, 2004.
12. B. Olshausen and D.J. Field, "Emergence of simple-cell receptive field properties by learning a sparse code for natural images", Nature, vol. 381, pp. 607-609, 1996.
13. P. Ekman, W.V. Friesen and J.C. Hager, "Facial action coding system", UT : A Human Face, 2002.
14. M. Valstar and M. Pantic, "Induced disgust, happiness and surprise: An addition to the mmi facial expression database", The Workshop Programme, 2010.
15. M. Valstar, M. Mehu, B. Jiang, M. Pantic and Klaus Scherer, "Meta- Analysis of the First Facial Expression Recognition Challenge", IEEE Trans. on Systems, Man, and Cybernetics-B, vol. 42(4), pp. 966-979, 2012.
16. M. Valstar and M. Pantic, "Fully Automatic Recognition of the Temporal Phases of Facial Actions", IEEE Trans. on Systems, Man, and Cybernetics-B, 42(1), pp. 28-43, 2012.
17. J. Haag and A. Borst, "Neural mechanism underlying complex receptive field properties of motion-sensitive interneurons", Nature Neuroscience, vol. 7, pp. 628-634, 2004.
18. P. Viola and M. J. Jones, "Robust real-time face detection", Int. journal of computer vision, 57.2, pp. 137-154, 2004.