A generative model for separating illumination and reflectance from images

Journal of Machine Learning Research

Volumn 4, Issue 7-8, 2004, Pages 1499-1519

  Stainvas, Inna ^a   Lowe, David ^b  

^a ORBOTECH LTD   (Israel)

^b ASTON UNIVERSITY   (United Kingdom)

Author keywords

General autoregressive model; Illumination; Potts model; Reflectance; Segmentation

Indexed keywords

GRAPHIC METHODS; HIGH PASS FILTERS; INDEPENDENT COMPONENT ANALYSIS; LIGHT REFLECTION; MATHEMATICAL MODELS; SENSORS;

GENERAL AUTOREGRESSIVE MODEL; ILLUMINATED IMAGES; POTTS MODEL; SEGMENTATION;

IMAGE SEGMENTATION;

EID: 8344240490     PISSN: 15324435     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (45)

1. E. H. Adelson and A. P. Pentland. The perception of shading and reflectance. In D. Knill and W. Richards, editors, Perception as Bayesian Inference, pages 409-423. New York: Cambridge University Press, 1996.
2. H. Attias. Independent factor analysis. Neural Computation, 11(4):803-851, 1999.
3. H. Attias. Independent factor analysis with temporally structured factors. In Leen et al., editor, Advances in Neural Information Processing Systems, volume 12, pages 73-80. MIT Press, Cambridge, MA, 2000.
4. H. G. Barrow and J. M. Tenenbaum. Recovering intrinsic scene characteristics from images. In A. Hanson and E. Riseman, editors, Computer Vision Systems, pages 3-26. New York: Academic Press, 1978.
5. P. Belhumeur and D. Kreigman. What is the set of images of an object under all possible illumination conditions? Int. Journal of Computer Vision, 28(3):245-269, 1998.
6. J. Besag. On the statistical analysis of dirty pictures. Journal Royal Statistical Society, Series B, 48: 259-302, 1986.
7. C. Bishop. Curvature-driven smoothing: A learning algorithm for feed-forward networks. IEEE Transactions on Neural Networks, 4(5):882-884, 1993.
8. C. Bishop. Mixture density networks. Technical report, ncrg/94/004, NCRG, Aston University, 1994.
9. C. M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, 1995. Available electronically from http://neural-server.aston.ac.uk/ NNPR/.
10. D. S. Broomhead and D. Lowe. Multivariable functional interpolation and adaptive networks. Complex Systems, 2:321-355, 1988.
11. J. Canny. A computational approach to edge detection. PAMI, 8(6), November 1986.
12. C. Carson, S. Belongie, H. Greenspan, and J. Malik. Blobworld: A system for region-based image indexing and retrieval. In Third Int. Conf. on Visual Information Systems, pages 1-8. Springer-Verlag, Junes 1999. Available electronically from http://www.cs.berkeley.edu/~carson/research/publications. html.
13. G. Celeux, F. Forbes, and N. Peyrard. EM procedures using mean filed-like approximations for Markov model-based image segmentation. Pattern Recognition, 36:131-144, 2003.
14. R. Chellappa and A. Jain. Markov Random Fields: Theory and Applications. Academic Press, 1993.
15. R. R. Coifman and M. Wickerhauser. Entropy-based algorithms for best basis selection. IEEE Trans. Info. Theory, 38(2):713-719, 1992. Available electronically from http://wuarchive.wust1.edu/doc/techreports/wust1.edu/math/ papers/entbb.ps.Z.
16. R. Everson and S. Roberts. Particle filters for non-stationary ICA. In Richard Everson and Stephen Roberts, editors, ICA: Principles and Practice, pages 280-298. Cambridge University Press, Cambridge, 2001a.
17. Richard Everson and Stephen Roberts, editors. ICA: Principles and Practice. Cambridge University Press, Cambridge, 2001b.
18. H. Farid and E. H. Adelson. Separating reflections from images using independent component analysis. Journal of the Optical Society of America, 9(1):2136-2145, 1999.
19. M. N. Gibbs and David J. C. MacKay. Efficient implementation of Gaussian processes. Available electronically from http://www.cs.toronto.edu/~mackay/ gpros.ps.gz. Preprint, 1997.
20. R. C. Gonzalez and P. Wintz. Digital Image Processing. Addison-Wesley Publishing Company, 1993.
21. L. U. Hjorth and I. T. Nabney. Regularisation of mixture density networks. In International Conference on Artificial Neural Networks, Edinburgh, pages 521-526, 1999.
22. B. K. P. Horn. Robot Vision. The MIT Press, New York, 1986.
23. A. K. Jain. Fundamentals of Digital Image Processing. Prentice Hall, London, 1989.
24. M. Jordan, editor. Learning in Graphical Models. The MIT Press, Cambridge, Massachusetts, London, England, 1999.
25. S-K. Laszlo. Monte-Carlo Methods in Global Illumination. Available electronically from http://www.iit.bme.hu/~szirmay/puba.html. Book Preprint, 2000.
26. S. Z. Li. Markov Random Field Modeling in Computer Vision. Springer, 1995.
27. D. Marr. Vision. Imprint FREEMAN, New York, 1982.
28. R. Morris, X. Descombes, and J. Zerubia. Fully Bayesian Image Segmentation - an Engineering Perspective. Technical report, n 3017, INRIA, October 1996. Available electronically from name@sophia.inria.fr.
29. M.S.Lewicki and T. J. Sejnowski. Learning overcomplete representations. Neural Computation, 12 (2):337-365, 2000. Available electronically from http://www.cnl.salk.edu/~lewicki/.
30. I. T. Nabney. Advances in Pattern Recognition. Springer, London, 2001.
31. K. Ohba, Y. Sato, and K. Dceuchi. Appearance-based visual learning and object recognition with illumination invariance. Machine Vision and Applications, 12(4), 189-196 2000. Available electronically from http://www.aist.go.jp/MEL/soshiki/robot/biorobo/ooba/cv.html.
32. Manfred Opper and David Saad, editors. Advanced Mean Field Methods. The MIT Press, Cambridge, 2001.
33. B. A. Pearlmutter and L. C. Parra. Maximum likelihood bund source separation: A context-sensitive generalization of ica. In D. S. Touretzky and R. P. Lippmann, editors, NIPS, volume 9, pages 613-619. MIT Press, 1997.
34. W. Penny, R. Everson, and S. Roberts. Hidden markov independent component analysis. In M. Girolami, editor, Advances in Independent Component Analysis. Springer, 2000.
35. W. Penny and S. Roberts. Variational Bayes for generalised autoregressive models. To appear in IEEE Transactions on Signal Processing, pages 1-22, 2002.
36. William H. Press, Saul A. Teukolsky, William T. Vetterling, and Brian P. Flannery. Numerical Recipes in C: The Art of Scientific Computing. Amazon, Cambridge, 2002. second edition.
37. S. Roberts and R. Choudrey. Data decomposition using independent component analysis with prior constraints. Pattern Recognition, 36(8), 2003. Available electronically from http://www.robots.ox.ac.uk/~parg/publications. html.
38. N. Saito. Image approximation and modeling via least statistically dependent bases. Pattern Recognition, 34:1765-1784, June 2001.
39. Peter Schroder. Wavelet Algorithms for Illumination Computation. PhD thesis, Computer Science, Princeton University, 1994. Available electronically from http://www.math.tau.ac.il/~stainvas.
40. I. Stainvas and D. Lowe. Towards sea surface pollution detection from visible band images. IEICE Transactions on Electronics a Special Issue on New Technologies in Signal Processing for Electromagnetic-wave Sensing and Imaging, E84C( 12): 1848-1856, December 2001. Available electronically from http://www.www.ncrg.aston.ac.uk/~stainvai/spolb.ps.gz.
41. C. Stauffer and W.E.L Grimson. Adaptive background mixture model for real-time tracking. In IEEE Computer Society Conference om Computer Vision and Patten Recognition, Cat. No. PR00149, volume 2, page IEEE Comput. Soc. part, 1999. Available electronically from http://www.ai.mit.edu/projects/vsam/ Tracking/.
42. Z. Tauber, Ze-Nian Li, and M. S. Drew. Local-based visual object retrieval under illumination cnange. In Proceedings of the Computer Vision and Pattern Recognition, volume 4, pages 43-46, Barcelona, 2000.
43. Y. Weiss. Deriving intrinsic images from image sequences. In Int. Conf. Computer Vision, number 8, pages 1-8, Vancouver, Canada, 2001.
44. W. M. Wells, R. Kikins, W. Grimson, and F. Jolesz. Adaptive segmentation of MRI data. IEEE Transactions on Medical Imaging, (15):429-442, 1996.
45. Y. Zhang, S. Smith, and M. Brady. A Hidden Markov Random Field Model for Segmentation of Brain MR Images. In SPIE Medical Imaging, pages 1126-1137, San-Diego, USA, February 2000. Available electronically from http://www.fmrib.ox.ac.uk/~yongyue/mriseg.html.