Residual images remove illumination artifacts!

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5748 LNCS, Issue , 2009, Pages 472-481

  Vaudrey, Tobi ^a   Klette, Reinhard ^a  

^a UNIVERSITY OF AUCKLAND   (New Zealand)

Author keywords

[No Author keywords available]

Indexed keywords

COMMON PROBLEMS; CORRESPONDENCE ALGORITHM; ILLUMINATION EFFECT; REAL-LIFE IMAGES; RESIDUAL IMAGES;

PATTERN RECOGNITION;

OPTICAL FLOWS;

EID: 70350442845     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-03798-6_48     Document Type: Conference Paper

References (16)

1. Abramson, S.B., Schowengerdt, R.A.: Evaluation of edge-preserving smoothing filters for digital image mapping. ISPRS J. Photogrammetry Remote Sensing 48, 2-17 (1993)
2. Aujol, J.F., Gilboa, G., Chan, T., Osher, S.: Structure-texture image decomposition-modeling, algorithms, and parameter selection. Int. J. Computer Vision 67, 111-136 (2006)
3. Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. In: Proc. IEEE Int. Conf. Computer Vision, pp. 1-8(2007)
4. Choudhury, P., Tumblin, J.: The trilateral filter for high contrast images and meshes. In: Proc. Eurographics Symp. Rendering, pp. 1-11 (2003)
5. .enpeda.. Image Sequence Analysis Test Site (EISATS):http://www.mi. auckland.ac.nz/EISATS
6. Haralick, R.M., Bosley, R.: Texture features for image classification. In: Proc. ERTS Symposium, NASA SP-351, pp. 1219-1228 (1973)
7. Kuan, D.T., Sawchuk, A.A., Strand, T.C., Chavel, P.: Adaptive noise smoothing filter for images with signal-dependent noise. IEEE Trans. Pattern Analysis Machine Intelligence 7,165-177 (1985)
8. Mao, Z., Strickland, R.N.: Image sequence processing for target estimation in forward-looking infrared imagery. Opt. Eng. 27, 541-549 (1988)
9. Middlebury data set: optical flow data, http://vision.middlebury.edu/ flow/data/and stereo data, http://vision.middlebury.edu/stereo/data/
10. Morales, S., Woo, Y.W., Klette, R., Vaudrey, T.: A study on stereo and motion data accuracy for a moving platform. Technical report, MI-tech-32 University of Auckland (2009), http://www.mi.auckland.ac.nz/
11. Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259-268 (1992)
12. Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Computer Vision 47, 7-42 (2002)
13. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proc. IEEE Int.Conf. Computer Vision, pp. 839-846 (1998)
14. Vaudrey, T., Rabe, C., Klette, R., Milburn, J.: Differences between stereo and motion behaviour on synthetic and real-world stereo sequences. In: IEEE Conf. Proc. IVCNZ (2008),doi:10.1109/IVCNZ.2008.4762133
15. Wedel, A., Pock, T., Zach, C., Bischof, H., Cremers, D.: An improved algorithm for TV-L1 optical flow. In: Post Proc. Dagstuhl Motion Workshop (to appear, 2009)
16. Zach, C., Pock, T., Bischof, H.: A duality based approach for realtime TV-L1 optical flow. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 214-223. Springer, Heidelberg (2007)