Back
 ARS  Vol.2 No.2 , June 2013
A New Shadow Removal Method for Color Images
Abstract: Shadow and variable illumination considerably influence the results of image understanding such as image segmentation, object tracking, and object recognition. The intrinsic image decomposition is to separate the reflectance and the illumination image from an observed image. The intrinsic image decomposition is very useful to remove shadows and then improve the performance of image understanding. In this paper, we present a new shadow removal method based on intrinsic image decomposition on a single color image using the Fisher Linear Discriminant (FLD). Under the assumptions-Lambertian surfaces, approximately Planckian lighting, and narrowband camera sensors, there exist an invariant image, which is 1-dimensional greyscale and independent of illuminant color and intensity. The Fisher Linear Discriminant is applied to create the invariant image. And further the shadows can be removed through the difference between invariant image and original color image. The experimental results on real data show good performance of this algorithm.
Cite this paper: Q. He and C. Chu, "A New Shadow Removal Method for Color Images," Advances in Remote Sensing, Vol. 2 No. 2, 2013, pp. 77-84. doi: 10.4236/ars.2013.22011.
References

[1]   B. V. Funt, M. S. Drew and M. Brockington, “Recovering Shading from Color Images,” In G. Sandini, Ed., Proceedings of the Second European Conference on Computer Vision, Springer-Verlag, Berlin, 1992, pp. 124-132.

[2]   A. Prati, I. Mikic, M. M. Trivedi and R. Cucchiara, “Detecting Moving Shadows: Algorithms and Evaluation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 7, 2003, pp. 918-923.

[3]   H. Farid and E. H. Adelson, “Separating Reflections from Images by Use of Independent Components Analysis,” Journal of the optical society of America, Vol. 16, No. 9, 1999, pp. 2136-2145. doi:10.1364/JOSAA.16.002136

[4]   E. H. Land and J. J. McCann, “Lightness and Retinex Theory,” Journal of the Optical Society of America, Vol. 61, No. 1, 1971, pp. 1-11. doi:10.1364/JOSA.61.000001

[5]   R. Szeliksi, S. Avidan and P. Anandan, “Layer Extraction from Multiple Images Containing Reflections and Transparency,” Proceedings of the IEEE conference Computer Vision and Pattern Recognition, Hilton Head Island, June 2000, pp. 246-253.

[6]   H. G. Barrow and J. M. Tenenbaum, “Recovering Intrinsic Scene Characteristics from Images,” In A. Hanson and E. Riseman, Eds., Computer Vision Systems, Academic Press, Waltham, 1978.

[7]   Y. Matsushita and K. Nishino, “Illumination Normalization with Time-Dependent Intrinsic Images for Video Surveillance,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, No. 10, 2004, pp. 1336-1347. doi:10.1109/TPAMI.2004.86

[8]   Y. Weiss, “Deriving Intrinsic Images from Image Sequences,” Proceedings of the IEEE International Conference on Computer Vision, Vancouver, 7-14 July 2001, pp. 68-75.

[9]   M. Baba and N. Asada, “Shadow Removal from a Real Picture,” Proceedings of the SIGGRAPH 2003 Conference on Sketches & Applications, San Diego, 30-31 July 2003, p. 1.

[10]   M. F. Tappen, W. T. Freeman and E. H. Adelson, “Recovering Intrinsic Images from a Single Image,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 9, 2005, pp. 1459-1472.

[11]   B. A. Olshausen and D. J. Field, “Emergence of Simple Cell Receptive Field Properties by Learning a Sparse Code for Natural Images,” Nature, Vol. 381, 1996, pp. 607-608. doi:10.1038/381607a0

[12]   E. P. Simoncelli, “Statistical Models for Images: Compression Restoration and Synthesis,” Proceedings of Asilomar Conference on Signals, Systems and Computers, Pacific Grove, 2-5 November 1997, pp. 673-678.

[13]   G. D. Finlayson, M. S. Drew and C. Lu, “Intrinsic Images by Entropy Minimization,” Proceedings of the 8th European Conference on Computer Vision, Prague, 11-14 May 2004, pp. 582-595. http://www.cs.sfu.ca/~mark/ftp/Eccv04/

[14]   G. D. Finlayson, S. D. Hordley and M. S. Drew, “Removing Shadows from Images,” ECCV 2002: European Conference on Computer Vision, Copenhagen, 27 May-2 June 2002, pp. 823-836.

[15]   R. O. Duda, P. E. Hart and D. G. Stork, “Pattern Classification,” 2nd Edition, Wiley-Interscience, New York, 2000.

[16]   J. B. MacQueen, “Some Methods for Classification and Analysis of Multivariate Observations,” In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, Berkeley, 1967, pp. 281-297.

 
 
Top