Enhanced Approaches to Rectify the Noise, Illumination and Shadow Artifacts

Authors: M. Sankari, C. Meena

Abstract:

Enhancing the quality of two dimensional signals is one of the most important factors in the fields of video surveillance and computer vision. Usually in real-life video surveillance, false detection occurs due to the presence of random noise, illumination and shadow artifacts. The detection methods based on background subtraction faces several problems in accurately detecting objects in realistic environments: In this paper, we propose a noise removal algorithm using neighborhood comparison method with thresholding. The illumination variations correction is done in the detected foreground objects by using an amalgamation of techniques like homomorphic decomposition, curvelet transformation and gamma adjustment operator. Shadow is removed using chromaticity estimator with local relation estimator. Results are compared with the existing methods and prove as high robustness in the video surveillance.

Keywords: Chromaticity Estimator, Curvelet Transformation, Denoising, Gamma correction, Homomorphic, Neighborhood Assessment.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1336464

References:

[1] Anthony R. Dick and Michael J. Brooks, ” Issues in Automated Visual Surveillance”, DICTA 2003, Sydney, pp. 195-204. 2003.
[2] Hu, W., Tan, T., Wang, L. and Maybank, S. ”A Survey on Visual Surveillance of Object Motion and Behaviors”, IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, Vol. 34, No. 3, pp. 334-352, 2004.
[3] Shah, M., Javed, O. and Shafique, K., ”Automated visual surveillance in realistic scenarios”, IEEE MultiMedia, Vol.14, Issue 1, pp.30-39, 2007.
[4] Agaian, S.S., Silver, B. and Panetta, K.A., ”Transform coefficient histogram-based image enhancement algorithms using contrast entropy”, IEEE transactions on Image Processing, Vol. 16, No.3, pp. 741-758, 2007.
[5] Ovsenik, C., Turan, J. and Kolesarova, A.K., ”Video surveillance systems with optical correlator” Proceedings of the 34th International Convention on MIPRO, pp. 227-230, 2011.
[6] Sohn, H., De Neve, W. and Ro, Y.M., ”Privacy Protection in Video Surveillance Systems: Analysis of Subband-Adaptive Scrambling in JPEG XR”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 21, Issue 2, pp. 1170-177, 2011.
[7] Tinku Acharya, Ajoy K.Ray, ” Image processing: principles and applications”, John Wiley & Sons, Inc, pp.79-104. 2005.
[8] A. Levin, R. Fergus, R. Fergus, F. Durand, and W. T. Freeman, ”Image and depth from a conventional camera with a coded aperture’, ACM SIGGRAPH, Vol.26, Issue 3, July 2007.
[9] A. Veeraraghavan, R. Raskar, A. Agrawal, A. Mohan, and J. Tumblin, ”Dappled photography: Mask enhanced cameras for heterodyned light fields and coded aperture refocusing” In ACM SIGGRAPH, 2007.
[10] Pizurica, A., Zlokolica, V. Philips, W. ”Combined wavelet domain and temporal denoising”, In Proceedings of the IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Miami, FL., USA, pp. 334-341, July 2003.
[11] Scharcanski, J., Jung, C.R., Clarke, R.T. ”Adaptive Image Denoising Using Scale and Space Consistency,” IEEE Trans. Image Process. Vol.11, No. 9, pp. 1092-1101, 2002.
[12] P.L. Rosin, and T. Ellis, ”Image difference threshold strategies and shadow detection,” in Proc. Sixth British Machine Vision Conference Birmingham (UK), pp. 347-356, July 1995.
[13] G. Attolico, P. Spagnolo, A. Branca, and A. Distante, ”Fast background modeling and shadow removing for outdoor surveillance,” in Proc. Third IASTED International Conference VIIP Malaga (Spain), pp. 668-671, September 2002.
[14] G.D. Finlayson, S.D. Hordley, and M.S. Drew, ”Removing shadows from images,” in Proc. Seventh European Conference on Computer Vision - Part IV Copenaghen (Denmark), pp. 823-836, May 2002.
[15] Huaiqiang Liu, Feng Guo, ”Shadow Elimination Method for Video Surveillance”, Modern Applied Science, www.ccsenet.org/journal .html, Vol. 3. No7pp 78-85, July 2009.
[16] Yashpal Singh, Pritee Gupta, Vikram S Yadav, ” Implementation of a Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications”, IJCSNS International Journal of Computer Science and Network Security, Vol.10, No.3, pp 136-143, March 2010.