Advertisement

A Novel Technique of Shadow Detection Using Color Invariant Technique

  • Leeza Panda
  • Bibhuprasad MohantyEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 109)

Abstract

One of the major challenges for computer vision-based lane detection is the manifestation of shadows and other vehicles. It is challenging to diagnose the chaotic lanes when it has both the shadowed and unshadowed areas. Detection and removal of shadow is the procedure to intensify the computer vision application including image processing, video processing, etc. Shadows are partial darkness or obscurity within a part of space from which rays from a source of light are cut off by an interposed opaque body. The presence of shadows results in severe issues in road detection as the bounding line of shadows can be incorrectly recognized leading to a higher false rate detection. This paper attempts a simple and efficient algorithm to detect shadows in an image by finding out the shadow ratio and calculating the threshold and then creating a shadow boundary.

Keywords

Road detection Shadow detection Thresholding Shadow ratio 

References

  1. 1.
    Wang JM, Chung YC, Chang CL, Chen SW (2004) Shadow detection and removal for traffic images. In: 2004 IEEE international conference on networking, sensing and control, vol 1, 21–23 Mar 2004, pp 649–654Google Scholar
  2. 2.
    Bevilacqua A (2003) Effective shadow detection in traffic monitoring applications. WSCGGoogle Scholar
  3. 3.
    Chen T, Yin W, Zhou XS, Comaniciu D, Huang TS (2005) Illumination normalization for face recognition and uneven background correction using total variation based image models. CVPR 2(2005):532–539Google Scholar
  4. 4.
    Adini Y, Moses Y, Ullman S (1997) Face recognition: the problem of compensating for changes in illumination direction. IEEE Trans Pattern Anal Mach Intell 19(7):721–732CrossRefGoogle Scholar
  5. 5.
    Zhao W, Chellappa R (1999) Robust face recognition using symmetric shape-from-shading. Technical report. Center for Automation Research, University of MarylandGoogle Scholar
  6. 6.
    Finlayson GD, Hordley SD, Drew MS (2002) Removing shadows from images. In: Proceedings of the 7th European conference on computer vision, pp 823–836CrossRefGoogle Scholar
  7. 7.
    Fredembach C, Finlayson, GD (2004) Fast re-integration of shadow free images. In: Proceeding color imaging conferenceGoogle Scholar
  8. 8.
    Lalonde J-F, Efros AA, Narasimhan SG (2010) Detecting ground shadows in outdoor consumer photographs. In: Proceeding 11th European conference computer visionCrossRefGoogle Scholar
  9. 9.
    Wu TP, Tang CK (2005) A Bayesian approach for shadow extraction from a single image. In: Proceedings of the IEEE international conference on computer vision, Beijing, China, vol 1, 17–21 Oct 2005, pp 480–487Google Scholar
  10. 10.
    Salvador E, Cavallaro A, Ebrahimi T (2004) Cast shadow segmentation using invariant color features. Comput Vis Image Underst 95(2):238–259CrossRefGoogle Scholar
  11. 11.
    Guo R, Dai Q, Hoiem D (2013) Paired regions for shadow detection and removal. IEEE Trans Pattern Anal Mach Intell 35(12):2956–2967CrossRefGoogle Scholar
  12. 12.
    Salvador E, Cavallaro A, Ebrahimi T (2001) Shadow identification and classification using invariant color models. In: Proceedings of IEEE international conference on acoustics, speech, and signal processing, Salt Lake City, USA, vol 3, 7–11 May 2001, pp 1545–1548Google Scholar
  13. 13.
    Shor Y, Lischinski D (2008) The shadow meets the mask: pyramid-based shadow removal. Comput Graph Forum 27(2):577–586CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringITER, Siksha ‘O’ Anusandhan University (Deemed to be University)BhubaneswarIndia

Personalised recommendations