Dealing with shadows: Capturing intrinsic scene appearance for image-based outdoor localisation

In outdoor environments shadows are common. These typically strong visual features cause considerable change in the appearance of a place, and therefore confound vision-based localisation approaches. In this paper we describe how to convert a colour image of the scene to a greyscale invariant image where pixel values are a function of underlying material property not lighting. We summarise the theory of shadow invariant images and discuss the modelling and calibration issues which are important for non-ideal off-the-shelf colour cameras. We evaluate the technique with a commonly used robotic camera and an autonomous car operating in an outdoor environment, and show that it can outperform the use of ordinary greyscale images for the task of visual localisation.

[1]  Derek Hoiem,et al.  Single-image shadow detection and removal using paired regions , 2011, CVPR 2011.

[2]  Shree K. Nayar,et al.  Vision in bad weather , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[3]  Alexei A. Efros,et al.  Detecting Ground Shadows in Outdoor Consumer Photographs , 2010, ECCV.

[4]  Michael Milford.  Visual Route Recognition with a Handful of Bits , 2012, Robotics: Science and Systems.

[5]  Cheng Lu,et al.  On the removal of shadows from images , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Shree K. Nayar,et al.  Chromatic framework for vision in bad weather , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[7]  Dirk P. Kroese,et al.  Kernel density estimation via diffusion , 2010, 1011.2602.

[8]  Sooho Park,et al.  Fast shadow detection for urban autonomous driving applications , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  David Beymer,et al.  Closed-Form Jensen-Renyi Divergence for Mixture of Gaussians and Applications to Group-Wise Shape Registration , 2009, MICCAI.

[10]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[11]  Peter I. Corke,et al.  Robotics, Vision and Control - Fundamental Algorithms in MATLAB® , 2011, Springer Tracts in Advanced Robotics.

[12]  智一 吉田,et al.  Efficient Graph-Based Image Segmentationを用いた圃場図自動作成手法の検討 , 2014 .

[13]  Shree K. Nayar,et al.  A class of photometric invariants: separating material from shape and illumination , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[14]  Cheng Lu,et al.  Intrinsic Images by Entropy Minimization , 2004, ECCV.

[15]  Paul Newman,et al.  Practice makes perfect? Managing and leveraging visual experiences for lifelong navigation , 2012, 2012 IEEE International Conference on Robotics and Automation.

[16]  Mei Han,et al.  Shadow removal for aerial imagery by information theoretic intrinsic image analysis , 2012, 2012 IEEE International Conference on Computational Photography (ICCP).

[17]  Jiejie Zhu,et al.  Learning to recognize shadows in monochromatic natural images , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Paul Newman,et al.  Self-calibration for a 3D laser , 2012, Int. J. Robotics Res..

[19]  H. Krim,et al.  Image Registration and Segmentation by Maximizing the Jensen-Rényi Divergence , 2003 .

[20]  Mark S. Drew,et al.  Sharpening from Shadows: Sensor Transforms for Removing Shadows using a Single Image , 2009, Color Imaging Conference.