Linearity of each channel pixel values from a surface in and out of shadows and its applications

Shadows, the common phenomena in most outdoor scenes, are illuminated by diffuse skylight whereas shaded from direct sunlight. Generally shadows take place in sunny weather when the spectral power distributions (SPD) of sunlight, skylight, and daylight show strong regularity: they principally vary with sun angles. In this paper, we first deduce that the pixel values of a surface illuminated by skylight (in shadow region) and by daylight (in non-shadow region) have a linear relationship, and the linearity is independent of surface reflectance and holds in each color channel. We then use six simulated images that contain 1995 surfaces and two real captured images to test the linearity. The results validate the linearity. Based on the deduced linear relationship, we develop three shadow processing applications include intrinsic image deriving, shadow verification, and shadow removal. The results of the applications demonstrate that the linear relationship have practical values.

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

[2]  Nikos Paragios,et al.  Robust shadow and illumination estimation using a mixture model , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Graham D. Finlayson,et al.  Shadow Identification using Colour Ratios , 2000, CIC.

[4]  Nicolas Martel-Brisson,et al.  Learning and Removing Cast Shadows through a Multidistribution Approach , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Nikolaos Papanikolopoulos,et al.  Learning to Detect Moving Shadows in Dynamic Environments , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Cheng Lu,et al.  Entropy Minimization for Shadow Removal , 2009, International Journal of Computer Vision.

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

[8]  Touradj Ebrahimi,et al.  Cast shadow segmentation using invariant color features , 2004, Comput. Vis. Image Underst..

[9]  P. Seitz,et al.  Optimum color filters for CCD digital cameras. , 1993, Applied optics.

[10]  Hideaki Haneishi,et al.  Color correction for colorimetric color reproduction in an electronic endoscope , 1995 .

[11]  Michael Gleicher,et al.  Texture-Consistent Shadow Removal , 2008, ECCV.

[12]  Mohan M. Trivedi,et al.  Analysis and detection of shadows in video streams: a comparative evaluation , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

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

[14]  Olfa Besbes,et al.  Moving shadow detection with support vector domain description in the color ratios space , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[15]  Hagit Hel-Or,et al.  Shadow Removal Using Intensity Surfaces and Texture Anchor Points , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Li Xu,et al.  Shadow Removal from a Single Image , 2006, Sixth International Conference on Intelligent Systems Design and Applications.

[17]  Wei Zhang,et al.  Moving Cast Shadows Detection Using Ratio Edge , 2007, IEEE Transactions on Multimedia.

[18]  Chu-Song Chen,et al.  Moving cast shadow detection using physics-based features , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Alessandro Leone,et al.  Shadow detection for moving objects based on texture analysis , 2007, Pattern Recognit..

[20]  Kuo-Liang Chung,et al.  Efficient Shadow Detection of Color Aerial Images Based on Successive Thresholding Scheme , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Jiandong Tian,et al.  Tricolor Attenuation Model for Shadow Detection , 2009, IEEE Transactions on Image Processing.

[22]  Rita Cucchiara,et al.  Detecting Moving Objects, Ghosts, and Shadows in Video Streams , 2003, IEEE Trans. Pattern Anal. Mach. Intell..