Shadow detecting using particle swarm optimization and the Kolmogorov test

An algorithm combining both gray level information and geometric features is introduced to detect cast shadows in gray level images. A simply connected candidate shadow region and a corresponding region are segmented by setting gray level thresholds, and neighbor-matching regions are constructed with a mathematical morphological algorithm. A shadow-non-shadow region pair is obtained from the result of Kolmogorov test for statistical features of both candidate neighbor-matching regions. Shadow regions are obtained by selecting the region with relatively lower average gray level from the matched region pair. The particle swarm optimization (PSO) algorithm is used to facilitate the feature extraction during the matching process. Experimental results showed the effectiveness of the proposed algorithm for cast shadow detecting in a single gray level image.

[1]  Jörn Ostermann,et al.  Shadow detection for moving humans using gradient-based background subtraction , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[2]  Chris Chatwin,et al.  On shadow elimination after moving region segmentation based on different threshold selection strategies , 2007 .

[3]  Atsuto Maki,et al.  Self shadows and cast shadows in estimating illumination distribution , 2007 .

[4]  Jun-Wei Hsieh,et al.  Shadow elimination for effective moving object detection by Gaussian shadow modeling , 2003, Image Vis. Comput..

[5]  R. Zamar,et al.  A multivariate Kolmogorov-Smirnov test of goodness of fit , 1997 .

[6]  Touradj Ebrahimi,et al.  Shadow identification and classification using invariant color models , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[7]  Murray H. Loew,et al.  The Entry-Exit Method of Shadow Boundary Segmentation , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Fatih Murat Porikli,et al.  Shadow flow: a recursive method to learn moving cast shadows , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[9]  Christopher O. Jaynes,et al.  Moving Shadow Detection using a Combined Geometric and Color Classification Approach , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

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