The Entry-Exit Method of Shadow Boundary Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Moving Shadow Detection using a Combined Geometric and Color Classification Approach
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Shadow Flow: A Recursive Method to Learn Moving Cast Shadows
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Shadow identification and classification using invariant color models
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Learning and Removing Cast Shadows through a Multidistribution Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shadow detection for moving humans using gradient-based background subtraction
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Computers & Mathematics with Applications
Data fusion of multi-sensor for IOT precise measurement based on improved PSO algorithms
Computers & Mathematics with Applications
Computers & Mathematics with Applications
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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.