Image difference threshold strategies and shadow detection
BMVC '95 Proceedings of the 1995 British conference on Machine vision (Vol. 1)
Shadow Elimination Method for Moving Object Detection
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Moving Shadow and Object Detection in Traffic Scenes
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
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
Invisible shadow for navigation and planning in minimal invasive surgery
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
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In video sequence processing, shadow remains a major source of error of object segmentation. Traditional methods of shadow removal are mainly based on colour difference thresholding between the background and current images. The application of colour filters on MPEG or MJPEG images, however, is often erroneous as the chrominace information is significantly reduced due to compression. In addition, as the colour attributes of shadows and objects are often very similar, discrete thresholding cannot always provide reliable results. This paper presents a novel approach for adaptive shadow removal by incorporating four different filters in a neuro-fuzzy framework. The neuro-fuzzy classifier has the ability of real-time self-adaptation and training, and its performance has been quantitatively assessed with both indoor and outdoor video sequences.