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This article presents a novel method for identifying and removing moving shadows using stationary wavelet transform (SWT) based on a threshold determined by wavelet coefficients.
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Cucchiara et al. [2] removed moving shadows by using the concept on HSV color space that the hue component of shaded pixels would vary in a smaller range and the saturation component would decrease more obviously.
In [19], moving shadows were detected by using its motion characteristics and the underlying physics based on the color segmentation, this method had poor accuracy since it could not distinguish shadows from black objects.
Such patterns are used to improve foreground detection in the presence of moving shadows and strong similarities in color between background and foreground.
The angular difference is used because it can be considered as photometric invariant of color measurement and in turn as significant cues to detect moving shadows.
Most shadow detection methods focus on detecting moving shadows.
Then, the problem of shadow detection becomes differentiating the moving objects and the moving shadows.
Moving shadow detection methods can employ the frame difference technique to locate moving objects and their moving shadows.
Decorrelation is caused by both moving reflectors (blood cells) and moving shadows (projected on distal high reflectance tissue).
The existing moving shadow detection methods in wavelet domain using DWT lack in translation invariance and loss phase information during reconstruction.
Therefore, the majority of moving shadow detection methods cannot be directly used to detect static shadows in single images.
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