ACM Transactions on Graphics (TOG)
Tricolor attenuation model for shadow detection
IEEE Transactions on Image Processing
Self shadow elimination algorithm for surveillance videos using ANOVA F test
Proceedings of the Third Annual ACM Bangalore Conference
Geometric constraints for human detection in aerial imagery
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Poisson cloning using bilateral image coarsening
ICIMCS '10 Proceedings of the Second International Conference on Internet Multimedia Computing and Service
Shadow Casting Out Of Plane (SCOOP) Candidates for Human and Vehicle Detection in Aerial Imagery
International Journal of Computer Vision
A method to estimate Grape Phenolic Maturity based on seed images
Computers and Electronics in Agriculture
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Shadow detection and removal in real scene images is always a challenging but yet intriguing problem. In contrast with the rapidly expanding and continuous interests on this area, it is always hard to provide a robust system to eliminate shadows in static images. This paper aimed to give a comprehensive method to remove both vague and hard shadows from a single image. First, classification is applied to the derivatives of the input image to separate the vague shadows. Then, color invariant is exploited to distinguish the hard shadow edges from the material edges. Next, we derive the illumination image via solving the standard Poisson equation. Finally, we got the shadow-free reflectance image. Experimental results showed that our method can robustly remove both vague and hard shadows appearing in the real scene images.