Learning Texture Discrimination Masks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image difference threshold strategies and shadow detection
BMVC '95 Proceedings of the 1995 British conference on Machine vision (Vol. 1)
Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Lighting Independent Background Subtraction
International Journal of Computer Vision - Special issue on a special section on visual surveillance
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 1)-Volume 1 - Volume 1
Shadow Elimination Method for Moving Object Detection
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Detecting Moving Shadows: Algorithms and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Atomic Decomposition by the Inhibition Method
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Efficient image representation by anisotropic refinement in matching pursuit
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Detection of moving cast shadows for object segmentation
IEEE Transactions on Multimedia
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Frame representations for texture segmentation
IEEE Transactions on Image Processing
Comparison of texture features based on Gabor filters
IEEE Transactions on Image Processing
Subband dictionaries for low-cost matching pursuits of video residues
IEEE Transactions on Circuits and Systems for Video Technology
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Moving objects tracking is an important problem in many applications such as video-surveillance. Monitoring systems can be improved using vision-based techniques able to extract and classify objects in the scene. However, problems arise due to unexpected shadows because shadow detection is critical for accurate objects detection in video stream, since shadow points are often misclassified as object points causing errors in localization, segmentation, measurements, tracking and classification of moving objects. The paper presents a new approach for removing shadows from moving objects, starting from a frame-difference method using a grey-level textured adaptive background. The shadow detection scheme uses photometric properties and the notion of shadow as semi-transparent region which retains a reduced-contrast representation of the underlying surface pattern and texture. We analyze the problem of representing texture information in terms of redundant systems of functions for texture identification. The method for discriminating shadows from moving objects is based on a Pursuit scheme using an over-complete dictionary. The basic idea is to use the simple but powerful Matching Pursuit algorithm (MP) for representing texture as linear combination of elements of a big set of functions. Particularly, MP selects the best little set of atoms of 2D Gabor dictionary for features selection representative of properties of the texture in the image. Experimental results validate the algorithm's performance.