Paper Info
Title
Enhanced Bayesian Foreground Segmentation Using Brightness And Color Distortion Region-Based Model For Shadow Removal
Abstract
In this paper we present a novel foreground segmentation system for monocular static camera sequences and indoor scenarios that achieves a correct shadow removal via global MAP-MRF framework formulation for the foreground, background and shadow classification task. We propose to combine a region-based spatial-color foreground model and a pixel-wise background model in the RGB domain with an spatial-Brightness Distortion(BD) and Color Distortion(CD) shadow model which present specific features to classify potential shadow regions. The results presented in the paper show the improvement of the system avoiding the necessity of thresholds for shadow detection task and reducing false positive and false negative detections originated by the shadow effects that other methods of the state of the art present.
Year
DOI
Venue
2010
10.1109/ICIP.2010.5653897
2010 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING
Keywords
Field
DocType
Foreground segmentation, shadow removal, brightness color distortion, space-color models, region models
Computer vision,Shadow,Pattern recognition,Segmentation,Computer science,Image segmentation,Artificial intelligence,Pixel,RGB color model,Contextual image classification,Distortion,Brightness
Conference
ISSN
Citations 
PageRank 
1522-4880
5
0.55
References 
Authors
7
2
Name
Order
Citations
PageRank
Jaime Gallego1564.90
Montse Pardàs234335.03