NDLI: Kernel-based learning of cast shadows from a physical model of light sources and surfaces for low-level segmentation

Content Provider	IEEE Xplore Digital Library
Author	Martel-Brisson, N. Zaccarin, A.
Copyright Year	2008
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Laval Univ., Quebec City, QC (Martel-Brisson, N.; Zaccarin, A.)
Abstract	In background subtraction, cast shadows induce silhouette distortions and object fusions hindering performance of high level algorithms in scene monitoring. We introduce a nonparametric framework to model surface behavior when shadows are cast on them. Based on physical properties of light sources and surfaces, we identify a direction in RGB space on which background surface values under cast shadows are found. We then model the posterior distribution of lighting attenuation under cast shadows and foreground objects, which allows differentiation of foreground and cast shadow values with similar chromaticity. The algorithms are completely unsupervised and take advantage of scene activity to learn model parameters. Spatial gradient information is also used to reinforce the learning process. Contributions are two-fold. Firstly, with a better model describing cast shadows on surfaces, we achieve a higher success rate in segmenting moving cast shadows in complex scenes. Secondly, obtaining such models is a step toward a full scene parametrization where light source properties, surface reflectance models and scene 3D geometry are estimated for low-level segmentation.
Starting Page	1
Ending Page	8
File Size	1819969
Page Count	8
File Format	PDF
ISBN	9781424422425
ISSN	10636919
DOI	10.1109/CVPR.2008.4587447
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Light sources Layout Solid modeling Geometry Lighting Reflectivity Object recognition Video sequences Attenuation Computer vision
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Shadow flow: a recursive method to learn moving cast shadows

Illumination distribution from shadows

Capturing long-range correlations with patch models

Robust shadow and illumination estimation using a mixture model

Physics-based segmentation: moving beyond color

Bayesian color constancy for outdoor object recognition

Color constancy using 3D scene geometry

Shedding light on stereoscopic segmentation

Learning and Removing Cast Shadows through a Multidistribution Approach

Kernel-based learning of cast shadows from a physical model of light sources and surfaces for low-level segmentation

Similar Documents

Shadow flow: a recursive method to learn moving cast shadows

Illumination distribution from shadows

Capturing long-range correlations with patch models

Robust shadow and illumination estimation using a mixture model

Physics-based segmentation: moving beyond color

Bayesian color constancy for outdoor object recognition

Color constancy using 3D scene geometry

Shedding light on stereoscopic segmentation

Learning and Removing Cast Shadows through a Multidistribution Approach

Kernel-based learning of cast shadows from a physical model of light sources and surfaces for low-level segmentation