NDLI: Skin color-based video segmentation under time-varying illumination

Content Provider	IEEE Xplore Digital Library
Author	Sigal, L. Sclaroff, S. Athitsos, V.
Copyright Year	1979
Description	A novel approach for real-time skin segmentation in video sequences is described. The approach enables reliable skin segmentation despite wide variation in illumination during tracking. An explicit second order Markov model is used to predict evolution of the skin-color (HSV) histogram over time. Histograms are dynamically updated based on feedback from the current segmentation and predictions of the Markov model. The evolution of the skin-color distribution at each frame is parameterized by translation, scaling, and rotation in color space. Consequent changes in geometric parameterization of the distribution are propagated by warping and resampling the histogram. The parameters of the discrete-time dynamic Markov model are estimated using maximum likelihood estimation and also evolve over time. The accuracy of the new dynamic skin color segmentation algorithm is compared to that obtained via a static color model. Segmentation accuracy is evaluated using labeled ground-truth video sequences taken from staged experiments and popular movies. An overall increase in segmentation accuracy of up to 24 percent is observed in 17 out of 21 test sequences. In all but one case, the skin-color classification rates for our system were higher, with background classification rates comparable to those of the static segmentation.
Sponsorship	IEEE Computer Society
Page Count	16
File Size	4626591
Starting Page	862
Ending Page	877
File Format	PDF
ISSN	01628828
Volume Number	26
Issue Number	7
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-07-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Skin Lighting Histograms Motion pictures Image segmentation Video sequences Predictive models Image sequences Robustness Light sources dynamic Markov model. Color video segmentation human skin detection
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Computational Theory and Mathematics 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

In Situ Image Segmentation Using the Convexity of Illumination Distribution of the Light Sources

Color space analysis of mutual illumination

Integrating Motion, Illumination, and Structure in Video Sequences with Applications in Illumination-Invariant Tracking

Face detection and tracking in a video by propagating detection probabilities

Segmentation and Estimation of Spatially Varying Illumination

Statistical analysis of dynamic actions

A dynamic conditional random field model for foreground and shadow segmentation

Efficient region tracking with parametric models of geometry and illumination

Color invariance

Skin color-based video segmentation under time-varying illumination

Similar Documents

In Situ Image Segmentation Using the Convexity of Illumination Distribution of the Light Sources

Color space analysis of mutual illumination

Integrating Motion, Illumination, and Structure in Video Sequences with Applications in Illumination-Invariant Tracking

Face detection and tracking in a video by propagating detection probabilities

Segmentation and Estimation of Spatially Varying Illumination

Statistical analysis of dynamic actions

A dynamic conditional random field model for foreground and shadow segmentation

Efficient region tracking with parametric models of geometry and illumination

Color invariance

Skin color-based video segmentation under time-varying illumination