Diferencia entre revisiones de «KES2009 Reflectance Analysis»

De Grupo de Inteligencia Computacional (GIC)
Línea 96: Línea 96:
;Georges Stamon
;Georges Stamon
;Jean Louchet
;Jean Louchet
;Shinji Umeyama
;Guy Godin
;Guy Godin
;Todd Zickler
;Todd Zickler
Línea 155: Línea 154:
:Université de Paris, France
:Université de Paris, France
:Equipe COMPLEX, INRIA, France
:Equipe COMPLEX, INRIA, France
:Equipe COMPLEX, INRIA, France
:Harvard University, USA
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Revisión del 17:38 15 oct 2008

Special Session on Reflectance Analysis
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Chairs

Computational Intelligence Group, UPV/EHU

Contact email

[1]

Description

El anlálisis de Reflectancia es un proceso fundamental en todos sistema de visión por computador. El conocimineto de las propiedades ópticas de los materiales y el comportamiento de la luz sobre estos, es lo que nos permite asociar la información contenida en la imagen con la realidad observada. Varios modelos de reflectancia han sido propuestos ( Dichromatic reflection model, BRDF y sus derivados) su aplicación es fundamental tanto para visión como para visualización. Son muchos los paradigmas concernientes; Color constancy, espacios de color, estudio de la luz, propiedades de los materiales, optics, photometry, radiometry,...

El gran objetivo de esta Special Session es tener un punto de encuentro para investigadores en este area, algo inusual en otros congresos.

Reflectance Analysis is a key process for computer vision systems. The knowledge of optical and phisycs properties of materials under light efects, let us to find the relacionship between image information and observed reality. Some models had been proposed for reflectance (Dichromatic Reflection Model, BRDF and their derivatives) their application is key for vision process and for visualization process too. A lot of paradigms are related to reflectance analysis; color constancy, color spaces, light research, phisycs properties of materials, optics, photometry, radiometry, ...

The goal of this Special Session is to serve a meeting point for researches in this area.

Important dates

Topics of interest

Shape from shading
Shape from Reflection Analysis
Sepaparating Specular Component
Reflectance Maps
Reflectance Models
Specular and Lambertian Surfaces
Geometrics and Photometrics Invariants
Methods for Chromatic Illummination Estimation
Color Spaces for Reflectance Analysis
Mathematical Morphology of Color Spaces and their applicantion in Reflectance Analysis
Color Constancy
Shadows

Program committee

Bruce A. Maxwell
Richard M. Friedhoff
Casey A. Smith
H. Ragheb
R. Hancock
Stephen Grossberg
Z.G. Pan
Jiuai Sun
J.H. Xin
P. Kakumanu
N. Bourbakis
Hui-Liang Shen
Thomas M. Lehmann
Todd Zickler
Yihong Wu
Kuk-Jin Yoon
Yoo Jin Choi
In-So Kweon
Javier Toro
Sei-Wang Chen
Ron O. Dror
Edward H. Adelson
Alan S. Willsky
Katsushi Ikeuchi
Robby T. Tan
Yoichi sato
Ko Nishino
Imari Sato
Yana Mileva
Andrés Bruhn
Joachim Weickert
Lavanya Sharan
A. Smolarz
Peter Orbanz
Lin Li
Gus Wiseman
Jun’ichiro Seyama
J. Lellmann
J. Balzer
A. Rieder
J. Beyerer
Rogerio Feris
Ramesh Raskar
Karhan Tan
Matthew Turk
Roger Trias-Sanz
Georges Stamon
Jean Louchet
Guy Godin
Todd Zickler
Satya P. Mallick
David J. Kriegman
Peter N. Belhumeur
Marc Ebner
Harold B. Westlund
Gary W. Meyer
Tandent Vision Science, Inc. USA
Tandent Vision Science, Inc. USA
Tandent Vision Science, Inc. USA
Digital Imaging Research Centre, Kingston University London
Department of Computer Science, University of York
Laboratory of Sensorimotor Research, USA
State Key Lab of CAD&CG, Zhejiang University, China
Machine Vision Lab, Faculty of CEMS, University of the West of England, UK
The Hong Kong Polytechnic University, Hong Kong, China
ITRI/Department of Computer Science and Engineering, Wright State University, USA
ITRI/Department of Computer Science and Engineering, Wright State University, USA
Department of Information and Electronic Engineering, Zhejiang University, China
Institute of Medical Informatics, Aachen University of Technology, Germany
Harvard University, Cambridge, USA
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Robotics and Computer Vision Lab. Dept. of EECS, KAIST, Korea
Mobile Multimedia Lab. LG Electronics Institute of Technology
Robotics and Computer Vision Lab. Dept. of EECS, KAIST, Korea
Grupo de Ingeniería Biomédica, Universidad de los Andes, Venezuela
Department of Computer Science and Information Engineering National Taiwan Normal University, Taiwan
Department of Electrical Engineering and Computer Science, MIT
Department of Brain and Cognitive Sciences, MIT
Department of Electrical Engineering and Computer Science, MIT
Department of Computer Science The University of Tokyo
Department of Computer Science The University of Tokyo
The University of Tokyo
Department of Computer Science Columbia University
The University of Tokyo
Mathematical Image Analysis Group, Saarland University, Germany
Mathematical Image Analysis Group, Saarland University, Germany
Mathematical Image Analysis Group, Saarland University, Germany
Department of Electrical Engineering and Computer Science, MIT
University of Technology of Troyes, ICD Laboratory FRE CNRS, FRANCE
Institute of Computational Science, ETH Zürich
Department of Earth Sciences, Indiana University – Purdue University, USA
Department of Mathematics, University of California
Department of Psychology, Tokyo, Japan
Informatik (ITEC), Department of Computer Science, University of Karlsruhe, Germany
Informatik (ITEC), Department of Computer Science, University of Karlsruhe, Germany
Informatik (ITEC), Department of Computer Science, University of Karlsruhe, Germany
Department of Mathematics, University of Karlsruhe, Karlsruhe, Germany
UCSB–University of California, Santa Barbara, USA
MERL–Mitsubishi Electric Research Labs, Cambridge, USA
MERL–Mitsubishi Electric Research Labs, Cambridge, USA
UCSB–University of California, Santa Barbara, USA
Institut Géographique National, France
Université de Paris, France
Equipe COMPLEX, INRIA, France
Equipe COMPLEX, INRIA, France
Harvard University, USA




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