ICIP2009

• ICIP 2009

ICIP 2009 Special Session on Computational Intelligence for Reflectance Analysis

Chairs

• Ramón Moreno
• Manuel Graña

Computational Intelligence Group, UPV/EHU

Contact email

[1]

Rationale for the special session

Reflectance Analysis is a key process in computer vision systems and applications. It allows the robust segmentation of the images. It has been studied and applied in autononomous robotics, multimodal human computer interaction and remote sensing. There are some physical models of the interaction between light and the surfaces that have been used either for image rendering (visualization) or for image analysis. The most general is the BRDF model used in visualization programs. The Dichromatic Reflection Model has been proposed for the reflectance analysis of general images. This model is local and generalizations involving spatial information and non-linear effects will greatly increase its usefulness in real life applications. Knowledge Engineering and Computational Inteligence tools may provide this generalization. Other related paradigms that can benefit from the application of Computational Intelligence are Color Constancy and Retinex. The special session is intended to gather researchers applying Reflectance Analysis to real life problems and applications, or proposing innovative computational methods.

Topics of interest

• Shape from shading
• Shape from Reflection Analysis
• Isolating 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
• Computational Intellligence for Reflectance Analysis
• BTF (bidirectional texture function) and BSSRDF (bidirectional surface scattering reflectance distribution function) and widen your application areas

Program committee (tentative)

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 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 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 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 Geometric Modeling and Industrial Geometry, Institute of Discrete Mathematics and Geometry, Vienna University of Technology 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 Universität Würzburg, Germany Departament of computer and Information Science, Canada Departament of computer and Information Science, U.S.A

Paper submission

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Conference proceedings will be published by Springer-Verlag

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