[ML-news] CFP Subspace2010: ACCV2010 workshop on Subspace Methods

[Toru Tamaki]

Call for Papers
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Subspace 2010 -- The 3rd International Workshop on Subspace Methods

November 9, 2010, in conjunction with ACCV2010, Queenstown, New
Zealand

http://www.cvlab.cs.tsukuba.ac.jp/~subspace/ss2010/
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Dates (tentative):
Paper submission deadline Aug 20 2010
Notification of acceptance Sep 25 2010
Camera ready due Oct 5 2010
Workshop date Nov 9 2010


GOAL OF THE WORKSHOP
The goal of the workshop is to share the potential of subspace
methods with researchers working on various problems in computer
vision and pattern recognition, and to encourage interactions which
could lead to further developments of subspace methods. Both the
fundamental theories of subspace methods and their applications in
computer vision will be discussed at the workshop.

SUBSPACE METHODS
Subspace methods have been used as a practical methodology in a large
variety of real applications. Also they have been studied intensively,
in particular, in the field of character recognition, contributing to
a number of commercial optical character recognition systems. During
the last three decades, the area has become one of the most successful
underpinnings of diverse applications such as classification,
recognition, pose estimation, motion estimation, etc. At the same
time, there are many new and evolving research topics: nonlinear
methods including kernel methods, manifold learning, subspace update
and tracking.

We expect that the workshop could accelerate the stream, by providing
the place to share the potential of subspace methods with researchers
working on various problems in computer vision and pattern
recognition, and encouraging interactions which could lead to further
developments of subspace methods.


PROCEEDINGS
Post-conference proceedings will be published in Springer's Lecture
Notes in Computer Science (both printed and electronic forms).
Also, on-site electronic copy will be available at ACCV.



SCOPE
The topics of interest include, but are not limited to, the following:

- Theory
PCA, LDA, CCA, FA, ICA, etc (linear, nonlinear/kernelized),
multivariate analysis
Tensor and multilinear representations
CLAFIC, mutual subspace methods, eigenspace methods
Nonlinear subspace methods, including kernel methods
Manifold learning
Similarity measures with subspaces
Iijima equation, degenerated Gaussians, geometry of subspaces, etc.

- Subspaces in various problems
Factorization methods, 3D geometry, local features, photometry and
Illumination constraints, analytic manifolds, etc.

- Applications
Object recognition, face recognition, gesture recognition,
character recognition, motion analysis, scene analysis,
robot vision, biometrics, anomaly detection, data visualization,
other novel applications.


Organizers:
David Suter (The University of Adelaide, Australia)
Kazuhiro Fukui (University of Tsukuba, Japan)
Toru Tamaki (Hiroshima University, Japan)

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