[visionlist] Last Call for Papers Special Sessions ICPRAM 2012

[Pedro Latorre Carmona]

Dear "Visionlist" members,

Please, find below the last Call for papers for Special Sessions for  
the "2012 International Conference on Pattern Recognition Applications  
and Methods" (ICPRAM 2012) http://www.icpram.org

I would also like to let you know that:

a) Papers accepted in a Special Session have the same "rights" and  
follow the same rules as those of "regular" type, i. e., they will  
appear in the conference proceedings, will be elegible for the  
conference best paper prize and could be selected to be part of the  
three Special Issues that ICPRAM 2012 has:

a.1) "Springer Proceedings in Mathematics" (PROM)
http://www.springer.com/series/8806

a.2) "Neurocomputing"
http://www.elsevier.com/wps/find/journaldescription.cws_home/505628/description#description

a.3) Springer-Verlag "Advances in Intelligent and Soft Computing"
http://www.springer.com/series/4240

b) As said in a), we have three Special issues where the best papers  
in specific areas will be selected so that revised and extended  
versions of these papers will be published there.

More information can be found at http://www.icpram.org or if you want,  
you can email me with any doubt that you may have.

Thanks very much for your patience!. Sincerely,

Pedro Latorre Carmona.
PS. If you know of any colleage that may be interested to send a paper  
to any of the Special Sessions, could you please send him/her this  
Call for Papers below?.

*********************************************************************
       2012 International Conference on Pattern Recognition
                 Applications and Methods (ICPRAM2012)

                           CALL FOR PAPERS
                           SPECIAL SESSIONS

                          February 6-8, 2012
                     Vilamoura, Algarve, Portugal
                        http://www.icpram.org
*********************************************************************

Let me kindly inform you that there is an open call for papers, until  
October 24, for the following Special Sessions:

- Pattern Recognition Applications in Remotely Sensed Hyperspectral  
Image Analysis
Chair:
- Antonio Plaza, University of Extremadura, Spain
http://icpram.org/PRARSHIA.asp


- Shape Analysis and Deformable Modeling
Chair:
- Xianghua Xie, Swansea University, U.K.
http://icpram.org/SADM.asp


- Machine Learning for Sequences
Chair:
- Thierry Artieres, Universite Pierre et Marie Curie (UPMC), France
http://icpram.org/MLS.asp


- High-Dimensional Inference from Limited Data: Sparsity, Parsimony  
and Adaptivity
Co-chairs:
- Jarvis Haupt, University of Minnesota, U.S.A.
- Rui M. Castro, Eindhoven University of Technology, The Netherlands
http://icpram.org/HDILD.asp


- Algebraic Geometry in Machine Learning
Chair:
- Jason Morton, Pennsylvania State University, U.S.A.
http://icpram.org/AGML.asp


- Interactive and Adaptive Techniques for Machine Learning,  
Recognition and Perception
Co-chairs:
- Luisa Mico, University of Alicante, Spain
- Francesc J. Ferri, University of Valencia, Spain
http://icpram.org/IATMLRP.asp

Papers accepted in a Special Session have the same "rights" and follow  
the same rules as those of "regular" type, i. e., they will appear in  
the conference proceedings, will be elegible for the conference best  
paper prize and could be chosen to be part of the Special Issues that  
ICPRAM 2012 has.

Those papers that are presented at the "2012 International Conference  
on Pattern Recognition Applications and Methods (ICPRAM 2012)"  
http://www.icpram.org including those presented in the Special  
Sessions, can be selected (based on their quality) so that extended  
versions of them can be published in one of the three following  
Special Issues:

i) "Springer Proceedings in Mathematics" (PROM)
http://www.springer.com/series/8806

ii) "Neurocomputing"
http://www.elsevier.com/wps/find/journaldescription.cws_home/505628/description#description

iii) Springer-Verlag "Advances in Intelligent and Soft Computing"
http://www.springer.com/series/4240


IMPORTANT DATES:
Paper Submission: October 24, 2011
Authors Notification: November 11, 2011
Final Paper Submission and Registration: November 25, 2011


1) Pattern Recognition Applications in Remotely Sensed Hyperspectral  
Image Analysis
Chair:
- Antonio Plaza, University of Extremadura, Spain

Scope
Hyperspectral imaging is concerned with the measurement, analysis and  
interpretation of spectra acquired from a given scene by an airborne  
or satellite imaging spectrometer providing information in narrow  
wavelengths.
The special characteristics of remotely sensed hyperspectral images  
pose different processing problems which must be necessarily tackled  
under specific mathematical formalisms, such as classification and  
segmentation, or spectral unmixing. For instance, several machine  
learning techniques are now actively being applied to extract relevant  
information (in supervised, semi-supervised or unsupervised fashion)  
from remotely sensed hyperspectral data. This special session aims at  
providing an overview of recent advances in the use of pattern  
recognition and machine learning techniques for hyperspectral data  
interpretation, with particular attention to specific aspects of  
hyperspectral image analysis such as the presence of mixed pixels or  
the high computational requirements introduced by the processing of  
data sets provided by the latest generation of imaging instruments.


2) Shape Analysis and Deformable Modeling
Chair:
- Xianghua Xie, Swansea University, U.K.

Scope and Topics of interest:
Deformable modeling is a powerful tool in extracting object shape,  
structure, and motion patterns. It is particularly suitable for  
non-rigid objects and has been widely used to measure and model, for  
instance, biological shape and shape evolution in medical data where  
shape extraction and analysis have shown enormous promise in  
understanding biological function and disease progression. Its  
application has a wide reach in all areas of computer vision.

This special session is devoted to the discussion of recent advances  
in shape analysis and deformable modeling, in particular, for non  
rigid objects. Contributions presenting recent work on shape  
representation, extraction, learning, classification and dynamic  
modeling are particularly welcome.

The technical topics include, but are not limited to:
* Shape Representation and Learning
* Shape Matching, Classification, and Registration
* Active Shape Model and Active Appearance Model
* Active Contour and Surface Model
* Partial Differential Equations
* Level Set Methods
* Variational Methods
* Shape Based Motion Analysis
* Applications


3) Machine Learning for Sequences
Chair:
- Thierry Artieres, Universite Pierre et Marie Curie (UPMC), France

Scope
Sequence classification and sequence labeling is at the heart of many  
pattern recognition and data mining tasks, in fields such as speech  
and handwriting recognition, bioinformatics, etc. Beyond well known  
Hidden Markov Models (HMMs), which have been widely used for modeling  
sequences of patterns, a number of alternative methods and models have  
been proposed in the recent years. These approaches include for  
instance discriminative training (e.g. large margin) of Hidden Markov  
Models, on-line learning of such models, discriminative models based  
on Conditional Random Fields, etc.
This special session aims at sharing new ideas and works on models and  
approaches for improving over state of the art methods for signal and  
sequence classification and labeling tasks.


4) High-Dimensional Inference from Limited Data: Sparsity, Parsimony  
and Adaptivity
Co-chairs:
- Jarvis Haupt, University of Minnesota, U.S.A.
- Rui M. Castro, Eindhoven University of Technology, The Netherlands

Scope and Topics
In recent years the signal processing and statistics communities have  
witnessed a flurry of research activity aimed at the development of  
new non- traditional sampling, sensing and inference methods, fueled  
by the growing need to understand highly complex processes from  
limited amounts of data.
For example, recent breakthrough results in compressive sampling have  
shed new light on our understanding of sampling and reconstruction,  
leading to revolutionary new technologies in a variety of application  
domains, including RF communications and surveillance, imaging, and  
genomics.

The enabling feature of this new wave of research is the notion that,  
in many practical applications, high-dimensional objects of interest  
possess some form of parsimonious or low-dimensional representation.
Identifying these representations and designing strategies for  
effectively exploiting them comprises the central unifying theme of  
many active research directions, including compressive and adaptive  
sensing, matrix completion, and dictionary learning.

This session is devoted to the presentation and discussion of recent  
advances in these broadly defined areas. Namely, we invite submissions  
of high quality contributions in theory, methods, and/or applications  
in the general area of high-dimensional inference from limited data.  
Specific topics of interest for this session include (but are not  
limited to):

     * Compressed Sensing
     * Active Learning and Adaptive Sensing
     * Sequential Experimental Design
     * Dictionary Learning
     * Optimal Information Gathering
     * Matrix Completion Approaches


5) Algebraic Geometry in Machine Learning
Chair:
- Jason Morton, Pennsylvania State University, U.S.A.

Scope
The philosophy of algebraic statistics is that for many models arising  
in statistics and machine learning, the space of parameters or  
probability distributions modeled has the structure of an algebraic  
variety. This observation has led to new precise characterizations of  
popular models, new insights into representational power, and new  
approaches to studying learning performance (e.g. in the neighborhood  
of singularities, or proving the existence of a MLE).

For many classes of machine learning models, theoretical understanding  
has lagged behind experimental success. In many cases,  
representational power and performance characteristics are poorly  
understood, and even proponents are unsure why they work.
Understanding the algebraic, polyhedral, and tropical geometry of  
graphical models and other popular models has provided a new set of  
tools enabling researchers to settle several open questions about  
their capabilities, and progress on this front is expected to continue.

Topics for the special session may include the algebraic geometry and  
representation theory of machine learning models, the polyhedral and  
tropical geometry of the space of functions they can compute,  
geometric characterizations of architecture choice and asymptotic  
performance, and related topics.


6) Interactive and Adaptive Techniques for Machine Learning,  
Recognition and Perception
Co-chairs:
- Luisa Mico, University of Alicante, Spain
- Francesc J. Ferri, University of Valencia, Spain

Scope
Human interaction is a very active field that is receiving increasing  
attention in the pattern recognition and machine learning community.
In this new paradigm the systems do not perform only in an automatic  
way but also in an interactive fashion.
The main reason for this is that automatic systems are not free from  
errors and, being high quality results the principal objective, a kind  
of supervision is needed. On the other hand, as time goes by,  
intrinsic interactive applications are more important and frequent.The  
use of the interactive paradigm in Pattern Recognition opens the door  
to new challenges in order to make convenient use of a number of  
emerging methods for supporting learning and data analysis in dynamics  
contexts: active and adaptive learning, hypothesis generation, data  
managed techniques, combining classifier techniques, probabilistic  
learning, interactive transduction, etc. Moreover, another challenge  
is the application of these ideas to interesting real-word tasks, as  
human behavior analysis, text transcription, content-based image  
retrieval, handwriting recognition, surveillance, biometric systems  
and many others.
This special session welcomes articles on advances on all the  
aforementioned hot topics.