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AISB miscellaneous Bulletin Item

CFP: EURASIP Journal on Advances in Signal Processing Special Issue on Machine Learning in Image Processing

EURASIP Journal on Advances in Signal Processing
Special Issue on Machine Learning in Image Processing


Call for Papers

Images have always played an important role in human life since vision probably
is human beings' most important sense. As a consequence, the field of image
processing has numerous applications (medical, military, etc.). Nowadays and
more than ever, images are everywhere and it is very easy for everyone to
generate a huge amount of images thanks to the advances in digital
technologies. With such a profusion of images, traditional image processing
techniques have to cope with more complex problems and have to face with their
adaptability according to human vision. Vision being complex, machine learning
has emerged as a key component of intelligent computer vision programs when
adaptation is needed (e.g., face recognition) . Among the existing methods, one
can quote neural networks, hidden Markov models, kernel based methods, and so
forth. However, this mainly concerns the computer vision field, the learning of
which emulates high-level vision processes (e.g., visual information
categorization or interpretation). But one can also incorporate learning in
image processing to emulate low-level vision processes. We can quote edge
detection, noise filtering, adaptive compression, and so on, as such potential
issues. With the advent of image datasets and benchmarks, machine learning and
image processing have recently received a lot of attention. An innovative
integration of machine learning in image processing is very likely to have a
great benefit to the field, which will contribute to a better understanding of
complex images. The number of image processing algorithms that incorporate some
learning components is expected to increase, as adaptation is needed. However,
an increase in adaptation is often linked to an increase in complexity and one
has to efficiently control any machine learning technique to properly adapt it
to image processing problems. Indeed, processing huge amounts of images means
being able to process huge quantities of data often of high dimensions, which
is problematic for most machine learning techniques. Therefore, an interaction
with the image data and with image priors is necessary to drive model selection

The primary purpose of this special issue is to increase the awareness of image
processing researchers to the impact of machine learning algorithms in
low-level tasks. Papers submitted to this special issue have to carefully
address the problem of model selection (features selection, parameter or
hyperparameters estimation) for the machine learning technique under

This special issue aims at providing original and high-quality submissions
related, but not limited, to one or more of the following topics:

    * Machine learning in image filtering
    * Machine learning in image restoration
    * Machine learning in edge detection
    * Machine learning in image feature extraction
    * Machine learning in image segmentation
    * Machine learning in image compression
    * Machine learning driven by imaging applications.

Moreover, since image databases created for benchmarking or for training are
crucial for progress in both machine learning and image processing fields, the
evaluation of the submitted papers will take that aspect into account
(accessibility, quality, reproducibility) and the performance evaluation has to
be carefully adressed.

Authors should follow the EURASIP Journal on Advances in Signal Processing
manuscript format described at the journal site Prospective authors should submit an
electronic copy of their complete manuscript through the journal Manuscript
Tracking System at according to the following

    Manuscript Due	        September 1, 2007
    First Round of Reviews	December 1, 2007
    Publication Date	        March 1, 2008

Guest Editors:

Olivier Lezoray, Vision and Image Analysis (VAI) Team,Cherbourg Applied Sciences
University Laboratory (LUSAC), 120 Rue de l'Exode, 50000 Saint-L, France

Christophe Charrier, Vision and Image Analysis (VAI) Team, Cherbourg Applied
Sciences University Laboratory (LUSAC), 120 Rue de l'Exode, 50000 Saint-L,

Hubert Cardot, Pattern Recognition and Image Analysis Team, Computer Science
Laboratory (LI), Universit Franois Rabelais de Tours, 64 avenue Jean
Portalis, 37200 Tours, France

Sbastien Lefvre, Models Images Vision (MIV) Team, Image Sciences, Computer
Sciences and Remote Sensing Laboratory (LSIIT), CNRS and Louis Pasteur
University (Strasbourg), Ple API, Bd. Brant, BP 10413, 67412 Illkirch, France