EURASIP Journal on Advances in Signal Processing, Hindawi Publishing Corporation
Call for Papers : Machine Learning in Image Processing

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 strategies.

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 consideration.

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 http://www.hindawi.com/journals/asp/. Prospective
authors should submit an electronic copy of their complete manuscript
through the journal Manuscript Tracking System at http://www.hindawi.com/mts/
according to the following timetable:

 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-Lô, France

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

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

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