Topics

Neural networks and, more generally, machine learning techniques can be widely applied to image processing and computer vision for preprocessing, feature extraction, segmentation, object tracking and recognition. The adopted machine learning tools include associative memories, kernel machines, neural networks, evolutionary computations, and statistical methods. Visual data are difficult to be dealt with, due to noise, such as light and colour changes, occlusion, and cluttered environments. The capability of machine learning techniques to incorporate imprecision and incomplete information, and to model very complex systems makes them a useful tool in image processing.
The aim of this session is to provide an international forum for presentation of recent results and advances regarding soft computing methods in image processing. The scope of this session is, but not limited to:

• Noise reduction in images
• Image compression
• Edge detection and segmentation
• Feature extraction
• Similarity learning in images
• Image analysis and classification
• Object localization and detection
  

Paper submission

Authors are encouraged to submit high quality, original work that has neither appeared in, nor is under consideration by, other conferences/journals. All submissions will be refereed by experts in the field based on originality, significance, and clarity. The conference preceedings will be published by Springer-Verlag in Lecture Notes in AI as part of the LNCS/LNAI series. When formatting papers - that must be no more than eight (8) pages long, including figures and bibliography - please refer to the Springer-Verlag site and strictly follow the instructions for LNCS authors (http://www.springer.de/comp/lncs/authors.html).


Important Dates

Chairs

Monica Bianchini, Marco Maggini, Franco Scarselli
Dipartimento di Ingegneria dell'Informazione
University of Siena, Italy
Email:{monica,marco,franco}diiunisiit