20 May, 2010 - Angers, France
In conjunction with the 3rd International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - VISIGRAPP 2010
Dorodnicyn Computing Center, Russian Academy of Sciences
Moscow, Russian Federation
Friedrich-Alexander-University of Erlangen-Nürnberg
Institute of Information Science and Technologies, Italian National Research Council
The IMTA-3 will continue the series of workshops devoted to modern mathematical techniques of image mining and to corresponding application problems. The IMTA-3-2010 will be conducted in cooperation with the Technical Committee No.16 "Algebraic and Discrete Mathematical Techniques in Pattern Recognition and Image Analysis" of the International Association for Pattern Recognition and with the National Committee for Pattern Recognition and Image Analysis of the Russian Academy of Sciences. The workshop will consist of a small number of invited talks, contributed talks, and informal discussions, and a wrap-up session.
Automation of image mining is one of the most important strategic goals in image analysis, recognition and understanding both in scientific and technological aspects. The main subgoals are developing and applying of mathematical theory for constructing image models accepted by efficient pattern recognition algorithms and for constructing standardized representation and selection of image analysis transforms. Automation of image-mining is possible by combined application mathematical techniques for image analysis, understanding and recognition.
Automation of image processing, analysis, estimating and understanding is one of the crucial points of theoretical computer science having decisive importance for applications, in particular, for diversification of solvable problem types and for increasing the efficiency of problem solving.
The role of an image as an analysis and estimation object is determined by its specific and inalienable informational properties. Image is a mixture and a combination of initial (raw, “real”) data and its representation means, of computational procedures results and of the physical nature and of the models of objects, events and processes to be represented via an image.
The specificity, complexity and difficulties of image analysis and estimation (IAE) problems stem from necessity to achieve some balance between such highly contradictory factors as goals and tasks of a problem solving, the nature of visual perception, ways and means of an image acquisition, formation, reproduction and rendering, and mathematical, computational and technological means allowable for the IAE.
The mathematical theory of image analysis is not finished and is passing through a developing stage. It is only recently came understanding of the fact that only intensive creating of comprehensive mathematical theory of image analysis and recognition (in addition to the mathematical theory of pattern recognition) could bring a real opportunity to solve efficiently application problems via extracting from images the information necessary for intellectual decision making. The transition to practical, reliable and efficient automation of image-mining is directly dependent on introducing and developing of mathematical means for IAE.
The purpose of the workshop is to discuss a methodology, mathematical and computational techniques for automation of image mining on the base of mathematical theory for IAE. Another important task of the workshop is to discuss linguistic tools for image mining – image knowledge bases and image science ontologies – and to estimate the prospects of the algebraic approach in representation of image analysis knowledge in this environment. The interpretation of mathematical and linguistic techniques will be illustrated by application problems, mainly from biology and medicine.
Topics of interest include, but are not limited to:
- New Mathematical Techniques in Image Mining
- Algebraic Approaches
- Discrete Mathematics Techniques
- Structural and Syntactic Techniques
- Multiple Classifiers
- Other Mathematical Techniques
- Image Models and Image Features
- Automation of Image Mining
- Image Mining, Computer Vision and Knowledge-Based Systems
- Image Databases
- Image Knowledge Bases
- Image Mining Technologies
- Medical Image Mining
- Linguistic Tools
- Image Science Ontologies
- Image Science Thesauri
- Applied Problems
Professionals, Researchers, PhD students and graduate students interested in Mathematical Theory of Image Analysis, designers of automated image analysis systems.
Prerequisites for participants
- Technical University course in mathematics including a course in general algebra;
- Technical University courses in pattern recognition and/or image analysis;
- Interest to theory and methodology of image analysis and to new mathematical techniques for pattern recognition and/or image analysis.
Workshop Program Committee
Igor Gurevich, Dorodnicyn Computing Center of the Russian Academy of Sciences, Russian Federation
Irina Koryabkina, Dorodnicyn Computing Center of the Russian Academy of Sciences, Ireland
Anatoly Nemirko, St. Petersburg Electrotechnical University, Russian Federation
Heinrich Niemann, University of Erlangen-Nuernberg, Germany
Ovidio Salvetti, CNR, Italy
Vladislav Sergeev, Image Processing Systems Institute of Russian Academy of Sciences, Russian Federation
Yulia Trusova, Dorodnicyn Computing Center of the Russian Academy of Sciences, Russian Federation
Vera Yashina, The Institution of the Russian Academy of Sciences “A.A.Dorodnicyn Computing Center of the RAS”, Russian Federation
Yuri Zhuravlev, Dorodnicyn Computing Center of the Russian Academy of Sciences, Russian Federation
All accepted papers will be published in the workshop proceedings book, under an ISBN reference, and on CD-ROM support.
Full revised texts of all papers presented at the workshop will be published in the special issue of the international journal “Pattern Recognition and Image Analysis. Advances in Mathematical Theory and Applications” (MAIK “Nauka/Interperiodica” Pleiades Publishing, Moscow, distributed worldwide by SPRINGER), 2011.