Dr.-Eng. Igor Gurevich
Dorodnicyn Computing Center, Russian Academy of Sciences
Moscow, the Russian Federation
Professor Dr. Heinrich Niemann
Friedrich-Alexander-University of Erlangen-Nürnberg
Professor Ovidio Salvetti
Institute of Information Science and Technologies, Italian national Research Council
The IMTA-2008 workshop was successfully conducted in conjunction with VISIGRAPP 2008. IMTA-II will continue this line of scientific events devoted to modern mathematical techniques of image mining and to corresponding application problems. The IMTA-2008 will be conducted in cooperation with the Technical Committee No. 16 of the International Association for Pattern Recognition "Algebraic and Discrete Mathematical Techniques in Pattern Recognition and Image Analysis" 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 aspexts. 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 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 results computational procedures 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.
The workshop will be organized 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.
This workshop is intended to cover, but it is not limited to, the following topics:
1. New Mathematical Techniques in Image Mining:
. Algebraic Approaches
. Discrete Mathematics Techniques
. Structural and Syntactic Techniques
. Multiple Classifiers
. Other Mathematical Techniques
2. Image Models and Image Features
3. Automation of Image Mining:
. Image Mining, Computer Vision and Knowledge-Based Systems
. Image Databases
. Image Knowledge Bases
. Image Mining Technologies
. Linguistic Tools:
o Image Science Ontologies;
o Image Science Thesauri.
4. Applied Problems