The presentation is devoted to the research of mathematical fundamentals for image analysis and recognition procedures. The final goal of this research is automated image mining: a) automated design, test and adaptation of techniques and algorithms for image recognition, estimation and understanding; b) automated selection of techniques and algorithms for image recognition, estimation and understanding; c) automated testing of the raw data quality and suitability for solving the image recognition problem. The main instrument is the Descriptive Approach to Image Analysis, which provides: 1) standardization of image analysis and recognition problems representation; 2) standardization of a descriptive language for image analysis and recognition procedures; 3) means to apply common mathematical apparatus for operations over image analysis and recognition algorithms, and over image models. It is shown also how and where to link theoretical results in the foundations of image analysis with the techniques used to solve application problems.
[1]
Alberto Sanfeliu,et al.
Progress in Pattern Recognition, Speech and Image Analysis
,
2003,
Lecture Notes in Computer Science.
[2]
Matti Pietikäinen,et al.
Proceedings of the 6th Scandinavian Conference on Image Analysis, Oulu, June 19-22, 1989
,
1989
.
[3]
Igor B. Gurevich,et al.
An Image Algebra Accepting Image Models and Image Transforms
,
2002,
VMV.
[4]
Igor B. Gurevich,et al.
The descriptive techniques for image analysis and recognition
,
2017,
VISAPP.
[5]
V. V. Yashina,et al.
Descriptive Image Algebras with One Ring 1
,
2003
.
[6]
Igor B. Gurevich,et al.
Conditions of Generating Descriptive Image Algebras by a Set of Image Processing Operations
,
2003,
CIARP.
[7]
Joseph N. Wilson,et al.
Handbook of computer vision algorithms in image algebra
,
1996
.