Fundamentals of Image Processing Theory

Description: The discipline is devoted to the study of fundamental issues in the theory of image processing (formation, input, representation in a computer and visualization). Introduces undergraduates to image and video processing algorithms, both classical and based on modern neural network architectures. The issues of constructing mathematical models and statistical algorithms for image processing are considered. Optimal and quasi-optimal solutions to basic image processing problems (filtering, detection, alignment and recognition) are described.

Amount of credits: 6

Course Workload:

Component: Component by selection

Cycle: Profiling disciplines

Goal

• the formation of theoretical knowledge and practical skills in undergraduates in solving problems that arise in the development of image processing and analysis systems, the ability to work independently with image processing software.

Objective

• familiarization with the main directions of development of this field of knowledge,
• studying methods and algorithms for digital image processing,
• acquisition of skills in solving applied problems related to image processing.

Learning outcome: knowledge and understanding

• extract theoretical knowledge about the mathematical and algorithmic apparatus used in modern image processing and analysis systems;

Learning outcome: applying knowledge and understanding

• apply basic scientific and theoretical knowledge to solve theoretical and practical problems of image processing and analysis;

Learning outcome: formation of judgments

• choose methods and technologies for pattern recognition to build formal mathematical models and interpret modeling results when solving applied problems in various fields;

Learning outcome: communicative abilities

• demonstrate your own results of engineering and research activities;

Learning outcome: learning skills or learning abilities

• apply a system of knowledge about modern methods and approaches to solving typical problems of image processing and analysis when designing intelligent control systems;
• apply various software tools for image processing and analysis, building formal mathematical models.

Teaching methods

interactive lecture (use of the following active forms of learning: guided (controlled) discussion or conversation; moderation; demonstration of slides or educational films; motivational speech);

information and communication (classes in a computer class using professional application software packages);

search and research (independent research activities in the learning process)

Assessment of the student's knowledge

Teacher oversees various tasks related to ongoing assessment and determines students' current performance twice during each academic period. Ratings 1 and 2 are formulated based on the outcomes of this ongoing assessment. The student's learning achievements are assessed using a 100-point scale, and the final grades P1 and P2 are calculated as the average of their ongoing performance evaluations. The teacher evaluates the student's work throughout the academic period in alignment with the assignment submission schedule for the discipline. The assessment system may incorporate a mix of written and oral, group and individual formats.

The evaluating policy of learning outcomes by work type

Evaluation form

The student's final grade in the course is calculated on a 100 point grading scale, it includes:

• 40% of the examination result;
• 60% of current control result.

The final grade is calculated by the formula:

 

Where Midterm 1, Midterm 2are digital equivalents of the grades of Midterm 1 and 2;

E is a digital equivalent of the exam grade.

Final alphabetical grade and its equivalent in points:

The letter grading system for students' academic achievements, corresponding to the numerical equivalent on a four-point scale:

Topics of lectures

• Statement of the image recognition problem
• Point image processing operations
• Spatial operations on images
• Algebraic and geometric operations on images
• Morphological transformations of images
• Feature generation based on linear transformations
• Discrete Fourier transform
• Wavelet transform of images
• Generating shape features based on image edge analysis
• Generating shape features based on the construction and analysis of image skeletons
• Construction of a measure of image similarity

Key reading

• Gonsales R., Vuds R. Cifrovaya obrabotka izobrazhenij.: Per. s angl. – M.: Tekhnosfera, 2012. – 1104 s.
• Shapiro L., Stokman Dzh. Komp'yuternoe zrenie: Per. s angl. – M.: BINOM. Laboratoriya znanij, 2015. – 763 s.
• Tomakova, R. A. Metody i algoritmy cifrovoj obrabotki izobrazhenij: uchebnoe posobie / R. A. Tomakova, E. A. Petrik; Yugo-Zap. gos. un-t. - Kursk : Universitetskaya kniga, 2020. - 310 s.

Further reading

• Fisenko V.T. Komp'yuternaya obrabotka i raspoznavanie izobrazhenij.: ucheb. posobie / V.T. Fisenko, T.Yu. Fisenko. – Sankt-Peterburg: NIU ITMO, 2008.– 192 s.
• Krasheninnikov, V. Osnovy teorii obrabotki izobrazhenij: ucheb.posobie / V.R. Krasheninnikov; M-vo obrazovaniya Ros. Federacii, Ul'yan. gos. tekhn. un-t. - Ul'yanovsk: 2003. - 151 s.
• Guk A.P. Metody i tekhnologii raspoznavaniya ob"ektov po ih izobrazheniyu: uchebno-metodicheskoe posobie / A.P.Guk, E.P.Hlebnikova. – Novosibirsk: SGUGiT, 2019. – 138 s.
• Gonsales R. Cifrovaya obrabotka izobrazhenij v srede MATLAB / R. Gonsales, R. Vuds, S. Eddins. – M.:Tekhnosfera, 2006. – 615s.