Theory: 50 %

Practice: 50 %

Recommended semester: 6

Study mode: full-time

Prerequisites: Calculus 2 + 100 cr

Evaluation type: term mark

Course category: required optional

Language: english

Responsible instructor: Dr. Megyesi Zoltán

Responsible department: Department of Information Technologies

Instructor(s): Dr. Megyesi Zoltán , Koszna Ferenc

Course objectives:
This series of lectures will cover the most common steps in the image processing processes used in industry, from image capture to decision making. The practical material will introduce students to the cameras and image processing software used in the industry. Students will learn about the possibilities and tools of image processing and the basics of how these tools work. Students will be able to solve image processing problems in practice using the tools they learn and will have the theoretical background to develop their own tools.

Course content - lectures:

1. Concepts, tasks, tools, problems, related fields, applications of image processing; 2. The measurement process of image processing; 3. Imaging models. Camera architecture; 4. Colour models; Histogram; 5. Intensity transformations. Low-pass filters, smoothing (Box, Gaussian), median filter; 6. High-pass filters: Laplace filter, sharpening; 7. Laplace and Gaussian pyramids, filtering acceleration options; 8. Detection of intensity: intensity gradient. Edge-detection masks, zero-crossing operator. The "Canny" edge detector; 9. Detection of image corners: KLT corner detector, Harris corner detector; 10. Pattern matching: Fitting tasks, similarity measures, problems, acceleration options; 11. Segmentation: thresholding, histogram-based segmentation, automatic thresholding; Area-based segmentation: Region growing, Split and merge; Binary image processing: basics, midline, distance transformation, thinning, skeleton; 12. Binary morphology: erosion, dilation, opening, closing, thinning, midline; 2D shape recognition: basics, representation methods, area-based methods, contour-based methods. 13. Complex computer vision tasks: 3D reconstruction, geometric basics, general methods.

Course content - labs:

1. Introduction to image processing with tools. 2. Measurement with DVT camera: recording images 3. Measurement with DVT camera: edge counting, pixel histogram interpretation, shape recognition 4. Measurement with DVT camera: static and dynamic scaling, use of mathematical tools, spot search 5. Work with DVT camera: complex tasks solving. 6. Measurement with COGNEX cameras: introduction and record images. 7. Measurement with COGNEX cameras: EasyBuilder tools 8. Measurement with COGNEX cameras: Using spreadsheet wiew 9. Work with COGNEX camera: complex tasks solving. 10. Introduction to OpenCV: installing and sample project under Visual Studio 11. Image processing with OpenCV: image recording and scaling 12. Image processing with OpenCV: detecting regions and binarizations 13. Image processing with OpenCV: binary morphology tools and usage

Acquired competences:
Knowledge:

- His/her English language skills will be sufficient for the level of training, and to understand English-language literature, to process professional texts, to carry out professional tasks, as well as for continuous professional development. - Knowledge of the principles and methods of natural sciences (mathematics, physics, other natural sciences) relevant to the field of IT. - He/she knows the operations of hardware and software elements, the technology of their implementation, how to solve problems related to their operation and the possibilities of the interconnection of IT and other technical systems. - He/she posesses a basic knowledge and engineering approach to signal processing, modelling, simulation and control of systems and networks. - He knows the vocabulary and special terms of the engineering profession in the Hungarian and English languages at least on the basic level.

Skills:

- He/she uses the principles and methods of natural sciences (mathematics, physics, other natural sciences) relevant to the field of information technology in his/her engineering work for the design of information systems. He/she can apply his/her knowledge acquired during his/her study to acquire deeper knowledge in the field of information engineering and to process special literature and solve problems related to information technology. - He/she is able to fulfill analytical, specification, planning, development and operation tasks, in addition, he/she applies the development methodology, debugging, testing and quality assurance methods in his/her field. - He/she cooperates with other computer science engineers, electrical engineers during team work, and with other experts during the analysis and solution of a problems. - He/she can communicate in Hungarian and in English about professional issues, he/she uses the terms of information technology in a creative way. - He/she constantly improves his/her knowledge and keeps up with the development of the computer engineering profession.

Attitude:

- He/she genuinely represents the professional principles of engineering and information technology fields. - He/she aims to see through the entire engineering system not only his/her own field. - He/she is open to acquire new methods, programming languages and develop skills to use them. - He/she makes an effort to work efficiently and to high standards.

Autonomy and responsibilities:

- He/she feels responsible for IT systems analysis, development and operation, both individually and as part of a team. - He/she reveals the weaknesses of the technologies applied, risks of processes and initiates measures which reduce them.

Additional professional competences:

Requirements, evaluation, grading:
Mid-term study requirements:
2 assignment task is required for success, one for DVT, one for COGNEX cameras. The grade is based on 100 point test. 50 point is minimum for eligible grade. Grading is based on the regulations.
Exam requirements:

Study aids, laboratory background:

Lecture and practice materials will be uploaded on Teams channel or Neptun.

Compulsory readings:

Arcangelo Distante, Cosimo Distante - Handbook of Image Processing and Computer Vision, Volume 1: From Energy to Image, ISBN: 978-3-030-38150-9, Springer, 2021 Arcangelo Distante, Cosimo Distante - Handbook of Image Processing and Computer Vision, Volume 2: From Image to Pattern, ISBN: 978-3-030-42376-6, Springer, 2021 Arcangelo Distante, Cosimo Distante - Handbook of Image Processing and Computer Vision, Volume 3: From Pattern to Object, ISBN: 978-3-030-42380-3,Springer, 2021

Recommended readings:

M. Sonka, V. Hlavac, R. Boyle: Image Processing, Analysis, and Machine Vision; Cengage Learning, 4th edition (28 Oct. 2013); ISBN-13: 978-1133593607 L. G. Shapiro, G. C. Stochman: Computer Vision; Prentice Hall, 1st edition (23 Jan. 2001); ISBN-13: 978-0130307965 R. C. Gonzalez, R. E. Woods: Digital Image Processing; Prentice Hall (3rd edition) (August 31, 2007); ISBN-13: 978-0131687288