Image Processing and Analysis

Garant předmětu:

Václav Hlaváč

Lecturer:

Václav Hlaváč, Zoltán Szabó

Tutor:

Evgeniia Karnoub, Marek Piorecký, Jan Štrobl

Supervisor:

Department of Biomedical Informatics

Synopsis:

The course deals with the topics - digital image processing vs. computer vision, the role of interpretation, objects in the image, digital image, distance transformation, histogram of brightness, image acquisition from the geometric and radiometric point of view, Fourier transform, derivation of the sampling theorem, frequency filtering of image, PCA, brightness transformation, geometric transformations, interpolation, registration, processing in the spatial domain, convolution, correlation, noise filtering, edge detection, linear and nonlinear methods, mathematical morphology, image compression, color images, texture, segmentation of objects in images, description of objects in images, and their recognition.

Requirements:

Credit requirements: Completion of exercises is required, which corresponds to a maximum of 40 points.

Requirements for the evaluation of the exam: The written exam of the material of the lecture will ensure a maximum of 30 points. Oral examination max. 30 points. A maximum of 40 points is taken from the exercise. The rating is based on the ECTS scale.

Syllabus of lectures:

1. DZO vs. computer vision. The role of interpretation. Objects in the image. Digital image.

2. Brightness transformations, geometric transformations, image interpolation. Image acquisition from an optical and radiometric point of view.

3. Processing in the spatial area. Convolution, correlation. Noise filtering.

4. Fourier transform in 1D and 2D. Frequency filtering of images. Edge detection. Space scales. Detection of significant points.

5. Principal Component Analysis method of guidelines. Wavelet transform.

6. Wavelet transform, completion. Segmentation of objects in images, thresholding, K-means, the center of gravity shift.

7. Optimization problems and methods, overview. Graphs, graph algorithms (overview).

8. Statistical recognition. The role of learning. Use for images.

9. Description of objects. Object segmentation formulated as optimization problems on graphs.

10. Binary and grayscale mathematical morphology.

11. Image compression. Color paintings.

12. Fixed and flexible registration of images and objects in images.

Syllabus of tutorials:

1. Grayscale mathematical morphology, dilatation, erosion.

2. Top Hat transformation, distance transformation.

3. Fourier transform, frequency filtering of images.

4. Principal Component Analysis method.

5. Brightness transformation.

6. Geometric transformations, interpolation.

7. Registration.

8. Processing in the spatial area. Convolution, correlation.

9. Noise filtering. Edge detection. Linear and nonlinear methods.

10. Image compression. Color images.

11. Huffman coding, discrete cosine transform.

12. Segmentation of objects in images.

Study Objective:

Study materials:

[1] ŠONKA, Milan, Václav HLAVÁČ, Roger BOYLE. Image processing, analysis, and machine vision. Fourth edition. Stamford: Cengage Learning, [2015], ©2015. 870 stran, A-P stran obrazových příloh. ISBN 978-1-133-59360-7.

[2] Szeliski R.Computer Vision: Algorithms and Application, Springer, Berlin, 2010. 812 p.

[3] BIRCHFELD, Stan. Image processing and analysis. Boston: Cengage Learning, [2016], © 2018. xviii, 718 stran. ISBN 978-1-285-17952-0

Note:

Further information:

https://people.ciirc.cvut.cz/~hlavac/teaching/FBMI-ZAO/2023-2024Zima/

Time-table for winter semester 2023/2024:

	06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
Tue
Wed	roomKL:B-435 Karnoub E. 10:00–11:50 (lecture parallel1 parallel nr.1) Kladno FBMI Počítačová učebna
Thu	roomKL:B-420 Hlaváč V. Szabó Z. 10:00–11:50 (lecture parallel1) Kladno FBMI Učebna
Fri

Time-table for summer semester 2023/2024:

Time-table is not available yet

The course is a part of the following study plans:

• Biomedical Engineering (compulsory course)