- SylabUZ
- Faculty of Engineering and Technical Sciences
- winter term 2023/2024
- Computer Science - Second-cycle Erasmus programme
- Image recognition
Image recognition - course description
General information
| Course name |
Image recognition |
| Course ID |
11.3--INFD-RozObr- Er |
| Faculty |
Faculty of Engineering and Technical Sciences |
| Field of study |
Computer Science |
| Education profile |
academic |
| Level of studies |
Second-cycle Erasmus programme |
| Beginning semester |
winter term 2023/2024 |
Course information
| Semester |
2 |
| ECTS credits to win |
6 |
| Course type |
obligatory |
| Teaching language |
english |
| Author of syllabus |
- dr hab. inż. Marek Kowal, prof. UZ
|
Classes forms
| The class form |
Hours per semester (full-time) |
Hours per week (full-time) |
Hours per semester (part-time) |
Hours per week (part-time) |
Form of assignment |
|
Project |
15 |
1 |
- |
- |
Credit with grade |
|
Laboratory |
30 |
2 |
- |
- |
Credit with grade |
|
Lecture |
15 |
1 |
- |
- |
Credit with grade |
Aim of the course
To familiarize students with the techniques of image processing, segmentation, recognition and classification.
Prerequisites
Artificial intelligence
Scope
- Image processing: image loading and saving, image types, color spaces and histogram, cropping and affine operations, point operators, image intensity transformation, basic image filtering, morphological operations
- Image segmentation: line and edge detection, thresholding methods, watershed method, active contours, deep neural networks
- Feature extraction: contour and region descriptors, corner and center detection, SIFT descriptors, Convolutional Neural Networks
- Object detection and classification: application of descriptors, classifiers and deep neural networks to detect and classify objects in images
Teaching methods
conventional lecture, exercises, project
Learning outcomes and methods of theirs verification
| Outcome description |
Outcome symbols |
Methods of verification |
The class form |
Assignment conditions
lecture - obtaining a positive grade from the written test
laboratory - obtaining positive grades from laboratory exercises reports
project - obtaining a positive assessment of the completed project
final grade = 30% lecture + 40% laboratory + 30% project
Recommended reading
- Digital Image Processing /Rafael C. Gonzalez, Richard E. Woods/ Pearson, 2018.
- Computer Vision: Algorithms and Applications /Richard Szeliski / Springer, 2022.
- Hands-On Image Processing with Python /Sandipan Dey/ Packt Publishing, 2018.
- Deep Learning with Python /François Chollet/ Manning, 2017.
- Deep Learning Ian /Goodfellow, Yoshua Bengio, Aaron Courville/ MIT Press, 2016.
Further reading
- Computer Vision Projects with OpenCV and Python 3 /Matthew Rever/ Packt Publishing, 2018.
Notes
Modified by dr hab. inż. Marek Kowal, prof. UZ (last modification: 25-04-2023 18:01)
