Tantárgyi adatlap
Download PDFI. Subject Specification
1. Basic Data
1.1 Title
Képfeldolgozás
1.2 Code
BMEEOFTDT71
1.3 Type
Module with associated contact hours
1.4 Contact hours
| Type | Hours/week / (days) |
| Lecture | 2 |
1.5 Evaluation
Exam
1.6 Credits
3
1.7 Coordinator
| name | Dr. Barsi Árpád |
| academic rank | Professor |
| barsi.arpad@emk.bme.hu |
1.8 Department
Department of Photogrammetry and Geoinformatics
1.9 Website
1.10 Language of instruction
hungarian and english
1.11 Curriculum requirements
Ph.D.
1.12 Prerequisites
1.13 Effective date
1 September 2022
2. Objectives and learning outcomes
2.1 Objectives
By completing the image processing course, students will gain a comprehensive understanding of the creation, storage and use of digital images. They will become familiar with basic image manipulation operators and techniques, computer vision solutions and their links to other disciplines. In addition to theoretical methods, the course will also cover practical applications and development environments. Throughout the semester, individual problems are solved through independent problems, allowing PhD students to integrate image analysis technologies into their own research.
2.2 Learning outcomes
Upon successful completion of this subject, the student:
A. Knowledge
- Has an overview of the latest development in the field of image processing and computer vision.
- Knows the advantages and shortcomings of the particular image acquisition and storage methods.
- Has an overview of the cutting-edge image processing techniques and solutions.
- Knows the main engineering application fields images can support.
B. Skills
- Evaluates the performance of the particular image processing technology.
- Selects appropriate data acquisition technology considering the application requirements.
- Selects the effective data processing method for particular tasks.
- Is able to assess the potential of data processing techniques.
C. Attitudes
- Cooperates with the teacher and fellow students in expanding the knowledge,
- Expands his knowledge with the continuous acquisition of knowledge,
- Open to the use of information technology tools,
D. Autonomy and Responsibility
- Is able to perform own literature survey.
- Makes responsible decisions based on consultation.
2.3 Methods
Some introductory lectures on state-of-the-art of image processing, own research in a selected area, continuous consultation, and home assignment.
2.4 Course outline
| Hét | Előadások és gyakorlatok témaköre |
| 1. | Basic terms of images and image processing |
| 2. | Image capture and storage |
| 3. | Image features and statistics |
| 4. | Histogram and LUT manipulations |
| 5. | Image filtering |
| 6. | Image segmentation and classification |
| 7. | Basics of computer vision |
| 8. | Consultation |
| 9. | Consultation |
| 10. | Consultation |
| 11. | Consultation |
| 12. | Consultation |
| 13. | Consultation |
| 14. | Students' presentation |
The above programme is tentative and subject to changes due to calendar variations and other reasons specific to the actual semester. Consult the effective detailed course schedule of the course on the subject website.
2.5 Study materials
Gonzalez - Woods: Digital Image Processing
2.6 Other information
2.7 Consultation
Weekly opportunities discussed by supervisor.
This Subject Datasheet is valid for:
Nem induló tárgyak
II. Subject requirements
Assessment and evaluation of the learning outcomes
3.1 General rules
Learning outcomes formulated in point 2.2 are assessed on homework assignment and exam.
3.2 Assessment methods
| Teljesítményértékelés neve (típus) | Jele | Értékelt tanulási eredmények |
| Home assignment | HW | A.1-A.4; B.1-B.4; C.1-C.3; D.1-D.2 |
| Exam | E | A.1-A.4; B.1-B.4 |
The dates of deadlines of assignments/homework can be found in the detailed course schedule on the subject’s website.
3.3 Evaluation system
| Jele | Részarány |
| HW | 50% |
| E | 50% |
| Összesen | 100% |
3.4 Requirements and validity of signature
Successfully submitted and presented home assignment.
3.5 Grading system
The final grade is calculated by the average of the HW and E.
3.6 Retake and repeat
Late submission of the home assigment is allowed on the make-up week.
3.7 Estimated workload
| Tevékenység | Óra/félév |
| classes | 28 |
| preparing HW | 40 |
| preparing for the E | 22 |
| Összesen | 90 |
3.8 Effective date
1 September 2022
This Subject Datasheet is valid for:
Nem induló tárgyak