Computer vision, machine learning and 3D space
| Title | Όραση υπολογιστών, μηχανική μάθηση και τρισδιάστατος χώρος / Computer vision, machine learning and 3D space |
| Code | 07YB044 |
| Faculty | Engineering |
| School | Rural and Surveying Engineering |
| Cycle / Level | 1st / Undergraduate, 2nd / Postgraduate |
| Teaching Period | Winter/Spring |
| Coordinator | Apostolos Axenopoulos |
| Common | No |
| Status | Active |
| Course ID | 600025763 |
Programme of Study: PPS Tmīmatos Agronómōn kai Topográfōn Mīchanikṓn (2025-sīmera)
Registered students: 67
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| Core Courses | Core Courses | 7 | 4 | 5 |
| Academic Year | 2025 – 2026 |
| Class Period | Winter |
| Faculty Instructors | |
| Weekly Hours | 6 |
| Class ID | 600267828
|
Course Type 2021
Specific Foundation
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600267828
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
- English (Examination)
Learning Outcomes
Through the lectures, students should gain a deep understanding of Computer Vision algorithms and methods, with emphasis on Photogrammetry and Remote Sensing. They should be able to compare algorithms and techniques for sparse/dense image matching, Structure For Motion, Simultaneous Localisation and Mapping. Through the lab exercises, students will become familiar with Python programming language and OpenCV library, for solving various computer vision problems.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
Course Content (Syllabus)
Structure for Motion (SFM) and comparison of methods. Dense Point Matching, 3D reconstruction. Simultaneous Localisation and Mapping (SLAM). Video analysis for 3D scene reconstruction. Pattern Recognition and Machine Learning. Artificial Intelligence. Image classification, image segmentation. Lab exercises in Python/OpenCV.
Keywords
Computer Vision, Machine Learning, Artificial Intelligence
Educational Material Types
- Notes
- Slide presentations
- Video lectures
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 52 | ||||
| Laboratory Work | 30 | ||||
| Reading Assigment | 18 | ||||
| Written assigments | 20 | ||||
| Exams | 5 | ||||
| Total | 125 |
Student Assessment
Description
Written exam (theory). Percentage 70%
Oral exam (lab exercise). Percentage 30%
Student Assessment methods
- Written Exam with Multiple Choice Questions (Summative)
- Written Exam with Short Answer Questions (Summative)
- Written Exam with Extended Answer Questions (Summative)
- Written Assignment (Formative)
- Oral Exams (Summative)
- Written Exam with Problem Solving (Summative)
- Labortatory Assignment (Formative)
Bibliography
Course Bibliography (Eudoxus)
Λυκοθανάσης, Σ., & Κουτσομητρόπουλος, Δ. (2023). Υπολογιστική
νοημοσύνη και βαθιά μάθηση [Προπτυχιακό εγχειρίδιο]. Κάλλιπος, Ανοικτές Ακαδημαϊκές Εκδόσεις.
http://dx.doi.org/10.57713/kallipos-168
Additional bibliography for study
Richard Szeliski, Computer Vision, Algorithms and Applications, 2nd ed. 2022, Springer https://szeliski.org/Book/
Last Update
21-11-2024
