i40

Semester B

Mandatory

Remote

Computer Vision Industry

This course introduces students to the basic principles of Computer Vision, focusing on understanding the three-dimensional world through two-dimensional images and on the process of image formation and capture. It provides theoretical and practical knowledge of image processing, feature extraction, motion detection and tracking, as well as the development of Computer Vision algorithms. In addition, students become familiar with the application and evaluation of such algorithms in real-world problems and industrial processes.

ECTS Credits
0
Weeks
0
Total Hours
0
Bibliography Sources
0

Learning Outcomes

1)

Description of the problem of understanding the three-dimensional world through two-dimensional images

2)

Familiarization with the theoretical and practical aspects of computations based on image data

3)

Description of the formation and recording of the two-dimensional image

4)

Implementation of methods for extracting features from images

5)

Programming Implementation of Image Superimposition, Recognition, and Motion Tracking Algorithms

6)

Application of basic computer vision algorithms to simple problems and the composition of simple computer vision algorithms

7)

Integration of Computer Vision Algorithms into Industrial Processes

8)

Evaluation of the effectiveness of algorithms on different datasets

General Skills

Data Search & Synthesis

Independent project

Decision-making

Syllabus

Week

Topic

1

Digital Image, Filtering, and Edge Detection

2

Feature Detection and Matching

3

Feature Descriptors and Feature Matching, Feature Descriptors and Feature Matching

4

Cameras and Multiple Views

5

Introduction to Machine Learning

6

Deep Neural Networks and Convolutional Neural Networks

7

Deep Neural Networks and Convolutional Neural Networks

8

Depth Estimation

9

Object Tracking

10

Object Analysis and Detection in Industrial Applications

11

Applications of Computer Vision in Robotics – I

12

Applications of Computer Vision in Robotics – II

13

Assignment of Tasks

Evaluation & Workload

Activity
Hours

Lectures

39

Project Implementation

56

Independent Study

30

Course Total

150

Individual Project

Final Individual Project Presentation

Bibliography

DIGITAL IMAGE PROCESSING AND ANALYSIS
NIKOLAOS PAPAMARKOS

Digital Image Processing. Tziola Publications, 2018
Gonzalez, R. C., Woods, R. E.

Image Analysis. Varvarigos Publications, 2014
G. Tsichrintzis

Computer Vision: Algorithms and Applications
Szeliski, R.

Multiple View Geometry in Computer Vision (Second Edition)
Hartley, R., & Zisserman, A. (2004).

IEEE
Transactions on Pattern Analysis and Machine Intelligence

Journal
International Journal of Computer Vision

Journal
Journal of Machine Learning Research

Journal
Computer Vision and Image Understanding

Journal
Pattern Recognition Letters.

Course Information

Semester

ECTS
5

Minutes per Week

180

Type

Specialized Knowledge

Requirements

Course Format

Synchronous

30%

Asynchronous

70%

Remote

e-class

Erasmus ✓

Technologies & Tools

Computer vision

AI

Object Recognition

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