Semester B
Mandatory
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.
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
Lectures
Project Implementation
Independent Study
Course Total
Individual Project
Final Individual Project Presentation
Bibliography
DIGITAL IMAGE PROCESSING AND ANALYSIS
NIKOLAOS PAPAMARKOS
Gonzalez, R. C., Woods, R. E.
Image Analysis. Varvarigos Publications, 2014
G. Tsichrintzis
Computer Vision: Algorithms and Applications
Szeliski, R.
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
B΄
Minutes per Week
180
Type
Specialized Knowledge
Requirements
Course Format
Synchronous
Asynchronous
Remote
e-class
Technologies & Tools
Computer vision
AI
Object Recognition
Πλατφόρμα e-class
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