The course covers the field of Machine Vision, more specifically Image and Video Analysis based on Machine Learning. The focus is on the Deep Neural Networks for Image Classification, Object Detection, Image Segmentation, and combined techniques for Image and Video Analysis. The second part discusses the basics of 2D and 3D Scene Reconstruction from single and multiple images. The material includes examples of systems designed for practical usage in industry. It will allow students to gain basic knowledge of the theoretical and practical aspects of Image Analysis and Machine Vision.
Course Overview Table
| Chapter | Details |
| Partner | Ss. Cyril and Methodius University in Skopje, Faculty of Electrical Engineering and Information Technologies |
| Title | Machine Vision |
| Service | Course |
| Target Group | Industry: Small Businesses, Research and Development Departments in Medium-sized Enterprises, Startups. |
| Format | Workshop including In-Person Training. |
| Focused on Key Technologies | Convolutional Neural Networks, Digital Signal Processing, Optimization algorithms. |
| Status | Ready to offer |
| Stakeholders from SME/PA Side | Companies whose activities include video surveillance systems, quality control, access control, robotics. |
| Requirements for Participation | Necessary basic knowledge of programming languages and working environment, preferred basic knowledge of Python. |
| Estimated Duration | 4 days, 4 hours per day. |
Description of the Course
The course offers an introduction to the field of Machine Vision, with a special emphasis on Image and Video Analysis using Machine Learning. The main goal is to introduce participants to Deep Neural Networks and their application to solving various problems in the field of Image and Video Analysis, including combined analysis methods.
The course is organized into two main parts:
- Deep Neural Networks for Image and Video Analysis
- 2D and 3D Scene Reconstruction from Single and Multiple Images
The first part focuses on the basics of Deep Learning and its application to Image Classification, Object Detection, and Image Segmentation. Participants will be introduced to different Neural Network architectures, explore their advantages and disadvantages for different tasks, and learn how to design, tune, and train these networks through practical examples. Emphasis will be placed on industrial applications of these technologies, such as Automated Traffic Analysis, Pick-and-Place systems, and OCR.
The second part discusses the basics of 2D and 3D Scene Reconstruction from single and multiple images, including examples of camera calibration for real-time vehicles speed estimation.
The course will introduce participants to the basic concepts and techniques for Image Analysis and Understanding and will provide them with basic knowledge about the theoretical and practical aspects of Image Analysis and Machine Vision. The participants will gain practical experience in working with widely used software tools and libraries for Image Analysis and will be able to adjust and combine the studied techniques in targeted Image Analysis Algorithms.
Upon completion of the course, participants will be introduced to the basic concepts and techniques of Image and Video Analysis and will gain knowledge about the theoretical and practical aspects of Machine Vision and Image Processing. They will develop skills in using a working environment for developing Machine Vision Algorithms, as well as practical experience in working with widely used software tools and libraries for Image Analysis. The course will prepare participants to customize and combine different Image Processing techniques, and to develop custom algorithms for effective Image Analysis.
Additional Course Information
| Category | Details |
| Developed skills | Participants will acquire knowledge and skills, including: |
| · Knowledge of basic concepts and techniques for Image and Video Analysis based on Machine Learning
· Hands-on experience with widely used image analysis software tools and libraries · Application of deep neural networks for image and video analysis • Image Classification, Object Detection, and Image Segmentation · Design, training and fine tuning of Neural Networks through practical examples · Fundamentals of 2D and 3D Scene Reconstruction from single and multiple images · Practical experience in using a working environment for developing Machine Vision Algorithms |
|
| Learning Methods Used | · Lectures
· Practical work in groups of two to three students · Presentation of industry projects |
| References/Resources | In preparation |
| Overview Slides | In preparation |