LTAT.02.036 Digital Image Processing -DIP- [Digitaalne Pilditöötlus]
Image Processing: A Gateway to Intelligent Vision
Humans are fundamentally visual beings, relying heavily on their eyesight to gather most of the information about their surroundings. This reliance is evident in how we establish and pursue technical goals in everyday life. For example, scientific instruments often generate images to present their findings, which then require verification or further processing. Using many advanced mechanical and medical devices demands expertise from multiple fields, including the intriguing discipline of image processing. Originating from engineering -particularly signal processing- image processing has significantly contributed to the advancement of computer vision and pattern recognition, both of which have seen remarkable progress over the last decade. A breakthrough occurred when machine learning techniques were integrated into image processing and analysis, elevating the field to a leading position across various applications. This course focuses on explaining and demonstrating methods to ensure clear understanding, rather than delving deeply into complex mathematical theories. It serves as an introductory-level study of image processing science, utilizing programming to showcase fundamental concepts in modern image processing and pattern recognition. This course may serve as a foundational steppingstone for more advanced courses, including machine learning, deep learning, and intelligent data analytics.
Objective
The aim of this course is to provide students with an introductory and practical experience in the field of digital image processing and analysis.
Knowledge and understanding
On completion of the course, students should have a foundational understanding of digital image processing concepts, such as image representation, enhancement, segmentation, and analysis techniques.
Skills and abilities
On completion of the course the student should:
- be able to structure and conceptualize an image processing solution from an image-based problem description, identifying relevant processing techniques and steps.
- be able to independently develop an algorithm to address an image-based problem of limited scope.
- be able to develop a solution to a programmable image-based problem of limited scope using the concepts, components and programming environment used in the course.
Content
The topics which we anticipate to cover together are:
- Introduction to Imaging Technology
- Image Formation, Format and types
- Intensity Transformations and Spatial Filtering
- Image Bit-planes Manipulation
- Colorimetry: Colour Perception
- Morphological Image Processing
- Image Segmentation
- Computer vision (CV): Feature Extraction
- CV: Shape Analysis
- CV: Image Pattern Matching and Classification
- Modern Applications (e.g., Industry, Medical)