IMAGE PROCESSING
- Overview
- Assessment methods
- Learning objectives
- Contents
- Bibliography
- Delivery method
- Teaching methods
- Contacts/Info
Students will be expected to be familiar with teachings of the course Programming that constitutes a constraint to the learning path
The evaluation consists in a written examination lasting 2 hours and the development of a project. The written examination is aimed to assess the knowledge acquired during the lessons and the project aims to assess the operational skills.
The test of the written examination consists of 3 questions in general, some entirely of theory, some including numerical exercises. Projects typically involve image processing tasks similar to those presented and developed in laboratory. The activity can be performed in small groups limited to a maximum of three members.
The separate assessment elements contribute towards the final mark according to the following rule:
• Written text 70%
• Project 30%
This course introduces the fundamentals of digital image processing. It emphasizes both general principles of image processing and specific applications in several domains including Earth Observation and Medical Diagnosis.
Students will be able to:
1. Know the main objectives and areas of Digital Image Processing with the ability to identify the potentialities of the processing techniques and the relationships with other disciplines
2. Know the basic physical, geometrical and perceptual principles of image formation
3. Know concepts related to image acquisition and digitalization
4. Know most relevant punctual and local operators for image enhancement with ability to select and compose sequences of operators to remove source of degradation
5. Know image segmentation techniques based on edge detection and region-based approaches with ability to choose and apply the proper techniques in specific contexts
6. Know basic concepts related to color spaces, and ability to apply color image processing techniques
7. Know most relevant approaches to image compression
It is also expected that students develop communicative skills through open discussions during lessons and laboratory activities and autonomous assessment in the choice of the proper technique to solve problems of Image Processing in several domains.
Students will acquire knowledge of the relevant Image Processing terminology.
The acquisition of knowledge and expected skill is developed along the entire course that includes the topics listed below.
1) Introductory topics
-Introduction to Digital Image Processing: historical perspective, fields of application
- Structure and functions of a digital image processing system
-Human Vision and Image formation
- Sampling, quantization
(8h- Course Objective 1,2,3)
2) Image Enhancement
-Contrast Enhancement
-Histogram, cumulative histogram
-Equalisation, Histogram matching
-Arithmetic and logical operators
- Smoothing filter
-Sharpening filter
- “Order Statistics” operators
(16h- Course Objective 4)
3) Image Segmentation based on edge detection (Sobel, Laplacian, LoG filters) and region-based approaches (region growing, split and merge)
(8 h- Course Objective 5)
4) Color image processing
- Color spaces: RGB, HSI
- Color image processing techniques
(5h- Course Objective 6)
5)Image Compression techniques based on code, interpixel and psychovisual redundancy; lossy and non- lossy techniques ; Image Formats
(3h -Course Objective 7)
7) Presentation of open source program ImageJ and laboratory exercises
(16h- Course Objective 3,4,5,6)
Teaching Methods
Lessons (40 hours); Laboratory (16 hours)
Lessons deal with the overall set of topics listed above using conceptual, formal descriptions and with the support of Demo and on line resources.
Laboratory activities are based on the use of Image Processing tools and includes exercises, developed under the supervision of the teacher. With these activities students develop abilities and operational skills in the domain of Image Processing.
Textbook: C. Gonzalez, R.E. Woods, Digital Image Processing, Addison Wesley , 1992
- Material available on the e-learning website:
- Slides of the lessons
- Additional readings, including selected papers from the literature and links to on line demo and tutorials
- Examples of written texts with solutions and examples of projects
Lessons (40 hours); Laboratory (16 hours)
Lessons deal with the overall set of topics listed above using conceptual, formal descriptions and with the support of Demo and on line resources.
Laboratory activities are based on the use of Image Processing tools and includes exercises, developed under the supervision of the teacher. With these activities students develop abilities and operational skills in the domain of Image Processing.
Office hours is agreed by e-mail: elisabetta.binaghi@uninsubria.it; the teacher responds only to e-mail sent from student.uninsubria.it
Professors
Borrowers
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Degree course in: COMPUTER SCIENCE