This course introduces the fundamentals of image processing. The students are expected to develop an understanding on how the visual data (images and video) are processed, either to convert one representation to another, or to manipulate data for further analysis. The course forms a basis for the students who wish to specialize in interrelated disciplines like computer vision and computational photography.The topics to be covered will be on image formation, point operations, spatial filtering, frequency domain approaches, pyramids, wavelets, feature extraction, image smoothing, and image segmentation. The course also reserves two weeks for introductory-level deep learning to provide insights into the state-of-the-art in image processing. It will ensure both theoretical/mathematical and practical aspects.
· Digital Image Processing, R.C. Gonzalez, R.E. Woods, Pearson Prentice Hall 2008.
Other Sources
Concise Computer Vision: An Introduction into Theory and Algorithms, Springer, Series: Undergraduate Topics in Computer Science, by Reinhard Klette, 2014. ISBN 978-1-4471-6319-0.
Course Schedules
Week
Contents
Learning Methods
1. Week
Introduction, Image formation
Oral presentation
2. Week
Color and Point operations
Oral presentation, Laboratory
3. Week
Spatial filtering
Oral presentation
4. Week
Frequency Domain Techniques
Oral presentation, Laboratory
5. Week
Frequency Domain Techniques (cont'd.)
Oral presentation, Laboratory
6. Week
Image pyramids
Oral presentation, Laboratory
7. Week
Gradients, edges, contours
Oral presentation, Laboratory
8. Week
Midterm
Oral presentation, Laboratory
9. Week
Image smoothing
Oral presentation, Laboratory
10. Week
Image segmentation
Oral presentation, Laboratory
11. Week
Image segmentation
Oral presentation, Laboratory
12. Week
Deep learning basics: Artificial neural networks
Oral presentation, Laboratory
13. Week
Deep learning basics: Convolutional neural networks
Oral presentation, Laboratory
14. Week
Project Presentations
Oral presentation, Laboratory
15. Week
16. Week
17. Week
Assessments
Evaluation tools
Quantity
Weight(%)
Program Outcomes
PO-1
Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied information in these areas to model and solve engineering problems.
PO-2
Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modelling methods for this purpose.
PO-3
Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way so as to meet the desired result; ability to apply modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety issues, and social and political issues according to the nature of the design.)
PO-4
Ability to devise, select, and use modern techniques and tools needed for engineering practice; ability to employ information technologies effectively.
PO-5
Ability to design and conduct experiments, gather data, analyse and interpret results for investigating engineering problems.
PO-6
Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
PO-7
Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language.
PO-8
Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.
PO-9
Awareness of professional and ethical responsibility.
PO-10
Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development.
PO-11
Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of engineering solutions.
Learning Outcomes
LO-1
I know the concept of image pyramids.
LO-2
I know how to detect low-level features such as edges, blobs, and boundaries.
LO-3
I am familiar with the principles of image smoothing and the elementary denoising techniques.
LO-4
I am familiar with the principles of image segmentation and the elementary segmentation techniques.
LO-5
I am familiar with the foundation of deep learning in image processing.
LO-6
I can build image processing applications with Python.
LO-7
I can describe the foundation of image formation and representation.
LO-8
I can discuss how the color is perceived and the concept of color spaces.
LO-9
I am familiar with gray-scale transformations.
LO-10
I know how to calculate two-dimensional convolution, window-filtering, and smoothing operations on images
LO-11
I can compare spatial and frequency domain techniques, I know to represent images in terms of frequency, and I can discuss on Fourier series, Convolution Theorem, sampling, Gabor wavelets, and steerable filters.
