Master of Science in Smart Systems Engineering

Digital Image Processing(ISI 523)

Course Code Course Name Semester Theory Practice Lab Credit ECTS
ISI 523 Digital Image Processing 1 3 0 0 3 6
Prerequisites
Admission Requirements
Language of Instruction English
Course Type Elective
Course Level Masters Degree
Course Instructor(s) İsmail Burak PARLAK bparlak@gsu.edu.tr (Email)
Assistant
Objective Digital image processing is among the fastest growing computer technologies. Image and video modalities are considered as complex data structures due to multidisciplinary applications and broad range of file structures. With increasing computer power, it is now possible to do numerically many tasks that were previously done using analogue techniques. The objective of this course is to provide a brief introduction to methodologies applicable to digital image processing and to develop a foundation that can be used as the basis for further study and research in this field.
Content • Introduction, Image Representation, Image Encoding
• Intensity Transformations, Geometric Transformations
• Spatial Filtering, Fourier Transform, Short-Time Fourier Transform, Convolution
• Frequency Domain Filtering, Sampling
• Image Restoration, Image Enhancement
• Edge Detection-Sharpening
• Multi-resolution Analysis
• Image Pyramids
• Image morphology
• Wavelets
• Image Compression
• Applications: segmentation, watermarking, recognition
• Deep learning models in image & video
• Advanced topics: Video applications
Course Learning Outcomes
Teaching and Learning Methods
References Textbook(s):
R. Gonzalez and R. Woods Digital Image Processing, Pearson, 4th Edition, 2018
M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis and Machine Vision, 4th Edition Cengage Learning, 2015

Supplementary Books:
Alberto Fernandez Villan - Mastering OpenCV 4 with Python: A practical guide covering topics from image processing, augmented reality to deep learning with OpenCV 4 and Python 3.7 Packt Publishing, 2019
A. Murat Tekalp - Digital Video Processing (Prentice Hall Signal Processing) 2nd Edition, 2015
Ian Goodfellow, Yoshua Bengio, Aaron Courville – Deep Learning, MIT Press, 2016 https://www.deeplearningbook.org/
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Theory Topics
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Practice Topics
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Contribution to Overall Grade
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Toplam 0 0
In-Term Studies
  Number Contribution
Toplam 0 0
No Program Learning Outcomes Contribution
1 2 3 4 5
Activities Number Period Total Workload
Total Workload 0
Total Workload / 25 0,00
Credits ECTS 0
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