1. Advanced Image Processing
Spring. 2017
Dept. of Computer Science and Engineering

2. Image Processing Definition[Wikipedia]
Processing of images using mathematical operations by using any form of signal processing for which
the input is an image, a series of images, or a video, such as a photograph or video frame;
the output of image processing may be either an image or a set of characteristics or parameters
 related to the image.

4. Areas of IP Early processing Segmentation Descriptors
Filtering for noise reduction
Enhancement
Super resolution
Feature extraction
Segmentation
Contour based
Region based
Video segmentation
Descriptors
Region Descriptors: color, texture
Relational Description
Image understanding
Object detection
Classification
Image coding
Vague boundary between IP and CV

5. Advanced Image Processing
Recommend prerequisites
Digital Image processing
Signal processing
Probability theory
Probability, Entropy, Bayes theorem
Linear Algebra
Matrix, Linear operators

6. Topics in Advanced Image Processing
Fourier Transform with its Applications
Wavelet Transform with its Applications
Image Pyramid and Multi-resolution
Other Image Transforms
Partial Differential Equations for Image Processing
Image Compression
Image Enhancement Techniques
Contrast Enhancement
Noise Reduction
Super Resolution
Local Image Descriptors
Colors
Image Restoration
Mathematical Morphology
Active Contours
Snake and Level Sets

7. But recently Because of machine learning
Some topics are out-of-date or replaced by deep learning in part.
Local descriptors
Visual descriptors of MPEG-7 becomes meaningless.
Mathematical morphology
Partial differential equation techniques
Image transforms
Image enhancement …
Now the changes is still going on.

8. Changes caused by … Machine Learning Deterministic models
Convolutional neural nets
Discriminative models in deep learning
Probabilistic models
Bayes theory, Graphical models
Generative models in deep learning

9. Strategy to Proceed IP class
Fundamentals should be covered at first by lecture.
Some topics need to be briefly introduced and to be covered by recent papers.
Milestone papers should be dealt with.
But too many papers are poured out.
You have to choose by yourself, read, and present in the class.

10. Not strictly organized class
Too many topics
Selected topics that depends on the interests of the lecturer
Rapidly changing area
Hard to catch up completely
Don’t know exactly what is next
I am interested in advanced early processing. So I am focused on that topic in the class.

11. Environment of Practice
So many open sources floating around the web.
OpenCV
Github
Libraries for Deep Learning
Programming Languages
Python
Matlab
C or Java

12. Grading Policy Midterm Exam 30% Assignment and computer projects 40%
Project Report
Objective
Procedure
Results
Discussion
References
Assignment will be given whenever a topic is finished.
Presentation after 2/3 of the semester 20%
Attendance in the class 10%

13. Note
Lecture material for coming Thursday will be uploaded no later than Tuesday in the week on
You should bring the downloaded copy in the class.
Try to follow the lecture plan on the web
Next week: Fourier transform and its applications