Advanced Image Processing

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 orparameters  related to the image.

4. Areas of IP • Early processing • 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 • Probabilitytheory • 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 But recently • Becauseof machine learning • Some topics are out-of-date or replaced by deep learning in part. • Local descriptors • 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 http://ailab.chonbuk.ac.kr • 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