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

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.

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

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

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

But recently Because of machine learning Some topics are out-of-date or replaced by deep learning in part.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.

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

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.

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.

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

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%

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