TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Bachelor's Degree
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	5
NAME OF LECTURER(S)	Assistant Professor Deniz Karaçor
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Know the mid-level image processing techniques. 2) Learn the morphological image processing algorithms. 3) Know the image segmentation. 4) Get an idea about the object recognition. 5) Gain the ability to develop an application for a specific problem.
MODE OF DELIVERY
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
COURSE DEFINITION
COURSE CONTENTS	WEEK TOPICS 1st Week Discrete time signals and systems. 2nd Week Discrete time signals and systems. 3rd Week Discrete time signals and systems. 4th Week Sampling, reconstruction and quantization. 5th Week Sampling, reconstruction and quantization. 6th Week Digital image display. 7th Week Digital image fundamentals. 8th Week Image transformations. 9th Week Image transformations. 10th Week Image enhancement. 11th Week Image enhancement. 12th Week Image repair. 13th Week Image repair. 14th Week Image segmentation and description.
RECOMENDED OR REQUIRED READING	1) Digital Image Processing by Rafael C. Gonzalez and Richard E. Woods, 3rd edition. 2) M. Sonka, V. Hlavac, R. Boyle, Image Processing, Analysis, and Machine Vision. 3) A. K. Jain, Fundamentals of Digital Image Processing, Prentice-Hall.
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Lecture,Questions/Answers,Problem Solving
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 30 Assignment 1 10 Quiz 1 20 Total(%) 60 Contribution of In-term Studies to Overall Grade(%) 60 Contribution of Final Examination to Overall Grade(%) 40 Total(%) 100
LANGUAGE OF INSTRUCTION
WORK PLACEMENT(S)	No