TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Doctorate Of Science
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	10
NAME OF LECTURER(S)	Professor Murat Emin Akata
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Know about history and applications of image processing. 2) Learn digital image processing models. 3) Learn spatial and gray-level resolution. 4) Learn pixel basis image operations. 5) Learn and apply arithmetic/logic operations on images. 6) Learn and apply image enhancement and filtering methods.
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	None
COURSE DEFINITION	Introduction, image formation; image model, imaging devices, low level vision: smoothing, edge detection, edge linking, multiscale approaches, Intermediate level vision: surface reconstruction, shape from shading, motion and stereo, range imaging, high level vision; model-based vision, semantic nets, generalized cylinders, Hough transform.
COURSE CONTENTS	WEEK TOPICS 1st Week Introduction, image formation 2nd Week Introduction, image formation 3rd Week Image model, image acquisition schemes 4th Week Image model, image acquisition schemes 5th Week Imaging devices, low level vision: smoothing, edge detection, edge linking, multiscale approaches 6th Week Imaging devices, low level vision: smoothing, edge detection, edge linking, multiscale approaches 7th Week Intermediate level vision: surface reconstruction, shape from shading, motion and stereo, range imaging 8th Week Midterm Exam 9th Week Intermediate level vision: surface reconstruction, shape from shading, motion and stereo, range imaging 10th Week High level vision: model-based vision, semantic nets, generalized cylinders 11th Week High level vision: model-based vision, semantic nets, generalized cylinders 12th Week High level vision: model-based vision, semantic nets, generalized cylinders 13th Week Hough transform 14th Week Hough transform
RECOMENDED OR REQUIRED READING	1. Digital Image Processing, Rafael C. Gonzales and Richard E. Woods, Printice Hall, 2002.
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Presentation,Lecture,Report Preparation
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 25 Assignment 1 25 Quiz 2 15 Attendance 1 5 Total(%) 70 Contribution of In-term Studies to Overall Grade(%) 70 Contribution of Final Examination to Overall Grade(%) 30 Total(%) 100
ECTS WORKLOAD	Activities Number Hours Workload Midterm exam 1 3 3 Preparation for Quiz 0 0 0 Individual or group work 14 8 112 Preparation for Final exam 1 40 40 Course hours 14 3 42 Preparation for Midterm exam 1 30 30 Laboratory (including preparation) 0 0 0 Final exam 1 3 3 Homework 2 35 70 Total Workload 300 Total Workload / 30 10 ECTS Credits of the Course 10
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No