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)	-
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Know the low- and mid-level image processing techniques such as filtering, edge detection, segmentation and clustering. 2) Know about the object and scene recognition. 3) Know the techniques of motion detection from video data. 4) Know about the object and people tracking. 5) Comprehend the human activity recognition and inference.
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	BIL 566 Digital Image Processing
COURSE DEFINITION	An introduction and basic concepts, low and mid level image processing: filtering, edge detection, segmentation and clustering, object and scene recognition, motion detection from video data, object and people tracking, human activity recognition and inference.
COURSE CONTENTS	WEEK TOPICS 1st Week An introduction and basic concepts 2nd Week Low and mid level image processing:filtering 3rd Week Low and mid level image processing:filtering 4th Week Edge detection 5th Week Segmentation and clustering 6th Week Object and scene recognition 7th Week Object and scene recognition 8th Week Mid-term 9th Week Motion detection from video data 10th Week Motion detection from video data 11th Week Object and people tracking 12th Week Object and people tracking 13th Week Human activity recognition and inference 14th Week Human activity recognition and inference
RECOMENDED OR REQUIRED READING	1. Forsyth, D.A. & Ponce, J., "Computer Vision: A Modern Approach", 2nd edition, Prentice Hall, (2011). 2. Shapiro, L.G. & Stockman, G.C., "Computer Vision", Prentice Hall, (2001). 3. Parker, J.R., "Algorithms for Image Processing and Computer Vision", Wiley, (2010).
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Lecture,Questions/Answers,Practice,Problem Solving,Project,Report Preparation,Presentation
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 30 Project 1 30 Total(%) 60 Contribution of In-term Studies to Overall Grade(%) 60 Contribution of Final Examination to Overall Grade(%) 40 Total(%) 100
ECTS WORKLOAD	Activities Number Hours Workload Midterm exam 1 2 2 Preparation for Quiz Individual or group work 14 11 154 Preparation for Final exam 1 69 69 Course hours 14 3 42 Preparation for Midterm exam 1 44 44 Laboratory (including preparation) Final exam 1 2 2 Homework Total Workload 313 Total Workload / 30 10,43 ECTS Credits of the Course 10
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No