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)	Associate Professor Emre Sümer
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Know the fundamental concepts of computer vision. 2) Know the image formation and representation. 3) Learn the segmentation, feature extraction, contour and region analysis along with the camera geometry and calibration. 4) Comprehend the object and scene recognition. 5) Have an opinion about the human activity recognition and inference.
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	BIL 421 - Image Processing
COURSE DEFINITION	Fundamental concepts of computer vision. Visual perception of human being. Mathematical foundations. Image formation and representation. Segmentation, feature extraction, contour and region analysis, camera geometry and calibration. Motion capture. 3-D reconstruction. Object and scene recognition. Object and people tracking. Human activity recognition and inference.
COURSE CONTENTS	WEEK TOPICS 1st Week Fundamental concepts of computer vision. 2nd Week Visual perception of human being. 3rd Week Mathematical foundations. 4th Week Image formation and representation. 5th Week Segmentation, feature extraction, contour and region analysis, camera geometry and calibration. 6th Week Motion capture. 7th Week 3-D reconstruction. 8th Week Mid-term 9th Week Object and scene recognition. 10th Week Object and people tracking. 11th Week Human activity recognition and inference. 12th Week Human activity recognition and inference. 13th Week Human activity recognition and inference. 14th Week Human activity recognition and inference.
RECOMENDED OR REQUIRED READING	1. Szeliski Richard, Computer Vision: Algorithms and Applications, ISBN: 9781848829343, Springer, 2010. 2. Parker J.R., Algorithms for Image Processing and Computer Vision, 2/E, ISBN: 0470643854, Wiley, 2010.
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Questions/Answers,Lecture,Problem Solving,Experiment,Project
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 30 Quiz 4 10 Project 3 20 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 1,5 1,5 Preparation for Quiz 4 1 4 Individual or group work Preparation for Final exam 1 30 30 Course hours 14 4 56 Preparation for Midterm exam 1 20 20 Laboratory (including preparation) Final exam 1 2 2 Homework 3 10 30 Quiz 4 ,5 2 Total Workload 145,5 Total Workload / 30 4,85 ECTS Credits of the Course 5
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No