CS414 Computer Vision

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Course Code Course Title Weekly Hours* ECTS Weekly Class Schedule
T P
CS414 Computer Vision 3 2 6 Mon 13:00-14:50; Wed 11:00-11:50
Prerequisite MATH201, CS103 It is a prerequisite to
Lecturer Sadina Gagula Palalic Office Hours Schedule Mon 15:00-16:00; Tue 11:00-13:00; Wed 12:00-14:00
E-mail sadina@ius.edu.ba
Phone +387 33 957 201 Office / Room No A F1.13
Assistant
E-mail sadina@ius.edu.ba
Course Objectives
Textbook Digital Image Processing, Gonzales R.C., Prentice Hall, 2019
Learning Outcomes After successful  completion of the course, the student will be able to:
  1. Apply techniques to extract useful features from an image
  2. Apply techniques to recognize patterns and objects
  3. Understand and apply theoretical and practical capabilities of Computer Vision
  4. Formulate solutions to problems in Computer Vision
Teaching Methods
WEEK TOPIC REFERENCE
Week 1 Introduction to Computer Vision systems and its applications and challenges Ch 1
Week 2 Image Formation. Image Acquisition. Image Sampling and Quantization. Image Representation Ch 2.3 - 2.5
Week 3 Image preprocessing (Filtering) Ch 3.2 - 3.3
Week 4 Image segmentation (Filtering) Chapter 3.4 – 3.5
Week 5 Image segmentation Chapter 10.1 – 10.2
Week 6 Image segmentation Chapter 10.3 – 10.4
Week 7 MIDTERM EXAM
Week 8 Color image processing Chapter 6.1 - 6.7
Week 9 Morphological operations Chapter 9
Week 10 Morphological operations Chapter 9
Week 11 Representation and Description Chapter 11
Week 12 Representation and Description Chapter 11
Week 13 Pattern Recognition and Training Chapter 12.1-12.2
Week 14 Project Presentations
Week 15 Project Presentations
Assessment Methods and Criteria Evaluation Tool Quantity Weight Alignment with LOs
Final Exam 1 30
Semester Evaluation Compenents
Midterm 1 20
Assignments 5 25
Quiz 1 10
Project 1 15
***     ECTS Credit Calculation     ***
 Activity Hours Weeks Student Workload Hours Activity Hours Weeks Student Workload Hours
Lecture hours 3 15 45 Midterm exam study 10 1 10
Home study 2 15 30 Final exam study 10 1 10
Assignments 5 6 30 Final project study 5 5 25
        Total Workload Hours = 150
*T= Teaching, P= Practice ECTS Credit = 6
Course Academic Quality Assurance: Semester Student Survey Last Update Date: 07/10/2019
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