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About the Course

Department Profile
Course unit title Level of course unit Course unit code Type of course unit Semester of course unit Local credit ECTS credit Syllabus
SUSTAINABLE BUILDING MATERIALS AND EXPERIMENTS First cycle MİM 631 1 7.50 7.50 Print
   
Description of course unit
Prerequisites and course requisities
Language of instruction Turkish
Coordinator Dr. M. Caglar BAYDOGAN
Lecturer(s) Dr. M. Caglar BAYDOGAN
Teaching assitant(s) -
Mode of delivery theoric
Course objective Computer vision (artificial vision) research areas in urban studies and architecture. To determine in which field the obtained data can be evaluated.
Course description Analysis of computerized image algorithms for architectural design applications. Introduction to computer vision theory and practice, ie understanding the objects and processes that make them up in the world, analyzing patterns in visual images. Main topics include optics, image representation, feature extraction, image processing, object recognition, feature selection, probabilistic inference, perceptual analysis and organization, segmentation, feature-based alignment, 3D depth data processing. Developing concepts and algorithms to solve visual problems for architectural design applications and urban studies.

Course contents
1 Image Creation, Photometry, Color.
2 Projective Geometry.
3 Local Features and Grouping.
4 Analysis of Local Feature Extraction Techniques for Architectural Design
5 Pattern recognition and learning.
6 Object Recognition.
7 Tracking.
8 Analysis of Pattern and Object Techniques for Architectural Design.
9 Sampling Methods.
10 Feature-based image matching.
11 Analysis of use of motion estimation techniques for architectural design.
12 3D depth data processing.
13 Analysis of 3D depth data processing particularly for architectural design
14 Computer vision applications in architectural design.
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Learning outcomes of the course unit
1 Learning computer vision techniques.
2 Understanding image formation, image processing, feature detection, and matching.
3 To recognize computer vision techniques in architecture and urban studies.
4 Technical knowledge of image processing, image recognition, etc.
5 To gain the ability of coding information in the content.
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*Contribution level of the course unit to the key learning outcomes
1 To have sufficient knowledge about Mathematics, Science and Geomatics Engineering
2 An ability to identfy, define formulate and solve complex engineering problems
3 An ability to select appropriate analytical methods and modeling techniques and practice for problems
4 An ability to analyze and design system or system component
5 An ability to select and use modern techniques and tools for engineering practice
6 An ability to use communication technologies effectively
7 An ability to access information for this purpose to do research, use databases and other information resources
8 To have knowledge about computer software and hardware used in Geomatics Engineering
9 An ability to function on multi-disciplinary teams and to have the confidence to take responsibility
10 An ability to complete a job and to have solution for complex situations by taking responsibility
11 To have knowledge of foreign language for communicate with colleagues and reaching information about geomatic engineering
12 To have an ability to monitor developments in science and technology and be open to innovative ideas
13 An understanding of professional and ethical responsibility
14 To have an ability to inform specialist or non-specialist audience groups about engineering problems and solutions related issues
15 To have an ability to understand solutions of engineering and implementations in the universal and social dimensions
16 To have a knowledge of developing and implementing all kinds of projects in the field of
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Number of stars refer to level of contribution from 1 (the least) to 5 (the most)

Planned learning activities, teaching methods and ECTS work load
  Quantity Time (hour) Quantity*Time (hour)
Lectures (face to face teaching) 13 3 39
Study hours out of classroom (study before and after the class) 13 3 39
Homework 8 5 40
Presentation / seminar 2 3 6
Quiz 0 0 0
Preparation for midterm exams 0 0 0
Midterm exams 1 10 10
Project (term paper) 1 10 10
Laboratuar 0 0 0
Field study 0 0 0
Preparation for final exam 0 0 0
Final exam 0 0 0
Research 10 4 40
Total work load     184
ECTS     7.50

Assessment methods and criteria
Evaluation during semester Quantity Percentage
Midterm exam 1 50
Quiz 0 0
Homework 1 50
Semester total   100
Contribution ratio of evaluation during semester to success   40
Contribution ratio of final exam to success   60
General total   100

Recommended and required reading
Textbook
Additional references

Files related to the course unit