• Basic information
• Learning outcomes
• Contents
• Assessment
• Workload

Learning outcomes

Upon successful completion of this course, students will be able to:

1. Type English text

   Contribution to Program Outcomes

   1. To gain insight and experience about the solution approaches to the technical problems encountered in projects.
   2. To follow the scientific literature in the related area of expertise.
   3. Acquire scientific knowledge.
   4. Find out new ways to improve current knowledge
   5. To be able to express ideas and findings related to the research topic both orally and in writing.
   6. Demonstrating professional and ethical responsibility.

   Method of assessment

   1. Homework assignment
   2. Seminar/presentation
   3. Term paper

Contents
Week 1:	Basic Image Processing Image Transformations, Image Arithmetic, Masking, Splitting And Merging Channels, Kernels, Morphological Operations, Smoothing and Blurring, Lighting and Color Spaces,
Week 2:	Image Segmentation Gradients, Point Detection, Line Detection, Edge Detection, Contour Segmentation, Hough Transforms, Thresholding
Week 3:	Image Descriptors/ Feature Extraction Image Descriptors, Feature Descriptors, Feature Vectors Color Histograms, Hu Moments, Zernike Moments, Haralick Texture, Local Binary Patterns,
Week 4:	Image Descriptors/ Feature Extraction Histogram of Oriented Gradients Local Invariant Descriptors, Harris, SIFT, SURF Binary Descriptors: BRIEF, ORB, BRISK, FREAK, Binary Feature Extraction and Matching
Week 5:	Object Detectors Introduction to Object Detection Template Matching Image Pyramids, Sliding Windows Building a Custom Object Detection Framework
Week 6:	Object Detectors Preparing Your Experiment and Training Data Constructing HOG Descriptor Non-Maxima Suppression Training Your Custom Object Detector
Week 7:	Machine Learning Introduction to ML Types of Learning Algorithms Pattern Recognition Overview of Image Classification The Image Classification Pipeline K-Nearest Neighbor Classification The Naive Bayes’ Classifier
Week 8:	Machine Learning Common Machine Learning Algorithms Logistic Regression, Support Vector Machines, Decision Trees, Random Forests, K-Means Clustering Bag of Visual Words for Classification Image Classification Examples
Week 9:	Deep Learning Neural Networks The Perceptron Algorithm Introduction to Deep Learning
Week 10:	Deep Learning Convolutional Neural Networks Training and Implementing CNN Architectures Image Classification with DL
Week 11:	Cameras Properties of a Digital Camera, Coordinates Camera Models, Intrinsic and Extrinsic Parameters, Camera Calibration
Week 12:	Raspberry Pi Computer Vision Projects Installing OpenCV on Raspberry Pi Setting Up Raspberry Pi Camera Accessing the Raspberry Pi Camera and Video Stream Home Surveillance and Motion Detection Face Recognition for Security
Week 13:	Case Studies 1. Face Recognition a. Face Detection in Images b. Face Detection in Video c. Local Binary Patterns for Face Recognition d. Face Recognition Using Eigenfaces Algorithm 2. Hand Gesture Recognition a. Introduction to Hand Gesture Recognition b. Hand and Finger Segmentation c. Recognizing Gestures
Week 14:	Case Studies 3. Automatic License Plate Recognition a. Localizing License Plates in Images b. Segmenting Characters From the License Plate 4. Object Tracking in Video 5. Identifying the Covers of Books
Week 15*:	-
Week 16*:	-
Textbooks and materials:	1. Computer Vision: Algorithms and Applications by Richard Szeliski 2. Programming computer vision with Python by Jan Erik Solem 3. Learning from Data by Yasee Abu Mostafa 4. Deep learning by Ian Good fellow and Yoshua Bengio
Recommended readings:	1. Computer Vision: Algorithms and Applications by Richard Szeliski 2. Programming computer vision with Python by Jan Erik Solem 3. Learning from Data by Yasee Abu Mostafa 4. Deep learning by Ian Good fellow and Yoshua Bengio
	* Between 15th and 16th weeks is there a free week for students to prepare for final exam.

Assessment
Method of assessment	Week number	Weight (%)
Mid-terms:		0
Other in-term studies:		0
Project:		50
Homework:		50
Quiz:		0
Final exam:		0
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	3	16
Own studies outside class:	3	16
Practice, Recitation:	0	0
Homework:	2	16
Term project:	2	16
Term project presentation:	2	16
Quiz:	0	0
Own study for mid-term exam:	0	0
Mid-term:	0	0
Personal studies for final exam:	0	0
Final exam:	0	0
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)