CS 559 Computer Vision
Fall 2014
Credits: 3 units
Class Hours: Monday and Wednesday 17:30 -18:45
Instructor: Mahmoud Tarokh, Ph.D., Professor of Computer Science
Email: mtarokh@mail.sdsu.edu
Office Hours: Mondays and Wednesdays 1400 – 1600 (and by appointment)
Course Assistant: Nishant Kumar, kmr.nishant@hotmail.com
Monday and Wednesday 16:00-17:30, Thursday 17:30-18:30
Course Materials
1. Required text: Textbook: N. Efford, Digital Image Processing, Addison-Wesley, 2004.
2. Supplementary Book: K.A. Hunt, The art of Image Processing, A.K. Peters, 2010.
3. Lecture Notes/slides: M. Tarokh, Computer Vision (available on Blackboard)
Course Information
Catalog Description: Algorithms and computer methods for processing of images. Visual
perception as a computational problem, image formation, characterization of images, feature
extraction, regional and edge detection, computer architectures for machine vision
Prerequisites: Computer Science 310-Data Structures, and Discrete Math. Math-245.
Course Type: Elective.
Student Learning Outcome
1. Ability to read and understand image processing software in Java.
2. Ability to create color images and modify them in Java.
3. Ability to apply early image processing techniques such as mapping and histogram
equalization.
4. Ability to perform edge detection, and spatial domain filtering to improve the quality of
images.
5. Ability to perform Fourier transform to images to filter noise.
6. Ability to write programs to detect objects of interest in an image.
7. Ability to perform compression of various types to images.
Topics Covered
1. Image representation; image formats; color images
2. Image representation, display and manipulation in Java
3. Image manipulation and enhancement; linear and nonlinear mapping; histogram
equalization.
4. Neighborhood operations and convolution; edge detection; noise and filtering in
spatial domain.
5. Frequency-domain image analysis and filtering; discrete Fourier transform and FFT;
low-pass, high-pass, band-pass and band-stop filters, applications to noise removal.
6. Segmentation; region detection; region growing; region and boundary representation.
7. Image compression; lossless compression methods, lossy compression and JPEG
format and procedures.
Assignments, Exams and Grading Policies
Assignments
There will be 5 assignments. Each student must work independently on assignments. The
assignments must be submitted in the specified format on the due date. The assignments in the
sequence gradually will require you to create more complex image processing programs.
Assignment 1: Selection of a camera for special applications, quantization and bilinear
interpolation for enlarging of images.
Assignment 2: Design and implementation for colorizing grayscale images.
Assignment 3: Design and implementation of edge detection by Canny algorithm and edge
detection for color images.
Assignment 4: Design and implementation of low pass and high pass filters in frequency domain.
Assignment 5: Design and implementation for region recognition in an image.
Exams
There will be two mid-term exams and the final. All exams are comprehensive.
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Mid-term exam 1 (October 13) will cover Topics 1, 2 and 3, and Assignments 1 and 2.
Mid-term exams 2 (November 17) will cover Topics, 2,3, 4 and 5 with more emphasis on
Topics 4 and 5. Assignments 2, and 3 are also included.
Final exam will cover all topics, all assignments with more emphasis on those topics not
covered in mid-term exams 1 and 2. See SDSU final exam schedule for the date of the
Final..
If you miss one mid term exam due to official university business or serious verified
illness, your final exam score will be used in lieu of the missed test.
You will not pass the course if you miss both mid term exams, the final, or more than one
assignment. No make up exam will be given.
Scores and weighting by percentage of total score
1. Assignments: 30%
2. Mid-term exam 1: 17%
3. Mid-term exam 2: 23%
4. Final: 30%
Grading Scale: Standard A,B,C,D,F
Other Course Policies
 Regular attendance is a requirement of this course, and you are responsible for all material,
schedules, assignments, etc. discussed in class or posted on the course website.
 Please come to the class on time and do not leave early, as these will cause disruption to the
class.
 Please do not eat, sleep or use your cell phone in class. Turn off your cell phone before
entering the room.
 Please do not ask or expect special case treatment, such as extension of your assignment
deadline, taking exams on a different date than those scheduled, make up exams/assignments,
etc.