We assume familiarity with calculus, basic concepts of linear algebra, basic probability, and programming experience, especially programming in MATLAB.
- CMPSC 201C or 103 or equivalent.
- MATH 230 or 231 or equivalent.
There is no required textbook, but you may find it useful to locate one or two for background reading. Here are some recommended references:
- Computer Vision, Algorithms and Applications by Richard Szeliski, Springer, 2011, ISBN: 9781848829343 is a good reference text and is available for free at http://szeliski.org/Book/. In addition, here is the link if you want to rent or purchase the electronic copy of this book. https://www.vitalsource.com/educators/products/computer-vision-richard-szeliski-v9781848829350?term=9781848829350
- Computer Vision: Models, Learning, and Inference by Simon Prince, Cambridge Univ Press, 2012. Available at http://www.computervisionmodels.com, it is beautifully illustrated and emphasizes the use of statistical machine learning in computer vision.
- Concise Computer Vision by Reinhard Klette, ISBN: 9781447163190: This textbook provides an accessible general introduction to the essential topics in computer vision.
- Introduction to Deep Learning by Sandro Skansi. This textbook presents a concise, accessible and engaging first introduction to deep learning
Course Goals and Objectives:
- Introduce the fundamental problems of computer vision.
- Introduce the main concepts and techniques used to solve those problems.
- Enable students to implement vision algorithms.
- Enable students to make sense of the vision literature.
Problem sets assignments are done individually and are due at the date/time specified. Problems will be assigned weekly or biweekly. Late homework will not be accepted. Some of them will be online questions that can be graded automatically by the Canvas system. When that happens, it will not be possible to get partial credit for wrong answers, so double check your answers before submitting. All homework will be submitted electronically in Canvas. This may require scanning in handwritten sketches or equations, or drawing them in some software package. Take a moment now to figure out how to do this so you won’t be running around in a panic before the first deadline trying to get your answers into the computer.
There will be two online midterm exams and we do NOT have a final exam. No makeup exams will be given unless there are extra ordinary circumstances, such as a significant illness, family emergency, etc…. Please notify the instructor BEFORE a missed exam. In addition, submit written or printed documentation of the reason for your absence. Cheating on any (exam) will result in a 0 grade on that exam/quiz.
- Projects may be assigned biweekly. All projects are to be submitted on Canvas by the specified date and time, a 5% deduction for each day of late submission for a maximum of 5 days (one minute will be counted as one day). No submission will be accepted after the 5th day of the due date. Your code must be in running order, and adhere to input and output formats that will be specified.
- We are going to run your code on new input data! If it doesn’t work, the grade will reflect that. The projects grade will be based on your code, a problem statement, description of solution approach, rationale for any design decisions made, description of user-defined parameter settings, pictures of results produced, and a discussion of the results, including explanation of any deficiencies observed.
- Computer projects can be done using the language of your preference (MATLAB is preferred), but we have to be able to run it on machines at PSU.
Problem Sets: 30%
Exam 1: 15% (Tentative date: July 11th and 12th)
Exam 2: 15% (Tentative date: July 31st and August 1st)
A 100-94 B+ < 90 – 86 C+ < 77 – 73 D < 68 – 61
A- <94- 90 B < 86 – 81 C < 73 – 68 F < 61 – 0
B- < 81-77
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