CS 474674 Image Processing Course Overview Fall 2021

CS 474/674 – Image Processing Course Overview Fall 2021 – Dr. George Bebis

General • Meets: MW 1: 00 pm – 2: 15 pm (WPEB 200) • Instructor: Dr. George Bebis – Office: 411 WPEB – Phone: (775) 784 -6463 – E-mail: bebis@cse. unr. edu • Course Webpage: http: //www. cse. unr. edu/˜bebis/CS 474 • Office Hours: MW 2: 30 pm – 4: 00 pm (we could also meet through Zoom)

Prerequisites • CS 202 and STAT 352 or STAT 461 – Good programming are very important! • Previous experience with processing images is a plus but not required. • Check out the code provided on the course webpage for reading/writing images. – Good math skills are essential! • Probabilities, Statistics, Linear Algebra, Calculus • We will review some important concepts.

Textbook • Digital Image Processing by R. Gonzalez and R. Woods, 4 th edition, Pearson, 2018. Resources Errata http: //www. imageprocessingplace. com/root_files_V 3/errata_sh eets. htm • Helpful Texts: – Image Processing, Analysis and Machine Vision, by M. Sonka, V. Hlavac, and R. Boyle, Cengage Learning, 2015. – Image Processing and Analysis, by S. Birchfield, Cengage Learning, 2018. – Digital Image Processing and Analysis, by S. Umbaugh, CRC Press, 2011.

Objectives • Digital image processing is among the fastest growing computer technologies. • This course will provide an introduction to theory and applications of digital image processing. • Introduce students to the fundamental techniques and algorithms used for processing and extracting useful information from digital images.

Course Outline (tentative) • • • Introduction to Image Processing Intensity & Geometric Transformations Spatial Filtering & Convolution Fourier Transform & Frequency Domain Filtering Sampling and Aliasing Image Restoration Image Compression Short-Time Fourier Transform (if time permits) Multi-resolution Representations & Wavelets (if time permits)

Intensity & Other Transformations Intensity Transformation Geometric Transformations Improve Image Quality

Spatial Filtering & Convolution Edge Detection Convolution

Fourier Transform & Frequency Domain Filtering Frequency Domain Band-reject filter Noise Filtering

Sampling and Aliasing A small number of samples might not represent well the original signal!

Image Restoration Model degradation Apply inverse model Blur due to motion Restored image

Image Compression Lossless Compression Information preserving Low compression ratios Lossy Compression Information loss High compression ratios

Short Time Fourier Transform • Fourier Transform cannot provide simultaneous time and frequency localization. Poor localization in time domain! Great localization in freq domain! • Short Time Fourier Transform addresses this issue by applying a “window” function.

Multi-resolution Representations & Wavelets Multi-resolution representation Extract information at different levels of detail Wavelet functions

Quizzes and Exams • There will be 6 -7 quizzes. – Quizzes will be announced in advance. – Closed notes/books. – Lowest grade will be dropped. • Two exams (i. e. , midterm and final) – Closed notes/books. – Final exam will be comprehensive. Time allotted Quizzes 10 minutes (beginning of the class) Midterm 1. 15 hours Final 2 hours

Programming Assignments • There will be 4 -5 programming assignments – Will be done in groups of two students – OS: Windows or Linux – Language: C or C++ (for CS majors); exceptions can be made for non-CS majors (e. g. , Python, Matlab). • Programming assignment reports need to be submitted on Canvas. – If you have problems submitting your work, email it to me as close to the deadline as possible.

Presentation • Graduate students would be required to present a paper to the rest of the class. – A list of papers will be provided after the midterm. – Presentations will be scheduled towards the end of the semester.

Course Policies • Lecture slides, assignments, and other useful information will be posted on the Web. • If you are unable to take an exam you must inform me in advance. Exams cannot be made up unless there is an extreme emergency. • Discussion of your work with others is allowed and encouraged. However, each student should do his/her own work. Assignments which are too similar will receive a zero.

Course Policies (cont’d) • No late work will be accepted unless there is an extreme emergency. • If you are unable to hand in your work by the deadline, you must discuss it with me before the deadline. • No incomplete grades (INC) will be given in this course.

Course Policies (cont’d) • Students are expected to attend, and be on time, for every class. • If you miss a class, you are responsible for all material covered or assigned in class.

Course Policies (cont’d) • The instructor reserves the right to add to, and/or modify any of the above policies as needed to maintain an appropriate and effective educational atmosphere. • If this happens, all students will be notified in advance of implementation of the new and/or modified policy.

Other Policies (see syllabus) • • • Academic Dishonesty Disability Services Academic Success Services Audio and Video Recording Safe Learning Environment COVID’ 19 info

Grading • • • Quizzes: 20% Midterm Exam: 20% Final Exam: 20% Prog. Assign: 40% Paper Presentation: 10% (grad students only) • • • A B C D F 90% and above 80%-89% 70%-79% 60%-69% <59%

Important Dates • • • 9/6/2021 – Labor Day (no class) 10/25/2021 – Midterm exam 10/27/2021 – Final day to drop classes and receive a "W" 12/8/2021 – Prep Day 12/13/2021 - Final exam (9: 50 am – 11: 50 am) • Zoom lectures the weeks of Oct 4 th and Oct 11 th at 6 pm (conflict due to conference organization)

Note Taker Need • We have a student in the class who is in need of a note taker. • Please, see me after class if you are interested in helping out. • Note taker would need to submit an application at the Disability Resource Center (Pennington Student Achievement Center, Suite 230).

Thank you! Questions?