This course provides an introduction to the basic concepts and techniques used in digital image and video processing. Two-dimensional sampling and quantization are studied, and the human visual system is reviewed. Edge detection and feature extraction algorithms are introduced for dimensionality reduction and feature classification. High-pass and bandpass spatial filters are studied for use in image enhancement. Applications are discussed in frame interpolation, filtering, coding, noise suppression, and video compression. Some attention will be given to object recognition and classification, texture analysis in remote sensing, and stereo machine vision.
Weekly Schedule:
(Week 01) Introduction to Image Processing and its applications Chapter 1
(Week 02) Two-dimensional sampling theory, quantization, and convolution Chapter 2
(Week 03) Two-dimensional convolution and correlation Chapter 2
(Week 04) Image transforms and their properties Chapter 3
(Week 05) Image enhancement by Histogram modification Chapter 4
(Week 06) Image filtering, image sharpening and noise removal Chapter 4
(Week 07) Image compression MID-TERM EXAM DUE (Take Home) Chapter 6
(Week 08) image segmentation Chapter 7
(Week 09) Edge detection techniques Chapter 7
(Week 10) Feature extraction and representation Chapter 8
(Week 11) Model-based object recognition Notes
(Week 12) Introduction to neural networks for pattern classification Notes
FINAL EXAM (Take Home)
The course materials are divided into modules which can be accessed by clicking Modules on the course menu. A module will have several sections including the overview, content, readings, discussions, and assignments. You are encouraged to preview all sections of the module before starting. Most modules run for a period of seven (7) days, exceptions are noted in the Course Outline. You should regularly check the Calendar and Announcements for assignment due dates.
(Week 01)Introduction to Image Processing and its applications Chapter 1 + Notes
(Week 02)Two dimensional sampling theory, quantization and convolution Chapter 2 + Notes
(Week 03)Two dimensional convolution and correlation Chapter 2 + Notes
(Week 04)Image transforms and their properties Chapter 3 + Notes
(Week 05)Image enhancement by Histogram modification Chapter 4 + Notes
(Week 06)Image filtering, image sharpening and noise removal Chapter 4 + Notes
(Week 07)Image compression MID-TERM EXAM DUE (Take Home) Chapter 6 + Notes
(Week 08)image segmentation Chapter 7 + Notes
(Week 09)Edge detection techniques Chapter 7 + Notes
(Week 10)Feature extraction and representation Chapter 8 + Notes
(Week 11) Model-based object recognition Notes
(Week 12)Introduction to neural networks for pattern classification Notes
To understand and describe basic image processing algorithms. To implement image processing algorithms and understand the results.
DIGITAL IMAGE PROCESSING by Rafael C. Gonzalez and Richard E. Woods, (Fourth Edition) Prentice-Hall, 2013, ISBN-13: 978-1292223049, ISBN-10: 1292223049. (not required, recommended)
Course notes for each week will be emailed and avaiable as modules onn the course website.
There are 9 HWs all need to be submitted in pdf format to the portal. Each HW carries 100 points. Only two HWs can be sub,itted late.
Home Works: 30%
Mid-Term Exam: 30%
Final Exam: 40%
Only two HWs are allowed to be submitted late.
Deadlines for Adding, Dropping, and Withdrawing from Courses
Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar. Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.
Academic Misconduct Policy
Students with Disabilities - Accommodations and Accessibility
Student Conduct Code
Classroom Climate
JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity. If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).
Course Auditing
When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team (EP-Registration@exchange.johnshopkins.edu) in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.