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Class Web page: Canvas page
TECHNOLOGY REQUIREMENTS: Access to canvas for course materials. Access to computer labs
Course Goals
The aim of CS334 is to
introduce fundamental techniques and concepts used in computational
imaging and multimedia. Upon completion of this course, a successful
student should be able to design and implement programs that deal with
image, video, and audio data.
Description:
This is a basic
undergraduate-level class that covers the fundamentals of image
processing, computer vision and multimedia computing. The students learn
about the basics of image, video, and audio formation, representations,
and processing, the basics of multimedia compression and
representation. The students will be exposed to dealing with image and
video data through programming assignments using Java and Python.
Recommended Background:
Linear algebra, basic
probability and statistics. Java and Python (you don’t need to know
Python in advance, but your will need to pick it up quickly early in the
course. We will provide help with that)
Pre-Requisites:
• 01:198:112 OR 14:332:351 (Data Structures)
• 01:198:206 OR 14:332:226 OR 01:640:477 (Discrete Mathematics and Probability)
• 01:640:250 (Linear Algebra)
Topics:
• Introduction to Multimedia: Historical overview, multimedia representations.
• Multimedia Digitization with digital camera as an example. Standard image formats. Colors in images and videos.
• Image Computing:
Point Operations, Filters, Binary image analysis: The basics of
processing 2D images, thresholding, convolution, edge and corner
detection, mathematical morphology, and shape descriptors.
Application: implementation of a simple Optical Character Recognition (OCR) System.
• Object detection and recognition in images: intro to deep learning models using convolution neural networks
• Fourier Transform: Understanding frequency components of signals, focusing on imaging.
• Multimedia compression basics: Lossless Compression: Variable length coding, Dictionary based coding.
Basics for Lossy
Compression: Fourier Transform, Discrete Cosine Transform. Application
to image compression (JPEG compression), Video compression (MPEGs),
Audio compression (MP3)
• Multimedia at the
age of AI: embedding of text, images, and other media and their
applications (text-to-image, text-to-speech, …)
Programming Assignments:
Course assignments
will be using Java, and/or Python. We will use ImageJ, which is an image
processing library using Java. We will also use imaging libraries in
Python.
Textbooks
• W. Burger & M.
Burge “Digital Image Processing: An algorithmic introduction using
Java”, Springer - Second Edition ISBN 978-1447166832 Available online
through Rutgers Libraries
• Ze-Nian Li, Mark S. Drew, Jiangchuan Liu “Fundamentals of Multimedia”, Springer 2014, Second Edition ISBN 978-3-319-05289-2
Available online through Rutgers Libraries
•Optional: P. Havaldar and G. Medioni “Multimedia Systems – Algorithms, Standards and Industry Practices”, Cengage Learning – 978-1-4188-3594-1 (recommended for some topics – not required)
Course Load
§ Homework/programming assignments and small projects: (55%) ~4 assignments. All assignments are equally weighted
§ Quizzes: ~6 quizzes (15%) In class. All quizzes are equally weighted.
§ Midterm: in class (15%), in late October – early November
§ Final: Online (15%)
§ Optional - Extra
credit Presentation: 5% can be achieved by researching and presenting a
relevant technology review topic – individuals or groups of 2.
Announcement will be made on how to apply.
Tentative Class Calendar (subject to change)
FM: Fundamentals of Multimedia textbook
DIP: Digital Image Processing text book
Academic Integrity: Rutgers University takes academic dishonesty very seriously. By enrolling
in this course, you
assume responsibility for familiarizing yourself with the Academic
Integrity Policy and the possible penalties (including suspension and
expulsion) for violating the policy.
As per the policy, all suspected violations will be reported to the Office of Student Conduct.
Academic dishonesty includes (but is not limited to):
• Cheating
• Plagiarism
• Aiding others in committing a violation or allowing others to use your work
• Failure to cite sources correctly
• Fabrication
• Using another person’s ideas or words without attribution–re-using a previous assignment
• Unauthorized collaboration
• Sabotaging another student’s work in doubt, please consult the instructor
Use of external
website resources such as Chegg.com or others to obtain solutions to
homework assignments, quizzes, or exams is cheating and a violation of
the University Academic Integrity policy. Cheating in the course may
result in grade penalties, disciplinary sanctions or educational
sanctions. Posting homework assignments, or exams, to external sites
without the instructor's permission may be a violation of copyright and
may constitute the facilitation of dishonesty, which may result in the
same penalties as plain cheating.
