An introduction to the field of image processing, covering both analytical and
implementation aspects. Topics include the human visual system, cameras and
image formation, image sampling and quantization, spatial- and frequency-
domain image enhancement, filter design, image restoration, image coding and
compression, morphological image processing, color image processing, image
segmentation, and image reconstruction. Real-world examples and assignments
drawn from consumer digital imaging, security and surveillance, and medical
R. Gonzalez and R. Woods, Digital Image Processing, 4th Edition, Prentice Hall,
2018. ISBN: 978-0133356724. This book is a good reference to own, but
expensive. You can rent it for the semester from Amazon, or look for a
used/paperback version. Be aware that the 4th edition (which came out a few
years ago) is much more up-to-date and useful than the 3rd edition (and the 2nd
edition is by now fairly out of date).
Due to its fully online nature, this course will use a variety of online resources to
share and collect course content. These include:
Webex Teams: we will hold lectures in the Webex Teams space for the
class. I will also be monitoring the Teams space outside of class for
fielding quick questions.
Mediasite: the lectures will be recorded and posted on RPI’s Mediasite
Youtube: A full set of video lectures from a previous offering of the
course is available at http://tinyurl.com/RadkeIP. Each long video is
annotated with a table of contents that makes it easy to jump to a specific
topic within the lecture. You can use these videos to review concepts or
learn about material not being discussed this semester. These will be
similar to (but a little outdated compared to) the fresh Mediasite videos.
Piazza: all the homeworks and class materials will be posted and linked
here. This is also the preferred discussion forum for asynchronous chat
and threads about specific homework or exam questions.
Gradescope: all assignments will be submitted and graded here. Make
sure you have a way to capture your handwritten solutions on a piece of
paper to upload to Gradescope (the phone app makes this easy).
Gather: The TA and I will hold office hours at
(password: processmyimage) which is a fun, videogame-like site that
makes it easy to form subgroups, share google docs and whiteboards, and
drop in and out of conversations.
Course Goals / Objectives
The main objective of this course is to expose you to the wide variety of
analytical and algorithmic tools used to analyze digital images. Many of the
tools we discuss are built into software like Instagram and Photoshop, and others
form the basis for designing more advanced tools for computer vision: the
automatic understanding of image content. This course provides the basis for
further study in graduate-level image processing, pattern recognition, and
computer vision courses.
A tentative course outline and calendar are given below (although the topics
covered may change slightly over the course of the semester, especially towards
the end as we cover advanced topics). Note that we will not cover all of the topics
in the same order as the textbook.
Student Learning Outcomes
1. An understanding of the basics of image formation and perception
2. An understanding of spatial- and frequency-domain image filtering techniques
for common image processing tasks such as edge detection, blurring, and
3. An understanding of image transformations corresponding to desired
geometric and photometric operations
4. An understanding of algorithms for image compression, restoration and
5. An understanding of the building blocks of computer vision, such as line
detection, image segmentation, and object recognition
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