Course objectives: Digital imaging has emerged
as the dominant technology for acquiring and working with images,
whether on the web, with a still camera or video. Here the issues
associated with acquiring useful information from digital images will
be considered from an artificial intelligence perspective. These
include image data structures, preprocessing for noise reduction and
feature enhancement, edge detection, segmentation, object recognition,
scene graphs, graph matching, top-down and bottom-up
image analysis. At the end of the course, students should be
knowledgable concerning the major steps and algorithms in the
end-to-end computer vision process beginning with image acquisition and
ending with a machine-produced description of the relevant scene
semantics.
Course syllabus: 1. Introduction (1/2 wk)
2. Digital images (1 wk)
3. Pre-processing of digital
images (1 2/3 wk)
4. Segmentation (1 2/3 wk)
5. Mathematical morphology (2 2/3
wk)
6. Shape and shape description (1
2/3 wk)
7. Object recognition (2 2/3 wk)
8. Image semantic understanding
(1 1/3 wk)
Registration:
CSE
473:
473L - lecture TR
2:00 - 3:20AM
110 Knox
089721: 473R1- recitation M 3:00 -
3:50PM 214 Norton
114074: 473R2 -
recitation W 12:00- 12:50PM 17 Clemen
067021: 473R3 -
recitation F 11:00-11:50AM
337
Bell
CSE 573:
573L -
lecture TR 2:00 -
3:20AM
110 Knox
190316: 573R1 -
recitation M 3:00 - 3:50PM 214 Norton
086068: 573R2 -
recitation W 12:00 -12:50PM 17 Clemen
103004: 573R3 -
recitation F 11:00 -11:50AM 337 Bell
Prerequisites: CSE305 or permission of instructor.
Course director: Peter
Scott, Associate Professor Dept. Computer Science and
Engineering, Rm. 136 Bell
Hall, 645-3180 x 137, mailto:peter@cse.buffalo.edu. Office hours
M 11:00AM-12:00PM, W 12:30PM-1:30PM, R
10:00AM-11:00AM, 136 Bell Hall.
TAs: Jim Evanko
mailto:jnevanko@buffalo.edu. Office hr time/place to be announced.
Achint Thomas
mailto:aothomas@buffalo.edu. Office hr Mondays 3:00pm-4:00pm in Bell
329.
Required textbook: Milan Sonka, Vaclav Hlavac and Roger
Boyle, "Image Processing, Analysis, and Machine Vision," Second or
Third Editions, Brooks/Cole Publishing Company, ISBN
053495393X (2nd edition), ISBN 049508252X (3rd edition).
Required work: In-class midterm and final exams, project,
four problem sets. Exams 75 minute open book, notes. For project
and homeworks, some
programming required. Problem sets (done individually) require use of
the Matlab application,
scripting language and the Matlab Image Processing Toolbox. Matlab is
mounted
on the CSE, ENG and CIT UNIX systems and available to all registered
students.
No prior knowledge of Matlab is assumed. For project, choice of
language
and development environment is up to each workgroup.
Grading: 25% each: midterm, final, problem set
average,
project.
Listserv newsgroup: sunyab.cse.573. A resource for
communication between students, and between course staff and students.
Registered students should check in at least once a week to see if
anything important for homeworks or exams, etc. has been posted. Beyond
that, you are encouraged to use the newsgroup to share information and
comments as you see fit, within the bounds of academic integrity and
good manners.
Academic integrity: The value of our courses, grades,
degrees and research findings are dependent upon adherence to standards
of ethical conduct. Plagiarism and inappropriate collaboration
will not be tolerated. In this course we will adhere to the CSE
departmental
standard for academic integrity. We quote here this standard as it
applies to
coding assignments and projects:
"The
following statement further describes the specific application of these
general
principles to a common context in the CSE Department
environment, the production of source code for project and homework
assignments. It should be thoroughly understood before
undertaking any cooperative activities or using any other sources in
such contexts.
All academic work must be your own. Plagiarism, defined as
copying or receiving materials from a source or sources and
submitting this material as one's own without acknowledging the
particular debts to the
source (quotations, paraphrases, basic
ideas),
or otherwise representing the work of another as one's own, is never
allowed. Collaboration, usually evidenced by unjustifiable
similarity, is never permitted in individual assignments. Any submitted
academic work may be subject to screening by software programs designed
to detect evidence of plagiarism or collaboration.
It is your responsibility to maintain the security of your computer
accounts and your written work. Do not share passwords with anyone, nor
write your password down where it may be seen by others. Do not change
permissions
to allow others to read your course directories and files. Do
not walk away from a workstation without logging out. These are
your responsibilities. In groups that collaborate inappropriately, it
may
be impossible to determine who has offered work to others in the group,
who has received work, and who may have inadvertently made their work
available
to the others by failure to maintain adequate personal security In such
cases, all will be held equally liable. "
Additional information on University-wide policies and
procedures is
contained in the UB
Academic Grievance Policy , and the UB
Office of Judicial Affairs .
Projects: Each student registered
for CSE473 or CSE573 must complete a project. Project groups of 2
students will be announced in lecture, and projects assigned to each
group at that time. Students within a project group are expected to
work collaboratively and submit a single project report. The same
project topic will be assigned to two groups, but please note that no
collaboration with students outside your announced project group is
permitted. A project report of 5-10 pages plus appendices is due
at the time the final exam is scheduled (to be
announced). The report will constitute the full documentation of your
work,
no executable code need be submitted electroncially, or
demonstrations done. This report should contain a concise problem
statement, clear description of the ideas and code you developed,
rationale for and description of the data and tests you used to
determine its performance, a clear statement of the results of the
tests, and discussion of these results. You should also attach a
clean copy of any source code you wrote as an appendix. This source
code should be commented to help a reader understand its logic.
Optionally, your report may also contain other elements such as
tutorial discussion,
literature citations, recommendations for future investigation, etc.
But
a maximum length of 10 typed pages is stipulated so be concise.
You
may work in any language and development environment you choose, for
instance
Borland C++ on a PC, Java on the CSE UNIX network, or Matlab
scripting language on the CSE, SEAS or CIT UNIX networks. Please read
your project
description carefully and make sure you understand the task that is
being
assigned before beginning your work.
Course schedule
