CMSC 426
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Announcements
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General Information |
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Course Policies |
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Course work, late policies, and grading |
There
will be (probably) eight problem sets assigned during the semester. Six of
these will include programming assignments to be done in Matlab.
Matlab is free to download throughout the
university. Students can download and install matlab
through Some
of these will also include pencil and paper exercises. Two problem sets will be
purely pencil and paper exercises. Students will have one or two weeks for
each problem set, depending on its size.
We will also have some in-class workshops, in which students work in
groups on problems related to recently discussed material. Attendance is required. In addition there will be a computer vision
workshop at the Homework
is due at the start of class. Problems may be turned in late, with a penalty
of 10% for each day they are late, but may not be turned in after the start
of the next class after they are due. For example, if a problem set is due on
Tuesday, it may be turned in before Wednesday at 9:30, with a 10% penalty, or
before Thursday at 9:30, with a 20% penalty, but no later than Thursday at
9:30. There will be three exams. Early in the semester, there will be a short,
in-class midterm that will take about half the class. Later, there will be a
midterm that will be in class for a complete class period. The final will be
during the normal final exam time. Each exam will be cumulative, covering all
material learned to that point in the class, with a greater emphasis on
material learned since the previous exam. Exams and problem sets will all be
based on material discussed in class. These will be weighted for the final grade as: Homework 35%, summary of talk, in class workshops, 4%, first midterm 9% second midterm 18%, final exam 34%. Homework
assignments are to be written up neatly and clearly, and programming
assignments must be clear and well-documented. Programs should be written in Matlab. A full paper copy of all of the homework must be
turned in. In addition, we will ask you to email a copy of all code to the
TA. Some homeworks and projects may have a special challenge problem. Points from the challenge problems are extra credit. This means that I do not consider these points until after the final course grade cutoffs have been set. Students participating in class discussion or asking good questions will also receive extra credit. |
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Academic Honesty |
All
class work is to be done independently. You are allowed to discuss class
material, homework problems, and general solution strategies with your
classmates. When it comes to formulating/writing/programming solutions you
must work alone. If you make use of other sources in coming up with your
answers you must cite these sources clearly (papers or books in the
literature, friends or classmates, information downloaded from the web,
whatever). It is best to try to solve problems on your own, since problem solving is an important component of the course. But I will not deduct points if you make use of outside help, provided that you cite your sources clearly. Representing other people's work as your own, however, is plagiarism and is in violation of university policies. Instances of academic dishonesty will be dealt with harshly, and usually result in a hearing in front of a student honor council, and a grade of XF. (Note, this and other course policies are taken from those of Prof. David Mount). |
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Any student
who needs to be excused for an absence from a single lecture, recitation, or
lab due to a medically necessitated absence shall: a) Make a reasonable
attempt to inform the instructor of his/her illness prior to the class. b)
Upon returning to the class, present their instructor with a self-signed note
attesting to the date of their illness. Each note must contain an
acknowledgment by the student that the information provided is true and
correct. Providing false information to University officials is prohibited
under Part 9(h) of the Code of Student Conduct (V-1.00(B) University of
Maryland Code of Student Conduct) and may result in disciplinary action. The
self-documentation may not be used for the Major Scheduled Grading Events as
defined below and it may only be used for only 1 class meeting (or more, if
you choose) during the semester. Any student who needs to be excused for a
prolonged absence (2 or more consecutive class meetings), or for a Major
Scheduled Grading Event, must provide written documentation of the illness
from the Health Center or from an outside health care provider. This
documentation must verify dates of treatment and indicate the timeframe that
the student was unable to meet academic responsibilities. In addition, it
must contain the name and phone number of the medical service provider to be
used if verification is needed. No diagnostic information will ever be
requested. The Major Scheduled Grading Events for this course include: a)
Midterm - October 9 during the lecture period b) Final exam, as given in
University schedule. |
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Academic Accommodations |
Any
student eligible for and requesting reasonable academic accommodations due to
a disability is requested to provide, to the instructor in office hours, a
letter of accommodation from the Office of Disability Support Services (DSS)
within the first two weeks of the semester. |
Below is a tentative schedule for problem sets and lectures.
Class slides will typically be posted here, usually minutes before class begins.
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Class
Slides |
SLIDES |
NOTES |
READINGS |
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Class
1: Introduction (Jan 26) |
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Class
2: Background Review and Intro to Matlab (Jan
31) |
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Class 3: Filtering and Correlation(Feb 2) |
Correlation and
Convolution Notes |
Tutorial
from Sussex on Convolution Szeliski,
Section 3.2 |
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Class
4: Correlation Continued, 1D Edge Detection (Feb 7) |
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Class
5: 2D Edge Detection (Feb
9) |
Canny
Edge Detector, from CVonline Szeliski,
Section 4.2 |
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Class
6: Fourier Analysis. Gradient and
correlation Workshop. (Feb 14) |
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Szeliski,
Section 3.4 |
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Class
7: Human
Perceptual Grouping (Feb 16)
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"Laws of
organization in perceptual forms" |
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Class
8: Interactive Segmentation with Graph Cuts (Feb 21) |
Interactive
Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D
Images |
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The
Foundations of Color Measurement and Color Perception by Brian Wandell |
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Class
10: Short Midterm and Review of Results (Feb
28) |
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Class
11: Optical flow and corner detection (March 2) |
Szeliski, Section 4.1, Section 8.4 (up
to 8.4.1). |
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Class
12: Perspective, cameras (March 7) |
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Class
13: Prev. topics cont’d (March 9) |
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Class
14: SNOW DAY (March 14) |
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Class 15: Stereo (March 16) |
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Class
16: Stereo cont’d (March 28) |
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Class
17: Geometric transformations, Geometry Workshop (March 30) |
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Dave Mount's
Lecture Notes for Computer Graphics, Chapters 6, 7 and 8 might be
helpful. |
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Class
18: 2 frame structure-from-motion (April 4) |
Szeliski, Section 7.2 |
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Class
19: Midterm (April 6) |
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Class
20: Midterm post-mortum (April
11) |
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Class
21: Intro to machine learning (April 13) |
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A Course in Machine Learning chapters 1 and 2. |
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Class
22: Linear classification (April 18) |
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A Course in Machine Learning chapter 4. |
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Class 23:
CNNs, intro to matconvnet (April 20) |
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Class
24: Applications of CNNs: classification, detection, semantic segmentation. (April 25) |
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Class
25: Applications of CNNs: Faces (April 27) |
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Class
26: Gradient
Descent (May 2) |
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Class 27: Backpropagation (May 4) |
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Class 28: Workshop (May 9) |
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Class 29: Conclusions
(May 11) |
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FINAL May
15 8-10 |
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