Grading will consist of projects (40%), topic presentations (40%),
participation (20%). Students will have a chance to present a relevant
research topic of their choice in small groups. They will also define and
implement a recognition project over the semester. Students are also expected to attend
class, read the assigned papers, and actively participate in group discussions.
If students do not demonstrate that they are reading the assigned papers, the
class may be asked to turn in short paper summaries prior to class. There will
not be any exams or formal homework assignments.
Prerequisites and Target Audience: No prior experience in computer vision is
required although some exposure to image processing, machine learning, or
graphics would be helpful. A previous course in linear algebra is recommended. The
course will start with some basic background and then move to reading and
discussion of relevant research papers and projects. This course is targeted toward
graduate students with an interest in computer vision. Undergrads may register
with permission of the instructor.
Email me or drop by my office if you have any questions!
Students will form small groups to prepare a presentation on a research
topic related to the course (group size will be determined based on
enrollment). Topics will be presented over a series of 2 lectures. Students
should read several papers related to their selected topic, then present a high
level cohesive summary of the topic (this should go beyond just detailing
specifics of 2-3 papers). 2-3 papers should also be selected for the entire
class to read and posted on the course website. Potential papers related to
each topic are posted here, but students may also
select their own relevant papers.
Students will implement course projects over the last 2 months of the semester.
Projects can range from implementation of a research paper to original
research. Project topics related to your research interests are encouraged.
Projects may be completed individually or in small groups in the programming
language of your choice. Projects will be evaluated based on 3 presentations
(proposal, update, and final presentation) and a final written report with
demo video if appropriate.
|Date||Topic ||Readings ||Presenter ||To Do|
|Aug 21||Intro - Slides||-||tamara||-|
|Aug 26||Computer Vision Review - Slides||-||tamara||-|
|Aug 28||Features Review - Slides||-||tamara||Form groups for topic presentations (potential papers for each topic here).|
|Sep 2||No Class - Labor Day||-||-||-|
|Sep 4||Machine Learning Review - Slides||-||tamara||-|
|Sep 9||Recognizing Objects (BoF models, spatial models) - Slides||Visual Categorization with Bags of Keypoints, |
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
|Sep 11||Recognizing Objects (BoF models, spatial models) - Slides||Shape Matching and Object Recognition Using Low Distortion Correspondence||tamara||-|
|Sep 16||Recognizing Objects (BoF models, spatial models) ||-||tamara||-|
|Sep 18||People (introduction) - Slides||-||tamara||-|
|Sep 23||Recognizing People (faces, actions) - Slides||"Face recognition via sparse sampling", |
"Active Shape Models - Their training and applications"
|group 1||kishore, aniket, keethan, dinghuang|
|Sep 25||Recognizing People (faces, actions) - Slides2||Learning Realistic Human Actions from Movies"||group 1||kishore, aniket, keethan, dinghuang|
|Sep 30||Localization (detection, pose) - Slides||"Histograms of Oriented Gradients for Human Detection",|
"Object Detection with Discriminatively Trained Part-Based Models"
|group 2||andrew, chun-wei, lu, qingyu|
|Oct 2||Localization (detection, pose) - Slides||"Recognition Using Visual Phrases"||group 2||andrew, chun-wei, lu, qingyu|
|Oct 7||Project Proposals||-||all||Prepare 5 minute proposal presentation|
|Oct 9||Project Proposals||-||all||Prepare 5 minute proposal presentation|
|Oct 14||Scenes (introduction)||-||tamara||-|
|Oct 16||Recognizing Scenes (recognition, parsing, surface recovery) - Slides1, Slides2||"SuperParsing: Scalable Nonparametric Image Parsing with Superpixels",|
"Recovering Surface Layout from an Image"
|group 3||hyo jin, ian, hongsheng, meng, young-woon|
|Oct 21||Recognizing Scenes (recognition, parsing, surface recovery) - Slides3, Slides4, Slides5||"From 3D Scene Geometry to Human Workspace"||group 3||hyo jin, ian, hongsheng, meng, young-woon|
|Oct 23||To Categorize or not to categorize - Slides1, Slides2|| "Cognition & Categorization",|
"TagProp: Discriminative Metric Learning"
|group 4||jared, joshua, schuyler, brian|
|Oct 28||To Categorize or not to categorize - Slides3, Slides4||"Exemplar SVM"||group 4||jared, joshua, schuyler, brian|
|Oct 30||Project Updates||-||all||Prepare 5 minute project update presentation & submit 2 page write-up detailing progress|
|Nov 4||Project Updates||-||all||Prepare 5 minute project update presentation & submit 2 page write-up detailing progress|
|Nov 6||Recognizing Attributes (as mid-level representation, relative attributes) - Slides1, Slides, Slides3||"Relative attributes",|
"Multi-attribute queries: To Merge or Not to Merge?"
|group 5||tianxiang, wen, yi, ke|
|Nov 11||Recognizing Attributes (as mid-level representation, relative attributes) - Slides1, Slides2||Attribute and Simile classifiers for Face verifications||group 5||tianxiang, wen, yi, ke|
|Nov 13||Recognizing perceptual phenomena (aesthetics, memorability)||"Finding Iconic Images"||group 6||dave, priyadarshi, sangwoo, niti|
|Nov 18||Recognizing perceptual phenomena (aesthetics, memorability)||"Assessing the aesthetic quality of photographs using generic image descriptors",|
What makes an image memorable?
|group 6||dave, priyadarshi, sangwoo, niti|
|Nov 20||What's next? (generating image descriptions, humans in the loop, large scale...)||-||tamara||-|
|Nov 25||What's next? (generating image descriptions, humans in the loop, large scale...)||-||tamara||-|
|Nov 27||No Class - Thanksgiving||-||-||-|
|Dec 2||Project Presentations||Prepare 5 minute project presentation||all||1) Dinghuang, 2) Hongsheng, 3) HyoJin & Meng, 4) Yi & Ke, 5) Dave, 6) Kishore, 7) Priyardashi, 8) Andrew & Chun-wei, 9) Sangwoo, 10) Brian|
|Dec 4||Project Presentations||Prepare 5 minute project presentation||all||1) Schuyler, 2) Keethan, 3) Wen, 4) Tianxiang, 5) Aniket & Niti, 6) Joshua, 7) Jared, 8) Lu, 9) Young-woon, 10) Qingyu|
|Dec 6||-||-||-||Final Project Reports and Demos/Videos due|
Useful links |
UNC students can get matlab from ITS here
Label Me - Link
Tiny Images - Link
Code for downloading Flickr images - Link
SIFT features - Link
Scale Invariant Interest Points - Link
Affine Covariant Regions - Link
Shape Contexts - Link
Gist - Link
Other Useful Software
Various Code from INRIA - Link
Various Code from Oxford - Link
Various useful machine learning tools - Link
Forsyth, David A., and Ponce, J. Computer Vision: A Modern Approach, Prentice Hall, 2003.
Hartley, R. and Zisserman, A. Multiple View Geometry in Computer Vision, Academic Press, 2002.
Stephen E Palmer, Vision Science: Photons to Phenomenology, MIT Press, 1999.
The professor reserves the right to make changes to the syllabus, including project due dates. These changes will be announced as early as possible.