|Date||Topic ||Readings ||Presenter ||Assignments|
|Jan 24||Intro & Overview of Course - Slides||-||Tamara ||Get access to matlab, do a tutorial.
|Jan 26||Computer Vision Review - Slides||Sections 1.1-1.2||Tamara ||Choose a paper to present from the reading list, HW1 released |
|Jan 31||Computer Vision Review (cont) see slides from Jan 26||-||Tamara ||- |
|Feb 2||Bag of Features Models - Slides||Object Recognition from Local Scale-Invariant Features||Tamara ||- |
|Feb 7||Bag of Features Models - Slides||Visual Categorization with Bags of Keypoints||Tamara ||HW2 released |
|Feb 9||BoF (cont) & Spatial Models - Slides||Shape Matching and Object Recognition Using Low Distortion Correspondence||Tamara, Kalyan||- |
|Feb 14||To Categorize or Not to Categorize - Slides||Principles of Categorization,|
Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships
|Feb 16||Recognizing Attributes - Slides1||Attribute and Simile Classifiers for Face Verification,|
|Tamara, Chen||- |
|Feb 21||Faces||Face Recognition using Eigenfaces,|
Robust Real-time Face Detection
| Chaitanya, Vinay||HW3 released |
|Feb 23||Faces (cont)||-||-||- |
|Feb 28||Intro to Pose & Actions - Slides||-||Tamara||- |
|March 1||Pedestrian Detection||Histograms of Oriented Gradients for Human Detection,|
Pedestrian Detection in Crowded Scenes
|Hailin, Nihar||- |
|March 6||Project Proposals||-||-||Prepare a 5 minute proposal presentation |
|March 8||Pose Estimation in Images||Recovering Human Body Configurations: Combining Segmentation and Recognition,|
Poselets: Body Part Detectors trained Using 3D Human Pose Annotations
|Keerthi, Rajan||- |
|March 13||Action Recognition||Recognizing Action at a Distance,|
Learning Realistic Human Actions from Movies
|March 15||Intro to Scenes - Slides||Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories||Tamara||- |
|March 20||Scene Interpretation||Automatic Photo Pop-Up,|
Blocks World Revisited: Image Understanding Using Qualitative Geometry and Mechanics
|March 22||Project Updates||-||-||Prepare a 5 minute update presentation |
|March 27||Project Help Day||-||-||- |
|March 29||Scenes & What We can Do in Them||Recovering the Spatial Layout of Cluttered Rooms,|
From 3D Scene Geometry to Human Workspace
|Feifei, Fan||- |
|April 3||Spring Break||-||-||- |
|April 5||Spring Break||-||-||- |
|April 10||Words & Pictures||-||Tamara||- |
|April 12||Catch up Day||-||-||- |
|April 17||No Class - Traveling||-||-||- |
|April 19||Project Updates||-||-||Prepare a 5 minute update presentation |
|April 24||Generating Descriptions of Images||Baby Talk: Understanding and Generating Simple Image Descriptions,|
Im2Text: Describing Images Using 1 Million Captioned Photographs
|April 26||Predicting Aesthetic Quality||High Level Describable Attributes for Predicting Aesthetics and Interestingness,|
Assessing the aesthetic quality of photographs using generic image descriptors
|May 1||Final Project Presentations||-||Shobha & Rajan, |
Keerthi & Nihar,
Chaitanya & Kalyan,
Hanyu & Chen
|Prepare a 15 minute final presentation |
|May 3||Final Project Presentations||-||FeiFei & Hailin, |
Swastika & Rohit & Vinay,
|Prepare a 15 minute final presentation |
|May 8||Final Project Write-up||-||-||Project Write-up Due |
There will be 3 simple homeworks during the first 1-2 months of the course to
get students aquainted with computer vision and recognition. Over the final part of
the course students will develop and present a project related to recognition.
Students will also be responsible for leading one class paper discussion.
A few short quizzes will also be given about assigned papers.
Grading will consist of: Assignments (35%), Project (35%), Paper presentation (10%),
Paper quizzes (10%), Participation (10%).
No prior experience in computer vision is required to take this course.
Homeworks should be done individually, but projects may be done individually or in pairs.
Homeworks will be completed in matlab.
Submit all homeworks, and
presentations to: email@example.com
Useful links |
Student Matlab licenses can be purchased from mathworks for $99 - Link.
Matlab tutorial by Hany Farid and Eero Simoncelli - Link
A more comprehensive Matlab tutorial by David Griffiths - Link
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.