| Course Introduction |
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Collins |
Intro to Course (Jan 12)
[slides]
[6 per page] |
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Paper Search/Reading/Writing/Vision Resources |
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| Gaussian Mixture Models and Expectation Maximization |
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Lecture Notes |
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Collins |
The Gaussian Distribution (Jan 12, 14)
[scanned notes] |
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Collins |
GMM and EM, Part 1 (Jan19)
[slides]
[6 per page] |
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Collins |
GMM and EM, Part 2 (Jan21,Jan26)
[slides]
[6 per page] |
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Incremental Programming Assignments |
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Set of EM assignments |
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hand in on Feb 9 as part of the EM Project. |
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Reference Material |
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Intro to Gaussian Distribution , Bishop, PRML book |
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Estimating Gaussian Mixture Densities with EM - A Tutorial, Carlo Tomasi. |
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Mixture Models and EM , Bishop, PRML book |
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Old and New Matrix Algebra Useful for Statistics, Tom Minka. |
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Research Papers / Oral Presentations |
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Thursday, Jan 28 |
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Collins |
Statistical Color Models
with Applications to Skin Detection, Jones and Rehg
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sample presentation on the Jones and Rehg paper.
[slides]
[6 per page]
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sample critique on the Jones and Rehg paper.
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I'll also present some recent work done here, using mixtures of Bernoulli distributions.
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Tuesday, Feb 2 |
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Sitichai |
Adaptive Background Mixture Models for Real-time Tracking, Stauffer and Grimson |
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related paper of interest:
Understanding Background Mixture Models for Foreground Segmentation, Power and Shoonees |
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Jingchen |
Tracking Colour Objects using Adaptive Mixture Models, McKenna et.al. |
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Project proposal: 1 page summary of what you are doing for the EM project. |
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Thursday, Feb 4 |
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Vairoj |
Blobworld: Image Segmentation using EM ..., Carson et.al. |
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Ankit |
Mixture Models for Optical Flow Computation, Jepson and Black |
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Critiques Due: written critiques for at least 2 papers we read. |
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Thursday, Feb 18 |
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Spotlight Presentations: 2-3 minutes presentation of your EM project. |
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Projects Due: code + writeup + spotlight slides. |
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EM Project Ideas |
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- From Bishop book: use kmeans or EM to label/compress colors in an image.
Try diff values of K. maybe compare k-means and EM results. Bishop's idea text is
available here
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- From Forsyth and Ponce: a number of ideas, including color/texture
segmentation (e.g. blobworld), fitting line segments (maybe compare EM with RANSAC?),
motion segmentation, background subtraction (Power and Shoonees paper). See the
[Forsyth and Ponce ideas].
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- Two other project ideas from the last time I taught this course. One
involves motion segmentation (I provide a dataset), one involves experimenting with
the Jones and Rehg skin color classifier.
[here are the ideas].
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- Of course, I encourage you to come up with your own project ideas
involving research or data that is relevant to you.
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| Procrustes Analysis |
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Lecture Notes |
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Collins |
Intro to Procrustes Analysis (Feb 9,11)
[slides]
[6 per page] |
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Least Squares Approach. scanned notes for
[translation]
and [rotation] |
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Solving for 2D rot, trans, and scale using
[complex numbers] |
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Collins |
Background Material on PCA (Feb 11)
[slides]
[6 per page] |
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Collins |
Active Shape Models (Feb 16)
[slides]
[6 per page] |
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| Graphical Models |
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Lecture Notes |
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Collins |
Intro to Graphical Models (Feb 23,25)
[slides]
[6 per page] |
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Collins |
Hidden Markov Models (Mar 2)
[slides]
[6 per page] |
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Collins |
Kalman Filter derivation (Mar 4)
[scanned notes]
These are lecture notes from my tracking course.
Don't freak out when you see a reference to "homework" on the very first page! |
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Reference Material |
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PRML Graphical Models Chapter, Bishop, PRML book |
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HMM Reading:
A Tutorial on Hidden Markov Models, Rabiner |
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Research Papers / Oral Presentations |
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Tuesday, Mar 16 |
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Khoa |
A Hidden Markov Model Framework for Video Segmentation Using Audio and Image Features, Boreczky and Wilcox |
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Soumya |
Gait-based Recognition of Humans Using Continuous HMMs,
Kale et.al. |
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Rachana |
Automatic Analysis of Multimodal Group Actions in Meetings,
McCowan et.al. |
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Thursday, Mar 18 |
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Masaru |
Applications of Hidden Markov Chains in Image Analysis, Aas et.al. |
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Nicholas |
Hidden Markov Models for Face Recognition,
Nefian and Hayes |
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earlier paper by Samaria that the above paper refers to |
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Razvan |
Investigating HMMs' Capabilities in 2D Shape Classification , Bicego and Murino |
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Thursday, Mar 25 |
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Critiques Due: written critiques for at least 2 papers we read. |
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| Markov Random Fields (Graphical Models II) |
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Lecture Notes |
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Collins |
Brief Intro to MRF (March 25)
[scanned notes] |
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movie |
Dan Huttenlocher (March 30)
Speeding Up Belief Propagation |
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MRF Research Papers / Oral Presentations |
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Tuesday, Apr 6 |
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Hung-Hsuan |
Interactive Graph Cuts fpr Optimal Boundary and Region Segmentation,
Boykov and Jolly. |
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Anuradha |
Comparison of Graph Cuts with Belief Propagation for Stereo,
Tappen and Freeman. |
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Jeonghyung |
An Application of Markov Random Fields to Range Sensing,
Diebel and Thrun. |
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Thursday, Apr 8 |
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Mi Sun Park |
Learning 3D Scene Structure from a Single Still Image,
Saxena, Sun and Ng. |
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Bharath |
Consistent Segmentation for Optical Flow Estimation,
Zitnick, Jojic and Kang. |
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| Sampling and Markov Chain Monte Carlo |
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Lecture Notes |
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Collins |
Intro to Sampling (Apr 13)
[slides]
[6 per page] |
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Collins |
Markov Chain Monte Carlo (Apr 15)
[slides]
[6 per page] |
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Research Papers / Oral Presentations |
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Tuesday, Apr 20 |
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Jang Young |
Monte Carlo Localization for Mobile Robots, Dellaert et.al. |
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Wen-Yu |
Sampling Plausible Solutions to Multi-body Constraint Problems , Chenney and Forsyth |
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Eric |
Human Upper Body Pose Estimation in Static Images,
Lee and Cohen |
