Course Introduction 

Collins 
Intro to Course (Jan 12)
[slides]
[6 per page] 


Paper Search/Reading/Writing/Vision Resources 

Gaussian Mixture Models and Expectation Maximization 

Lecture Notes 

Collins 
The Gaussian Distribution (Jan 12, 14)
[scanned notes] 

Collins 
GMM and EM, Part 1 (Jan19)
[slides]
[6 per page] 

Collins 
GMM and EM, Part 2 (Jan21,Jan26)
[slides]
[6 per page] 

Incremental Programming Assignments 


Set of EM assignments 


hand in on Feb 9 as part of the EM Project. 


Reference Material 


Intro to Gaussian Distribution , Bishop, PRML book 


Estimating Gaussian Mixture Densities with EM  A Tutorial, Carlo Tomasi. 


Mixture Models and EM , Bishop, PRML book 


Old and New Matrix Algebra Useful for Statistics, Tom Minka. 

Research Papers / Oral Presentations 

Thursday, Jan 28 

Collins 
Statistical Color Models
with Applications to Skin Detection, Jones and Rehg



sample presentation on the Jones and Rehg paper.
[slides]
[6 per page]



sample critique on the Jones and Rehg paper.



I'll also present some recent work done here, using mixtures of Bernoulli distributions.


Tuesday, Feb 2 

Sitichai 
Adaptive Background Mixture Models for Realtime Tracking, Stauffer and Grimson 


related paper of interest:
Understanding Background Mixture Models for Foreground Segmentation, Power and Shoonees 

Jingchen 
Tracking Colour Objects using Adaptive Mixture Models, McKenna et.al. 


Project proposal: 1 page summary of what you are doing for the EM project. 


Thursday, Feb 4 

Vairoj 
Blobworld: Image Segmentation using EM ..., Carson et.al. 

Ankit 
Mixture Models for Optical Flow Computation, Jepson and Black 


Critiques Due: written critiques for at least 2 papers we read. 


Thursday, Feb 18 


Spotlight Presentations: 23 minutes presentation of your EM project. 



Projects Due: code + writeup + spotlight slides. 


EM Project Ideas 


 From Bishop book: use kmeans or EM to label/compress colors in an image.
Try diff values of K. maybe compare kmeans and EM results. Bishop's idea text is
available here



 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].



 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].



 Of course, I encourage you to come up with your own project ideas
involving research or data that is relevant to you.


Procrustes Analysis 

Lecture Notes 

Collins 
Intro to Procrustes Analysis (Feb 9,11)
[slides]
[6 per page] 


Least Squares Approach. scanned notes for
[translation]
and [rotation] 


Solving for 2D rot, trans, and scale using
[complex numbers] 

Collins 
Background Material on PCA (Feb 11)
[slides]
[6 per page] 

Collins 
Active Shape Models (Feb 16)
[slides]
[6 per page] 

Graphical Models 

Lecture Notes 

Collins 
Intro to Graphical Models (Feb 23,25)
[slides]
[6 per page] 

Collins 
Hidden Markov Models (Mar 2)
[slides]
[6 per page] 

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! 

Reference Material 


PRML Graphical Models Chapter, Bishop, PRML book 


HMM Reading:
A Tutorial on Hidden Markov Models, Rabiner 

Research Papers / Oral Presentations 

Tuesday, Mar 16 

Khoa 
A Hidden Markov Model Framework for Video Segmentation Using Audio and Image Features, Boreczky and Wilcox 

Soumya 
Gaitbased Recognition of Humans Using Continuous HMMs,
Kale et.al. 

Rachana 
Automatic Analysis of Multimodal Group Actions in Meetings,
McCowan et.al. 

Thursday, Mar 18 

Masaru 
Applications of Hidden Markov Chains in Image Analysis, Aas et.al. 

Nicholas 
Hidden Markov Models for Face Recognition,
Nefian and Hayes 


earlier paper by Samaria that the above paper refers to 

Razvan 
Investigating HMMs' Capabilities in 2D Shape Classification , Bicego and Murino 

Thursday, Mar 25 


Critiques Due: written critiques for at least 2 papers we read. 


Markov Random Fields (Graphical Models II) 

Lecture Notes 

Collins 
Brief Intro to MRF (March 25)
[scanned notes] 

movie 
Dan Huttenlocher (March 30)
Speeding Up Belief Propagation 

MRF Research Papers / Oral Presentations 

Tuesday, Apr 6 

HungHsuan 
Interactive Graph Cuts fpr Optimal Boundary and Region Segmentation,
Boykov and Jolly. 

Anuradha 
Comparison of Graph Cuts with Belief Propagation for Stereo,
Tappen and Freeman. 

Jeonghyung 
An Application of Markov Random Fields to Range Sensing,
Diebel and Thrun. 

Thursday, Apr 8 

Mi Sun Park 
Learning 3D Scene Structure from a Single Still Image,
Saxena, Sun and Ng. 

Bharath 
Consistent Segmentation for Optical Flow Estimation,
Zitnick, Jojic and Kang. 



Sampling and Markov Chain Monte Carlo 

Lecture Notes 

Collins 
Intro to Sampling (Apr 13)
[slides]
[6 per page] 

Collins 
Markov Chain Monte Carlo (Apr 15)
[slides]
[6 per page] 

Research Papers / Oral Presentations 

Tuesday, Apr 20 

Jang Young 
Monte Carlo Localization for Mobile Robots, Dellaert et.al. 

WenYu 
Sampling Plausible Solutions to Multibody Constraint Problems , Chenney and Forsyth 

Eric 
Human Upper Body Pose Estimation in Static Images,
Lee and Cohen 