CAP 6412  - Advanced Computer Vision

Fall 2009
TuTh 3:00PM - 4:15PM
CLI 119 (Classroom building 1, room 119) 

Credit hours: 3
Office  hours: 4:30-5:30 PM Tuesdays, 2:00-3:00 PM Thursdays
Office Location: HEC 247

Instructor: Mubarak Shah, email: shah@cs.ucf.edu

Course Web Page:  http://www.cs.ucf.edu/courses/cap6412/

TA: 

Lectures / Paper List(new!)    Announcements   Potential Papers    Datasets    Assignments    Homeworks

---

Course Goals:

To prepare students for graduate research in computer vision.

Course Description:

Review recent advances in computer vision. 

Required and Optional texts:

No textbook. 

Course Prerequisites:

CAP5415 or consent of instructor. 

Exam and Grading Policy:

Reports                                                  30%
Discussion and Attendance                     20%
Homework                                            10%
Programs/Project                                   40%
No exam

Reports:

Summary
Strengths
Weaknesses
Ideas
Questions

Class Policy:

The University Golden Rules will be observed in this class. Copying or Plagiarism is violation of the Golden Rules.

Some Tips on Reading Research Papers:

1. You have to read the paper several times to understand it. When you read the paper first time, if you do not understand something do not get stuck, keep reading assuming you will figure out that later. When you read it the second time, you will understand much more, and the third time even more ...

2. Try first to get a general idea of the paper: What problem is being solved? What are the main steps? How can I implement the method?, even though I do not understand why each step is performed the way it is performed?

3. Try to relate the method to other methods you know, and conceptually find similarities and differences.

4. In the first reading it may be a good idea to skip the related work, since you do not know all other papers, they will confuse you more.

5. Do not use dictionary to just look up the meaning of technical terms like particle filters, maximum likelihood, they are concepts, dictionaries do not define them. They will tell you literal meanings, which may not be useful.

6. Try to understand each concept in isolation, and then integrate them to understand the whole paper. For instance, the paper on "Feature Integration with adaptive weights in a sequential Monte Carlo Tracker" is quite complex paper at the first look. Because it uses Monte Carlo, particle filter, likelihood etc. But try to understand the gist of it. The paper is about tracking, you know a few tracking methods already. It uses features: color histogram, templates in correlation, shape, etc. You know these features, and you have used them. The probabilities obtained by each features are combined (fused) to achieve tracking. How will you combine the probabilities or confidences of each features: multiply, add, apply threshold and then add ...
Particle filter/condensation method is already available in Intell Open CV library, use it, get some idea how it works, what are the parameters, then go back to read the paper again ... If you keep doing it for one week, you will understand a lot about that paper! Next week you do the second paper, and so on ...

Research Tip in MIT:

mit.research.how.to.html 

Other Useful Links:

• CAP 6412 Advanced Computer Vision (2003, 2005, 2008, 2009)
• CAP 6411 Computer Vision Systems (1999, 2000, 2002, 2004, 2008)
• CAP 5415 Computer Vision (2001, 2002, 2003, 2004)
• Book: Fundamentals of Computer Vision
• UCF Vision Lab

Lecture List:

1- August 25, 27: Lecture 1-3  Presenter: Dr. Mubarak Shah
        - Computing optical flow
        - Pyramids
        - Global Motion Compensation

2- September 2, 4: Lecture 4  Presenter: Jingen Liu
        - Bag of Words Approach

Related papers:

3- September 22: Lecture 5  Presenter: Dr. Mubarak Shah
        - Alignment

Related papers:

Paper List:

1- September 8-10: Object Tracking, Presenter: Dr. Mubarak Shah
Alper Yilmaz, Omar Javed, Mubarak Shah,  "Object Tracking: A Survey", ACM Computing Surveys  2006.

Related papers:

2- September 15: Photo Synthesis, Presenter: Omar Oreifej
Noah Snavely, Steven M. Seitz, Richard Szeliski  "Photo Tourism: Exploring Photo Collections in 3D", SIGGRAPH 2006.

3- September 17 : Human Tracking, Presenter: Subhabrata Bhattacharya
S. Pellegrini, A. Ess, K. Schindler, L. van Gool, "You’ll Never Walk Alone: Modeling Social Behavior for Multi-target Tracking", ICCV 2009.

Related papers:

Dataset: ETH Walking Pedestrians (EWAP)

4- September 22: Alignment, Presenter: Dr. Mubarak Shah

5- September 24: Data Clustering Presenter: Yang Yang
Anil K. Jain, "Data Clustering: 50 Years Beyond K-Means", Pattern Recognition Letters 2009.

Related papers:

6- September 29: Machine Recognition of Human Activities, Presenter: Hakan Boyraz
Pavan Turaga, Rama Chellappa, V. S. Subrahmanian, and Octavian Udrea, "Machine Recognition of Human Activities: A Survey", PAMI 2008.

Related papers:

7- October 1: Geo-spatial Aerial Video Processing, Presenter: Chris Huff
Sameer Agarwal, Noah Snavely, Ian Simon, Steven M. Seitz, Richard Szeliski, "Building Rome in a Day", CVPR 2009.

8- October 6: Image Geolocation, Presenter: Amir Roshan Zamir
Evangelos Kalogerakis, Olga Vesselova, James Hays, Alexei A. Efros, Aaron Hertzmann, "Image Sequence Geolocation with Human Travel Priors", ICCV 2009.

           Related papers:

9- October  8: Support Vector Machines, Presenter:  Adarsh Nagaraja
R. Berwick, "An Idiot’s guide to Support vector machines", 

                     Related papers:

10- October 13: Discussion of  Homework 1: Ramin Mehran

                    Related papers:

11- October 15: Image Classification, Presenter: Kishore Reddy
Oren Boiman, Eli Shechtman, Michal Irani,  "In Defense of Nearest-Neighbor Based Image Classification", CVPR08.

12- October 27: Image Editing, Presenter: Devina Shiwlochan 
Yael Pritch, Eitam Kav-Venaki, Shmuel Peleg,  "Shift-Map Image Editing", ICCV09.

13- October 29: Human Detection, Presenter:  Berkan Solmaz
Xiaoyu Wang, Tony X. Han, Shuicheng Yan,  "An HOG-LBP Human Detector with Partial Occlusion Handling", ICCV09.

                    Related papers:

14- November 3: Video Annotation, Presenter:  Laura Norena
Jenny Yuen, Bryan Russell, Ce Liu, Antonio Torralba,  "LabelMe video: Building a Video Database with Human Annotations", ICCV09.

15- November 5: Data Clustering Presenter:  Maria Villarreal
Lior Shapira, Shai Avidan, Ariel Shamir,  "Mode-Detection via Median-Shift", ICCV09.

16- November 10: Weakly Supervised Clustering, Presenter:  Tyler Gomez
Oncel Tuzel, Fatih Porikli, Peter Meer,  "Kernel Methods for Weakly Supervised Mean Shift Clustering", ICCV09.

17- November 12: Human Computer Interaction, Presenter:  Guang Shu
Tilke Judd, Krista Ehinger, Fredo Durand, Antonio Torralba,  "Learning to Predict Where Humans Look", ICCV09.

18- November 24: Image Enhancement, Presenter:  Pramod Chakrapani
Amit Agrawal and Ramesh Raskar,  "Resolving Objects at Higher Resolution from a Single Motion-blurred Image", CVPR07.

Potential Papers (ICCV09, CVPR 09, ECCV 09, SIGGRAPH 09, PAMI, and ...):