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Compose a description of how you would implement each of the three best approaches at the hospital. Create a distinct, 100- to 200-word plan for each approach. Identify in each of the three plans if you would use one or more of the following four approaches to managing organizational change:

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I would demo the following paper:

Context-based vision system for place and object recognition, A. Torralba, K. P. Murphy, W. T.

Not all of them, but I'd like to focus on only their global and local image features (based on steerable pyramid + PCA).

Is it OK ?

I'll present 31. Dalal, Triggs, Histograms of Oriented Gradients for Human Detection

I can present:
Pinar Duygulu, Kobus Barnard, Nando de Freitas, and David Forsyth. Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. ECCV 2002.

I see that nobody is demoing "Shape Matching and Object Recognition Using Shape Contexts". I would like to do it.

Edited by author 02-03-2006 07:47 PM
I can demo #37 M. Leordeanu and M. Hebert, A Spectral Technique for Correspondence Problems using Pairwise Constraints, ICCV 2005. I would like to mention though, that I implemented this last semester as part of CV final project (if using it for both is frowned upon, I will chose something else).

Edited by author 02-03-2006 06:42 PM
I can present: #32 Gavrila & Philomin, Real-time Object Detection for Smart Vehicles, ICCV 1999

I can oppose: #55 The Perception of Shading and Reflectance, 1996

I would present the following papers:

1a) #27 Boykov & Jolly, Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in ND Images. ICCV 01
1b) #28 Yin Li; Jian Sun; Chi-Keung Tang; Heung-Yeung Shum, Lazy Snapping, SIGGRAPH 04

These are related papers; 28 seems to be an application of 27 -- hence I will try to present them together.


2) #57 Yair Weiss, Deriving intrinsic images from image sequences, ICCV 2001

Demo:

1) #57 Yair Weiss, Deriving intrinsic images from image sequences, ICCV 2001


I will sign up for opposing as and when more papers come up for being advocated.

I'll demo the #30 Viola Jones stuff and #29 too if necessary.

I said I'd like to oppose 22 and 23 papers. But, it seems that the presentation of 23 paper is cancelled (according to syllabus.)

Then, I'll oppose "Feb.8" papers

20. Jianbo Shi; Malik, J. Normalized cuts and image segmentation.

22. Weiss, Y. Segmentation using eigenvectors: a unifying view.

Edited by author 01-29-2006 02:18 PM
Summarizing my preference list:

1) I would definitely present:
 # 35 Tamara L. Berg, Alexander C. Berg, Jaety Edwards, Michael Maire, Ryan White, Yee Whye Teh, Erik Learned-Miller, David A. Forsyth. Names and Faces.

I can give a short demo for it too.

2) I wish to demo:
# 24 T. F. Cootes, G. J. Edwards, C. J. Taylor, Active Appearance Models, PAMI 2001
I can present some results for it from my research work.

I do not wish to present it though. I am hoping someone will. Its an interesting paper.

3) I also wish to demo:

#53 Ramanan, Forsyth, Zisserman. Strike a Pose: Tracking People by Finding Stylized Poses, CVPR 2005.

I can be tempted into presenting it , if no one feels the urge to do so.

4) I wish to sign up for opposing as and when papers come up.

I will oppose:
47. J. Winn and N. Jojic. LOCUS: Learning Object Classes with Unsupervised Segmentation, Proc. IEEE Intl. Conf. on Computer Vision (ICCV), Beijing 2005.

I'd like to demo either
24. Xiaofeng Ren and Jitendra Malik, Learning a Classification Model for Segmentation. in ICCV '03
or
65. D. D. Lee and H. S. Seung. Learning the parts of objects by non-negative matrix factorization. Nature 401, 788-791 (1999)
but neither was picked by anyone to be presented.
Should I give a brief introduction before I demo?
For 65, is the code available even though there is no link to it and I couldn't find it in the author's webpage?

Edited by author 01-25-2006 09:55 PM
I'd like to oppose

22. Weiss, Y. Segmentation using eigenvectors: a unifying view.

23. Andrew Y. Ng, Michael I. Jordan, Yair Weiss, On Spectral Clustering: Analysis and an algorithm

I'll oppose Dave Thompson on the isomap/lle papers.

I can demo the following (depending on whether anyone wants to present it)

1)#8 T. F. Cootes, G. J. Edwards, C. J. Taylor, Active Appearance Models, PAMI 2001
and/or
2)#37 Ramanan, Forsyth, Zisserman. Strike a Pose: Tracking People by Finding Stylized Poses, CVPR 2005

We need to start finding opponents and demoers for the papers.
In particular, we need an opponent (and perhaps a demo?) for the upcoming Torralba papers.

Looks like Dave will do the Ullman papers and I will give an overview of bag-of-words models. So, we would need an opponent for that.

Edited by author 01-23-2006 01:19 PM
I can present and demo the remaining dimensionality reduction papers:
 63) J. B. Tenenbaum, V. De Silva, and J. C. Langford
 64) Sam Roweis & Lawrence Saul

I'll oppose David Lowe's SIFT paper.

I can present:
Tamara L. Berg, Alexander C. Berg, Jaety Edwards, Michael Maire, Ryan White, Yee Whye Teh, Erik Learned-Miller, David A. Forsyth. Names and Faces.

I am sad to see that #15-16 didn't get picked by anyone.
If there are people without papers (or not enough papers), these are nice (and relatively simple).
I will conver 12-14 myself briefly.

I can also present the following if there is a need to present another one.
David G. Lowe, Object Recognition from Local Scale-Invariant Features, ICCV 1999

I would like to demo:
Jianbo Shi; Malik, J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, Aug. 2000, vol.22, (no.8):888-905.

And oppose:
Belongie, Malik, and Puzicha. Shape Matching and Object Recognition Using Shape Contexts (2002)
and
A Berg, T Berg, J Malik, Shape Matching and Object Recognition using Low Distortion Correspondences, CVPR 2005

Maloha: #60 already taken by Stephan.
Regarding #61, would you be able to also do a tutorial on MRF models? Following Geman&Geman or something?

SungWon: you definitely don't have to do 3 papers. Either 1 big one or 2 small ones is fine. I say, you can safely drop #65.

Seth: if you do Adelson & pentland, why not also do "World of painted polyhedra"? They are quite related.

OK, how are we doing on papers? Do we have enough yet?

Dhiraj,
    #30 was already claimed by Ellie along with #29. A list of who claimed what on this discussion board is available at http://www.cs.cmu.edu/~efros/courses/AP06/...papers_assigned.htm
Although this list is not official yet because it hasn't been approved by Alyosha.

    -Dave

I would like to present on these topics


59 Marshall F Tappen, William T Freeman, Edward H Adelson, Recovering Intrinsic Images from a Single Image. NIPS 2002. (there is also a longer version that was published in the September 2005 issue of IEEE Transactions on Pattern Analysis and Machine Intelligence)
 

60 Hoiem, Efros, Hebert, Geometric Context from a Single Image, ICCV 2005 (code available)
 

61 Ashutosh Saxena, Sung Chung, and Andrew Y. Ng. Learning Depth from Single Monocular Images. NIPS 2005.

I'm interested in presenting:
30. Viola, Jones, Robust Real-time Object Detection (2001)

I’d like to present:
26. D. Comaniciu, P. Meer. Mean Shift: A Robust Approach toward Feature Space Analysis
40. T. F. Cootes, G. J. Edwards, C. J. Taylor, Active Appearance Models
65. D. D. Lee and H. S. Seung. Learning the parts of objects by non-negative matrix factorization

Btw, how many topics can or must a student present? Is there any limitation of the number of presentations?

I would like to present:
(20) Normalized cuts and image segmentation (Jianbo Shi; Malik, J.)
and/or
(22) Weiss, Y. Segmentation using eigenvectors: a unifying view.

I would like to wait to see which papers are going to be presented to decide which demos and/or oppositions I can do.

Also (25) Tu and Zhu, Image Segmentation by Data-Driven Markov Chain Monte Carlo, and stuff about active learning like: "Active Learning For Outdoor Obstacle Detection" (C. Dima and M. Hebert) looks very interesting to me.

Edited by author 01-18-2006 02:10 PM
I'm interested in presenting

35. Belongie, Malik, and Puzicha. Shape Matching and Object Recognition Using Shape Contexts (2002)
36. A Berg, T Berg, J Malik, Shape Matching and Object Recognition using Low Distortion Correspondences, CVPR 2005

It seems to me that two papers are closely related, so I pick out two papers.

I can present #60: D. Hoiem, A.A. Efros, and M. Hebert "Geometric Context from a Single Image" and I can possibly demo it too.

I can present the Schneiderman & Kanade in addition to the Viola & Jones.

If anyone presents the manifold learning papers (Isomap or LLE) I'd like to be the opposition.

I would like to present:

17. Fei-Fei and P. Perona. A Bayesian hierarchical model for learning natural scene categories.
 --- in combination with ---
18. Josef Sivic, Bryan Russell, Alexei A. Efros, Andrew Zisserman, Bill Freeman, Discovering Objects and thier Location in Images

Additionally, I would like to present

25. Tu and Zhu, Image Segmentation by Data-Driven Markov Chain Monte Carlo

I would like to oppose:

30. Viola, Jones, Robust Real-time Object Detection

I would like to demo:
26. D. Comaniciu, P. Meer. Mean Shift: A Robust Approach toward Feature Space Analysis
 --- or ---
23. Andrew Y. Ng, Michael I. Jordan, Yair Weiss, On Spectral Clustering: Analysis and an algorithm

I would like to present Adelson & Pentland, The Perception of Shading and Reflectance, 1996

I would like to present:

A. Torralba, K. P. Murphy, W. T. Freeman and M. A. Rubin, Context-based vision system for place and object recognition, ICCV 2003

This one looks interesting. I'd like to present:
  47. J. Winn and N. Jojic. LOCUS: Learning Object Classes with Unsupervised Segmentation, Proc. IEEE Intl. Conf. on Computer Vision (ICCV), Beijing 2005.

Sure! I was thinking that myself but didn't want to block others from presenting them. :) So I'll present papers 41-43 from the list.

Joseph: Can you do the other two previous Borenstein papers as well? They are all very related, building up on each other, so they would be a good background.

Present:
E. Borenstein, E. Sharon, S. Ullman, Combining Top-Down and Bottom-Up Segmentation, Proceedings IEEE workshop on Perceptual Organization in Computer Vision, IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, June 2004.

Hmmm...
I just realized, this is a bit tricky. What happens if people sign up to oppose/demo a paper which, in the end, doesn't get selected for presentation?

Why don't we do this: only sign up to present for now. You can also signup to demo/oppose papers that have already been signed up for. Sorry for the confusion.

present:
Martin, Fowlkes, Malik, Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(5):530-549, May 2004.

oppose:
Eran Borenstein, Shimon Ullman. Class-Specific, Top-Down Segmentation. ECCV 2002.

demo:
David G. Lowe, Object Recognition from Local Scale-Invariant Features, ICCV 1999.

Sounds good. We should combine that with Shnidermann&Kanade in one day. Ellie, do you want to do that one too?

Alternatively, someone can oppose Viola&Jones and present Shniderman&Kanade as alternative? AFAIK, S&K is actually quite a bit more accurate than V&J, even though it's older. Any takers?

present: Viola, Jones, Robust Real-time Object Detection (2001) Second International Workshop on Statistical and Computational Theories of Vision

oppose:Gavrila & Philomin, Real-time Object Detection for Smart Vehicles, ICCV 1999

demo: Fei-Fei and P. Perona. A Bayesian hierarchical model for learning natural scene categories. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, volume 2, pages 524-531, June 200

Please post what papers you would like to present, oppose, or create a demo for.