Computer Vision in a Nutshell

--An 8 Week Reading Group on Selected Topics in Computer Vision



1.       The CVinaNutshell is now part of the PhD monitoring.

2.       May/25: I will be out of town next Wednesday. Nianjun will give a lecture on SVM.



General Information:


Yi Li (yi dot li@nicta) and Xuming He (xuming dot he@nicta)


Level 3, Seminar Room D


Wednesday 3pm-4:30pm+/-30 mins


Computer Vision: A Modern Approach (CVMA)

Computer Vision: Algorithms and Applications (CVAA)

Multiple View Geometry (MVG)

Pattern Classification (PC)

Pattern Recognition and Machine Learning (PRML)


The materials in this syllabus are primarily from David Jacobs course series and Yiannis Aloimonoss seminars taught in the University of Maryland.




Computer vision is a fast growing research area. Each year, hundreds of papers are published in all possible topics in computer vision. The goal of this reading group is to show the big picture of the state of the art of computer vision to the students in NICTA. Due to the limited time, only a number of topics will be covered. Possible topics are:

1.       Camera and Image Formation

2.       Early Vision

3.       Image Segmentation

4.       Face/object Recognition

5.       Image Processing fundamentals

6.       Basic techniques in Computer Vision



Reading Group Requirements:



We will present one topic in each reading group. For each topic, the students will be required to read the designated chapters and up to 2 selected papers. The students are required to write a one page summary of any of the paper.

There will be a grading on the summary.


All assignments must be done independently. You are allowed to discuss with your colleagues and the instructors. It is best to understand the problem by your own, because this reading group is solely for your future.


Once agreed to attend the reading group, you are required to email / talk to the instructors if you are not able to show up.







Required Reading

Background Reading














Image Formation

Early vision

Cognition in psychology

Face recognition (I)


Lecture Notes


Biederman, I. (1987). Recognition--by--components: A theory of human image understanding. Psychological Review, 94(2):115--147.

Sinha et al, ``Face recognition by humans: twenty results all computer vision researchers should know about.


Turk, M. & Pentland, A. (1991). Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3, 71-86.



CVMA: Chap. 4, 5, 8


CVAA: Chap. 2, 14.1, 14.2


PC: Appendix A














Image Processing

Diffusion Process

Filter Banks

Noise Removal


Lecture Notes



L. Alvarez, P. Lions, and J.M. Morel. Image selective smoothing and edge detection by nonlinear diffusion II. SIAM J. of Numerical Analysis


Active contour without edges, Chan, T.F.; Vese, L.A., IEEE Transactions on Image Processing, 10 (2), Feb. 2001


Blind motion deblurring from a single image using sparse approximation,

J. Cai, H. Ji, C. Liu and Z. Shen, CVPR09.



CVMA: Chap. 6, 7, 8, 9


CVAA: Chap. 3


Advanced: Fundamentals of Digital Image Processing, Anil. K. Jain (2 copies available from YL)






















Support vector machine



Martin Szummer, Pushmeet Kohli, Derek Hoiem Learning CRFs using Graph Cuts.In: ECCV 2008.

Catalin Ionescu, Liefeng Bo, Cristian Sminchisescu: Structural SVM for visual localization and continuous state estimation. ICCV 2009: 1157-1164

M. B. Blaschko and C. H. Lampert. Learning to localize objects with structured output regression. In ECCV, pages 215, 2008.

The chapters of Statistical Pattern Recognition for SVM by Andrew Webb.
I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun, Large Margin 
Methods for Structured and Interdependent Output Variables, JMLR 
T. Joachims, T. Finley, Chun-Nam Yu, Cutting-Plane Training of Structural SVMs, Machine Learning 









Optical flow




D. Lowe: Three-Dimensional Object Recognition from Single Two-Dimensional Images. Artificial Intelligence, 1987.


Mikolajczyk and Schmid, A Performance Evaluation of Local Descriptors, PAMI October 2005 pp. 1615-1630


CVMA: Chap. 4, 6, 8







Face/Person detection

Face/Object recognition



Paul Viola, Michael Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features," cvpr, vol. 1, pp.511, 2001


Pedro F. Felzenszwalb and Daniel P. Huttenlocher. Pictorial Structures for Object Recognition. IJCV


Stochastic grammar models







Human pose estimation

Human in street


Sidenbladh, H., Black, M. J., and Fleet, D.J., Stochastic tracking of 3D human figures using 2D image motion, ECCV 2002


Saul and Roweis: Think Globally, Fit Locally, Unsupervised Learning of Nonlinear Manifolds









Scene understanding

Image segmentation

Normalized Cut / Graph cut

Markov random field

J Shi and Jitendra Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence , 22(8):888-905, August 2000.


A. Blake, C. Rother, M. Brown, P. Perez, and P. Torr. Interactive image segmentation using an adaptive GMMRF model. Proc. Eur. Conf. on Computer Vision, ECCV (2004)


Efficient Graph-Based Image Segmentation

Pedro F. Felzenszwalb and Daniel P. Huttenlocher. International Journal of Computer Vision, Volume 59, Number 2, September 2004


Scene understanding papers.