Resourse links

Video talks on mathematical background & general vision topics

 

Stephen Boyd, Lieven Vandenberghe: Convex Optimization, Cambridge University Press, Cambridge, 2004.  (you can view the video lecture from YouTube)

 

If you need some background for graph cut and level set, the following website may be of help

http://www.cs.washington.edu/education/courses/577/04sp/contents.html#BP containing videos of talks by:

1.        R. Zabih : A Selective Overview of Graph Cut Energy Minimization Algorithms

2.        G Sapiro; Level Sets and Partial Differential Equations in Image Sciences

 

If you want to know more about BP (belief propagation):

1. Chapter 8 Graphical Models from Bishop’s Book: Machine Learning and Pattern Recognition http://research.microsoft.com/en-us/um/people/cmbishop/PRML/Bishop-PRML-sample.pdf (more detailed coverage of the basics)
2. Understanding Belief Propagation and its. Generalizations. Jonathan S. Yedidia, William T. Freeman, and Yair Weiss. MERL TR-2001-22. www.merl.com/papers/docs/TR2001-22.pdf
3. Efficient belief propagation for higher-order cliques using linear constraint nodes, CVIU(112), No. 1, October 2008, pp. 39-54.

 

If you want to know more about spectral clustering (upon which the Normalized-Cut technique is based):

1. A tutorial on spectral clustering, Statistics and Computing. Volume 17, Issue 4 (December 2007). Pages: 395 – 416

 

Finally, this website at Mathematical Sciences Research Institute hosts many video talks by experts in the field of computer vision:

http://www.msri.org/calendar/workshops/WorkshopInfo/298/show_workshop

http://www.msri.org/calendar/workshops/WorkshopInfo/270/show_workshop

http://www.msri.org/calendar/workshops/WorkshopInfo/273/show_workshop

 

Maths & SoC Seminars

http://ww1.math.nus.edu.sg/seminars.aspx 

http://www.comp.nus.edu.sg/cs/csseminar.html

 

Resources

 

Gary Bradski, Adrian Kaehler. Learning OpenCV: Computer Vision with the OpenCV Library. 2008.

This book would be most useful to someone who already has a fundamental understanding of computer vision and image processing and wants to see how the open source OpenCV will make their programming tasks easier.

 

OpenCV Library (General purpose and opensource, currently actively maintained and provides C, C++, Python, and Octave interfaces)

Piotr’s Image and Video Toolbox for Matlab

Peter’s Matlab Functions for Computer Vision

 

There is matlab code available for computing optical flow:

http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=17500&objectType=File

 

It is based on the work of Weickert’s group: High Accuracy Optical Flow Estimation Based on a Theory for Warping 

Thomas Brox, Andres Bruhn, Nils Papenberg, and Joachim Weickert

Which is essentially the same as the IJCV2005 paper (When Lucas & Kanade meets Horn &Schunck)

 

The optical flow codes from MIT is also available:

Peter Sand, Seth Teller, “Particle Video: Long-Range Motion Estimation using Point Trajectories,” cvpr, pp. 2195-2202, 2006

http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=17500&objectType=File

 

http://phototour.cs.washington.edu/bundler/

Bundler takes a set of images, image features, and image matches as input, and produces a 3D reconstruction of camera and (sparse) scene geometry as output. The system reconstructs the scene incrementally, a few images at a time, using a modified version of the Sparse Bundle Adjustment package of Lourakis and Argyros as the underlying optimization engine. Bundler has been successfully run on many Internet photo collections, as well as more structured collections.

 

Here are the links that provide numerous motion sequences, some of which have ground truth.

 

http://vasc.ri.cmu.edu/idb/html/motion/index.html

http://www.robots.ox.ac.uk/~vgg/data.html

http://www-cvr.ai.uiuc.edu/ponce_grp/data/

http://vis-www.cs.umass.edu/~vislib/Motion/

http://research.microsoft.com/en-us/um/people/zhang/Calib/

 

CODES

 

For region segmentation:

http://www.cis.upenn.edu/~jshi/software/ Normalized cut using intervening contours (earliest version as well as a multiscale CVPR2005 version)

http://www.cs.berkeley.edu/~fowlkes/BSE/BSE-1.2/ & http://www.cs.berkeley.edu/~fowlkes/BSE/BSE-1.2/util/  codes for PAMI 2004 paper: D. Martin, C. Fowlkes, J. Malik. “Learning to Detect Natural Image Boundaries Using Local Brightness, Color and Texture Cues”, TPAMI 26 (5) p.530-549

http://www.eecs.berkeley.edu/Research/Projects/CS/vision/stellayu/code.html various normalized codes from the same group (including constrained ncut)

http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench/ edge detection based on brightness/color/texture .

 

http://www.caip.rutgers.edu/riul/research/code.html mean-shift segmentation

 

For C/C++ codes on sparse bundle adjustment, refer to http://www.ics.forth.gr/~lourakis/sba/ by Manolis Lourakis and Antonis Argyros

 

Graph cut optimization: http://www.csd.uwo.ca/faculty/olga/code.html

 

Various Computer vision codes:

http://www.csse.uwa.edu.au/~pk/Research/MatlabFns/

Andrew Zisserman’s MATLAB Functions for Multiple View Geometry

Jean-Yves Bouguet’s MATLAB Calibration Software

Peter Corke’s Machine Vision Toolbox he also has a well regarded Robotics Toolbox.

Libor Masek’s Iris Recognition Code

Epipolar Geometry Toolbox by Gian Luca Mariottini and Domenico Prattichizzo.

MathWorks’ links to sites containing MATLAB vision functions

 

Feature Extraction

• VLFeat (SIFT, MSER, plus fast kmeans, hierarchical kmeans … )
• SIFT Demo by David Lowe (closed-source)
• SURF (Speeded Up Robust Features, original implementation, closed-source)
• OpenSURF (An opensource implementation of SURF)

Machine Learning Algorithms

• LIBSVM (A Library for Support Vector Machines)
• SVMlight (Another popular implementation of SVM)
• shogun (A large-scale ML toolbox, specialized in SVM. It provides unified interface for several popular SVM implementations, and features supports for Multiple Kernel Learning. )

Sparse Coding / Representation

• Sparse modeling software package (General-purpose package for various sparsity-related problems, include Lasso, elastic-net, and sparse dictionary learning. Closed-source)