Prof. Shah 是计算机视觉的大牛，尤其是视频中的行为识别，做的很不错，Jingen Liu就是他的学生。

Source Code

This code performs background modeling and foreground estimation in dynamic scenes captured by static cameras. The algorithm implemented has three innovations over existing approaches. First, the correlation in intensities of spatially proximal pixels is exploited by using a nonparametric density estimation method over a joint domain-range representation of image pixels, multimodal spatial uncertainties and complex dependencies between the domain (location) and range (color). The model of the background is implemented as a single probability density, as opposed to individual, independent, pixel-wise distributions.

Second, temporal persistence is used as a detection criterion. Unlike previous approaches to object detection which detect objects by building adaptive models of the background, the foreground is modeled to augment the detection of objects (without explicit tracking) since objects detected in the preceding frame contain substantial evidence for detection in the current frame.

Finally, the background and foreground models are used competitively in a MAP-MRF decision framework, stressing spatial context as a condition of detecting interesting objects and the posterior function is maximized efficiently by finding the minimum cut of a capacitated graph. This method is useful for moving object detection in scenes containing dynamic backgrounds, e.g., fountains, fans, and moving trees, etc. The entry point for background modeling is Main.m.

Yaser Sheikh and Mubarak Shah, Bayesian Modelling of Dyanmic Scenes for Object Detection, IEEE Transactions on PAMI, Vol. 27, Issue 11 (Nov 2005), pp. 1778-1792.

Code formthe following publication:

Ruo Zhang,Ping-Sing Tsai, James Cryer and Mubarak Shah,Shape from Shading: A Survey', IEEE Transactions on PAMI, Volume 21, Number 08, August, 1999, pp 690-706

Source code for the Cryer-Tsai-Shah method for combining shape from shading and stereo depth maps.

Related Publication:  James Cryer, Ping-Sing Tsai and Mubarak Shah.Shape from Shading and Stereo, Pattern Recognition, Volume 28, No. 7, pp 1033-1043, Jul 1995.

Source code for the Tsai-Shah method for shape from shading.

Related Publication:Ping-sing Tsai and Mubarak Shah, Shape From Shading Using Linear Approximation, Technical Report, 1992.  Fundamental Matrix

Computes the fundamental matrix from 8 or more matching points in a stereo pair of images using the normalized 8 point algorithm. The normalized 8 point algorithm given by Hartley and Zisserman is used. To achieve accurate results it is recommended that 12 or more points are used. The code uses the normalise2dpts.m file also provided.

On directions to using the code please refer to the code documentation.

Acknowledgements: The code was provided by Peter Kovesi. http://www.csse.uwa.edu.au/~pk/Research/MatlabFns/

Please note that the code requires OpenCV version 1.0 (April Edition) to be installed on the target system. The package includes sample stereo images together with the correspondence points.

Acknowledgements: The code was provided by Paul Smith. http://www.cs.ucf.edu/~rps43158/index.php

The EDISON system contains the image segmentation/edge preserving filtering algorithm described in the paper Mean shift: A robust approach toward feature space analysis and the edge detection algorithm described in the paper Edge detection with embedded confidence. There is also Matlab interface for the EDISON system at the below link.

Acknowledgements: The source code is also available from Rutgers: http://www.caip.rutgers.edu/riul/research/robust.html

For instructions on using the code please refer to the readme.txt file included in the zip package. Note the code requires OpenCV to be installed on the target system.

Acknowledgements: The code was provided by Alper Yilmaz. http://www.cs.ucf.edu/~yilmaz/

The KDE class is a general matlab class for k-dimensional kernel density estimation. It is written in a mix of matlab ".m" files and MEX/C++ code. Thus, to use it you will need to be able to compile C++ code for Matlab. The kernels supported are Gaussian, Epanetchnikov and Laplacian. There is a detailed instruction about how to use it at http://www.ics.uci.edu/~ihler/code/kde.html

K-Means in Statistics Toolbox (Matlab code)

The goodness of this code is that it provides the options, such as 'distance measure', 'emptyaction', and 'onlinephase'. It is quit slow when dealing with large datasets and sometimes memory will be overflow.

This code uses the JIT acceleration in Matlab, so it is much faster than k-means in the Statistics Toolbox. It is very simple and easy to read.

Acknowledgements: The code was provided by Yi Cao.

This code calls the c code of k-means. It is the fastest one among these three and can deal with large dimensional matrix.Acknowledgements: The code was provided by Mark Everingham. http://www.comp.leeds.ac.uk/me

For instructions on using the code please refer to the readme.txt file included in the zip package. Note the code requires OpenCV  and fltk (an open source window toolkit) to be installed on the target system.

Acknowledgements: The code was provided by Paul Smith. http://www.cs.ucf.edu/~rps43158/index.php

This code includes the basic Lucas Kanade algorithm and Hierarchical LK using pyramids. Please refer to the 'readme' file included in the package for help on using the code. Following is a test sample to demonstrate the use of this code to calculate the optical flow.

Acknowledgements: The code was written by Sohaib Khan. http://www.cs.ucf.edu/~khan/

It provides three methods to calculate optical flow: Lucas Kanade, Horn&Schunck and cross-correlation.

Acknowledgements: The code was written by Piotr Dollar. http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html

This code implements a variation of the paper "High accuracy optical flow using a theory for warping", presented at ECCV 2004. Details of implementation, especially the numerics, have been adapted from the PhD thesis of Dr. Thomas Brox. Extension to different color channels, and the concept of a local smoothing function, has been adopted from the PhD thesis of Dr. Peter Sand.

Acknowledgements: The code was written by Visesh Uday Kumar Chari . http://perception.inrialpes.fr/~chari/myweb/Software/

Please refer to the 'readme' file included in the package for help on using the code. Following is a test sample to demonstrate the use of this code for image registration.

Acknowledgements: The code for image registration along with test samples is provided by Yaser Sheikh. http://www.cs.ucf.edu/~yaser/

VFM performs frame grabbing from any Video for Windows source. On directions to using the code please refer to the code documentation. Acknowledgements: The code was written by Farzad Pezeshkpour, School of Information Systems, University of East Anglia.

Interactive java demo of Williams-Shah snakes algorithm. Code written by Sebastian van Delden

Code for the greedy snake algorithm.

DirectShow tutorial.

This MATLAB code is meant for research purposes only.

There have been various changes made to the code since the initial publication.  Some subtle, some not so subtle.  The most significant change is the use of a tessellation method to calculate the orientation bins.  Our testing has shown improved results; however, currently rotational invariance has not been re-implemented.  Rotational invariance is useful in certain applications, however it is useless in others, for this reason we have focused our time elsewhere.  Another noteable change is the elimination of some points due to lack of descriptive information (multiple gradient orientations).  This is a change which has a flag, and can therefore be turned on or off, however I suggest leaving it on and writing your frontend in such a way that allows 3DSIFT to refuse points, as this too has proven very effective in our testing.

Please see the README file for more detailed and up-to-date information.

Code formthe following publication:

SPatiotemporal REgularity Flow (SPREF) is a new spatiotemporal feature that represents the directions in which a video or an image is regular, i.e., the pixel appearances change the least. It has several application, such as video inpainting and video compression. For more detail, please refer to our project page SPREF section.

Fast Registration of Aerial Image SEquences (FRAISE) is a lightweight OpenCV based software system written in C/C++ to register a sequence of aerial images in near-realtime. A demo test video video sequence and an image sequence with corresponding FRAISE alignment are included.

Acknowledgements: The code was written by Subhabrata Bhattacharya. http://www.cs.ucf.edu/~subh/