Software:

Copyright Notice: It is important that you read and understand the copyright of the following software packages as specified in the individual items. The copyright varies with each package due to its contributor(s). The packages should NOT be used for any commercial purposes without direct consent of their author(s). 


Sparse PCA via Augmented Lagrangian Methods


Copyright (c) UC Berkeley, 2011.

http://www.eecs.berkeley.edu/~yang/software/SPCA/SPCA_ALM.zip


Fast l-1 Minimization Algorithms and a Performance Benchmark

Copyright (c) UC Berkeley, 2010.

http://www.eecs.berkeley.edu/~yang/software/l1benchmark/index.html


Distributed Wearable Action Recognition

Copyright (c) UC Berkeley, 2008.

For technical details and licensing information, please see our website:
http://www.eecs.berkeley.edu/~yang/software/WAR/index.html



Robust Face Recognition with Facial Occlusion

Copyright (c) UIUC, 2007.

http://perception.csl.illinois.edu/recognition/Home.html



Texture based Image Segmentation via Lossy Compression

Copyright (c) UC Berkeley, 2007.

The CTM toolbox and the image segmentation benchmarking scripts are free for academic users:
http://www.eecs.berkeley.edu/~yang/software/lossy_segmentation/


 

Generalized Principal Component Analysis


Copyright (c) UIUC, 2005-2007.
  • EM & K-Subspaces: This package uses iterative EM and K-Subspaces methods to estimate multiple subspace structures with all dimensions given.

  • RANSAC on Subspaces: This package consists of two implementations about using ransom sampling techniques to estimate multiple subspaces.
  • GPCA for Subspaces of Different Dimensions (GPCA-Voting): This package contains a variation of the original algebraic solution, which uses a voting scheme to improve the segmentation performance in the presence of noise. The number of subspaces and their dimensions are assumed to be given.
  • Robust GPCA: This package consists of implementations of three robust techinques to robustify GPCA-Voting in the presence of large amounts of outliers. For function details, please read the README file in the package.


   
 

  A Fast Line Segment Detector (MATLAB Toolbox)


The toolbox is free for academic users: [code]