## Wednesday, December 15, 2010

### CS: Bob's Confusion Coherence, CS Python, Privacy and Compressive Sensing: Nonadaptive Mastermind Algorithms for String and Vector Databases, with Case Studies, Real-time Visual Tracking Using Sparse Representation

Something is awesome in the state of  Denmark, Bob is continuing his smarter conversation with Phil Alejandro and Guan  in
So you have some vectors that are yielding success and others yielding failure, maybe an empirical analysis using some simple tools of machine learning could give some perspective on the type of vectors in the dictionary that are failing OMP, I am sure the Nuit Blanche readers have better ideas.

Miketrumpis has just released a Python implementation of a compressive sensing demo from Justin Romberg's Signal Processing.. From the Readme file:

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Demo of Compressive Sampling in Image Reconstruction
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This code is essentially a Scientific Python port of Justin Romberg's Compressive Sampling (CS) demo, which accompanied his publication

J. Romberg, "Imaging via Compressive Sampling," Signal Processing Magazine, March 2008.

http://users.ece.gatech.edu/~justin/spmag/


The $\ell_1$ tracker obtains robustness by seeking a sparse representation of the tracking object via $\ell_1$ norm minimization \cite{Xue_ICCV_09_Track}. However, the high computational complexity involved in the $\ell_1$ tracker restricts its further applications in real time processing scenario. Hence we propose a Real Time Compressed Sensing Tracking (RTCST) by exploiting the signal recovery power of Compressed Sensing (CS). Dimensionality reduction and a customized Orthogonal Matching Pursuit (OMP) algorithm are adopted to accelerate the CS tracking. As a result, our algorithm achieves a real-time speed that is up to $6,000$ times faster than that of the $\ell_1$ tracker. Meanwhile, RTCST still produces competitive (sometimes even superior) tracking accuracy comparing to the existing $\ell_1$ tracker. Furthermore, for a stationary camera, a further refined tracker is designed by integrating a CS-based background model (CSBM). This CSBM-equipped tracker coined as RTCST-B, outperforms most state-of-the-arts with respect to both accuracy and robustness. Finally, our experimental results on various video sequences, which are verified by a new metric---Tracking Success Probability (TSP), show the excellence of the proposed algorithms.