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Compressive Sensing and Computer Vision

Thanks to Igor Carron for pointing out this video lecture
Compressive Sensing for Computer Vision: Hype vs Hope
It start with comprehensible explanation of what compressive sensing is about (BTW wiki article on compressive sensing is wholly inadequate).
Basically it’s about imagining the lower-dimensional signal(image) as projection by rectangular matrix from mostly zero high-dimensional vector. It happens that this sparse high-dimensional vector can be restored if the matrix is almoste orthonormal (Restricted Isometry Property). Discrete Fourier Transform and random matrices have that property.
This sparse vector could be considered as classification space for original signal. So application of Compressive Sensing to Computer Vision is mostly about classification or recognition. As methods used by CS are convex and linear programming those are not run-time methods, and would not help much in real-time tracking. There is CS-inspired advise at the end of the lecture, about trying to replace L^{2} norm optimization with L^{1} norm. That could be actually helpful in some cases. If L^{1} approximated as iteratively reweighted L^{2} it’s essentially the same as robustification of least square method.

7, December, 2009 Posted by | Coding AR | , , | 3 Comments