Using Hidden Markov Models for staff line removal (in OMR) [w/code]

So lately I'm into Optical Music Recognition (OMR), and a central part of that is doing staff line removal. That is when you get rid of the staff lines that obscure the musical symbols to make recognition much easier. There are a lot of ways to do it, but I'm going to share with you how I did it (fairly easily) with Hidden Markov Models (HMMs), which will also teach us a good lesson on this wonderfully useful approach.

OMR has been around for ages, and if you're interested in learning about it [Fornes 2014] and [Rebelo 2012] are good summary articles.
The matter of Staff Line Removal has occupied dozens of researchers for as long as OMR exists; [Dalitz 2008] give a good overview. Basically the goal is to remove the staff lines that obscure the musical symbols, so they would be easier to recognize.
Screen Shot 2015-01-24 at 10.11.00 PM
But, the staff lines are connected to the symbols, so simply removing them will cut up the symbols and make them hardly recognizable.
So let's see how we could do this with HMMs.
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Extending the hand tracker with snakes and optimizations [w/ code, OpenCV]

I wish to report of a number of tweaks and additions to the hand silhouette tracker I posted a while back. First is the ability for it to "snap" to the object using a simple Active Snake method, another is a more advanced resampling technique (the older tracker always resampled after every frame), and of a number of optimizations to increase the speed (tracker now runs at real-time on a single core).
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Curve tracking with a Heap&Hogg's Particle Filters [w/ code, OpenCV]

Screen Shot 2013-03-15 at 6.18.30 PM I wanna share some code for 2D curve tracking with a particle filter, implementing the body of work of Tony Heap and David Hogg. These guys presented a relatively easy to implement method for tracking deformable curves through space and change in form using a Hierarchical Point Distribution Models (HPDM), which is another elegant way to store shape priors. Granted, it is not perfect, but for a simple 2D shape like a hand it works pretty good, and rather fast as well.
Let's dive in then,
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