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I'm looking for a good resource (book, tutorial, lesson etc.) that explains the usage of Kalman filtering in image processing applications.

I'm aware of the fact that Kalman filtering is an optimal estimator which is a tool not a concept in image processing because of that I didn't find a book that explains the usage of Kalman filtering for real applications (from a technical perspective).

I use OpenCV (Matlab will be fine), so I prefer resources that handle image processing in OpenCV.

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  • $\begingroup$ Hey, this is my old answer from stacoverflow.com about an online course that deals with Monte Carlo localization, Kalman filters and particle filters. The answer there is focused on particle filters, but everything written applies to Kalman filters as well. $\endgroup$ – penelope Feb 11 '14 at 11:18
  • $\begingroup$ Thank you so much for the link. I've seen this online course and it is an amazing course. But, what I'm looking for is the technical details about getting the state vector from an image. $\endgroup$ – CroCo Feb 14 '14 at 2:38
  • $\begingroup$ Does that have to do so much with Kalman filters then, actually? I would think that would be pose estimation (whose results you'll be then using as an input for the Kalman filtering), but I would imagine the steps would be practically independent. Am I wrong? $\endgroup$ – penelope Feb 14 '14 at 9:57
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Here are a couple of examples from the computer vision system toolbox: Using kalman filter for object tracking

For a more in-depth explanation the best book is Multiple Target Tracking with Radar Applications by Samuel Blackman.

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Disclaimer: this information is essentially the same an one I provided in my answer about particle filters on SO

This online course deals with Monte Carlo localization, Kalman fitlers and particle filters.

It is called "Programing a Robotic Car", so not really image processing, but: It is explained in very general way, so the principles explained are easily applicable to any kind of input (e.g. sonic, gps, visual...).

It has examples in python, including "in class tasks" and "homeworks" (various non-programming and programming tasks meant to help you understand and learn how to implement the various filters).

It is taught by a Standford professor if I'm not mistaken, and it's very easy to follow. By the end of the course, you should have all three methods implemented in python and ready to apply to any kind of data, including visual.

Disclamer 2: I assume you mean applications in Computer Vision, since Image processing deals mostly with static images. To estimate something, you would need multiple data, that is a video sequence, which makes it lean more towards CV than IP.

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