unravel the kalman filter concepts?

Question

kalman filter to be applied for image segmentation

Answer 1

Unfortunately, it has been too long since my classwork in control theory for me to answer this properly. Here is a Wikipedia link for your use.

Answer 2

i can not be able to assert that I thoroughly understand your subject actuality, yet I do have a undemanding volume of journey with Kalman filters. If the truly length you've is an accelerometer analyzing, than a Kalman sparkling out probably received't help you that very a lot. The Kalman sparkling out is nice for providing an optimal estimate of a variable contained in the presence of diverse elements of options about that variable. In navigation, as an party, there are inertial inputs, GPS inputs, stellar fixes, etc, each and every and each and every with their personal a priori blunders elements. (E.g. everybody understand that the inertial nav unit has a 5% blunders in acceleration length, and the stellar restoration has a 2% angular blunders.) The Kalman sparkling out can account for the records of the disparate length accuracies to even if provide the optimal estimate of the measured volume. on your case, you've truly one length: acceleration. each and every and each and every length has the same blunders profile. The Kalman sparkling out received't help you. although, because you're processing the options after the very actuality, you could favor to be waiting to surely combine the acceleration readings at each and every and each and every era to get carry of the actually position vs time graph. in case you pick to mushy it, some first or 2d order (alpha or alpha-beta) filters might want to do the trick constructive.