These questions are in regards to the Kalman filter and the Hough Transform.
What are the Pros and Cons of using each method?
In what situations is it better to prefer one over the other?
Tell me more
×
Quantitative Finance Stack Exchange is a question and answer site for
finance professionals and academics. It's 100% free, no registration required.
|
|
I am not sure they are comparable as they serve for slightly different purposes. In robotics (specifically vision), Hough Transform is used for objects (or shape) detection. This can subsequently be used for objects tracking, but Hough Transform has no prediction phase. On the other hand, Kalman Filter is a two phase algorithm; measure and predict. With dynamic systems it can be used to find/predict "hidden" parameters like velocity and respectively predict the next move that subsequently can be adjusted with the measurement phase. However, Kalman Filter assumes that the system's dynamic is linear, though with some Gaussian noise. What else? Algorithm efficiency with regards to state space: exponential for Hough Transform and quadratic for Kalman Filter. And Kalman Filter (as far as I know) isn't applicable to multi-modal distribution cases. Some useful links http://www.ptgrey.com/newsletters/images/GreShaJas04.pdf a work based on Hough Transform with with few complains: "Currently, the BHT does not include any predictive techniques. While predictive techniques such as Kalman filtering do not respond well to arbitrary motions, they may improve precision" and "The benefits of predictive techniques, such as Kalman and particle filtering, will also be investigated". And closer to finance one http://www.r-bloggers.com/the-kalman-filter-for-financial-time-series/ describing the pros and cons of the Kalman Filters. |
|||
|
