Digital Signal Processing


A Study on the Effect of Regularization Matrices in Motion Estimation

Authors: Vania Vieira Estrela, A. M. Coelho, Vania V. Estrela

Inverse problems are very frequent in computer vision and machine learning applications. Since noteworthy hints can be obtained from motion data, it is important to seek more robust models. The advantages of using a more general regularization matrix such as Λ=diag{λ1,…,λK} to robustify motion estimation instead of a single parameter λ (Λ=λI) are investigated and formally stated in this paper, for the optical flow problem. Intuitively, this regularization scheme makes sense, but it is not common to encounter high-quality explanations from the engineering point of view. The study is further confirmed by experimental results and compared to the nonregularized Wiener filter approach. Int J Comput Appl. 2012 August 1; 51(19): 17–24. doi:10.5120/8151-1886

Comments: 21 Pages. Int J Comput Appl. 2012 August 1; 51(19): 17–24. doi:10.5120/8151-1886

Download: PDF

Submission history

[v1] 2017-02-19 04:41:53

Unique-IP document downloads: 10 times is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. will not be responsible for any consequences of actions that result from any form of use of any documents on this website.

Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.

comments powered by Disqus