title: A Study on the Effect of Regularization Matrices in Motion Estimation.
abstract: 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 asΛ=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.
authors.affiliation: Instituto Federal de Educacao, Ciencia e Tecnologia do Sudeste de Minas Gerais (IF Sudeste MG), Av. Dr. José Sebastião da Paixão, s/n°, Lindo Vale CEP: 36180-000, Rio Pomba, MG, Brazil.
authors.forename: Alessandra Martins
authors.initials: AM
authors.lastname: Coelho
authors.forename: Vania V
authors.initials: VV
authors.lastname: Estrela
keywords: Computer Vision; Error Concealment; Image Processing; Inverse Problems; Machine Learning; Motion Detection; Pattern Recognition; Regularization; computer vision; image analysis; inverse problems; machine learning; motion estimation; optical flow