2026-06-05T13:04:23Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/249502026-02-03T12:14:45Zcom_10630_2254col_10630_37959

Dimensionality Reduction in images for Appearance-based camera Localization Luengo, Silvia Jaenal, Alberto Moreno-Dueñas, Francisco Ángel González-Jiménez, Antonio Javier Reconocimiento de formas (Informática) - Congresos Visión artificial (Robótica) - Congresos Appearance-based Localization (AL) focuses on estimating the pose of a camera from the information encoded in an image, treated holistically. However, the high-dimensionality of images makes this estimation intractable and some techniques of Dimensionality Reduction (DR) must be applied. The resulting reduced image representation, though, must keep underlying information about the structure of the scene to be able to infer the camera pose. This work explores the problem of DR in the context of AL, and evaluates four popular methods in two simple cases on a synthetic environment: two linear (PCA and MDS) and two non-linear, also known as Manifold Learning methods (LLE and Isomap). The evaluation is carried out in terms of their capability to generate lower-dimensional embeddings that maintain underlying information that is isometric to the camera poses. 2022-09-12T11:24:50Z 2022-09-12T11:24:50Z 2022-09-12 2022-09-07 conference output Silvia Luengo, Alberto Jaenal, Francisco A. Moreno, and Javier Gonzalez-Jimenez, "Dimensionality Reduction in images for Appearance-based camera Localization". XLIII Jornadas de Automática, Logroño, Sep- 2022. https://hdl.handle.net/10630/24950 eng XLIII Jornadas de Automática Logroño, España 7-10 Septiembre 2022 http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional XLIII Jornadas de Automática