RT Conference Proceedings
T1 Dimensionality Reduction in images for Appearance-based camera Localization
A1 Luengo, Silvia
A1 Jaenal, Alberto
A1 Moreno-Dueñas, Francisco Ángel
A1 González-Jiménez, Antonio Javier
K1 Reconocimiento de formas (Informática) - Congresos
K1 Visión artificial (Robótica) - Congresos
AB 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.
PB XLIII Jornadas de Automática
YR 2022
FD 2022-09-07
LK https://hdl.handle.net/10630/24950
UL https://hdl.handle.net/10630/24950
LA eng
NO 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.
NO Plan propio UMA, HOUNDBOT (P20 01302), funding by Andalusian Regional Government, and ARPEGGIO (PID2020-117057GB-I00), funded by Spain National Research Agency.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026