Dimensionality reduction in images for appearance-based camera localization
![Thumbnail](/dspace/bitstream/handle/2183/31495/2022_Luengo_Silvia_Dimensionality-reduction-images-appearance-based-camera-localization.pdf.jpg?sequence=5&isAllowed=y)
Use este enlace para citar
http://hdl.handle.net/2183/31495
A non ser que se indique outra cousa, a licenza do ítem descríbese como Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
https://creativecommons.org/licenses/by-nc-sa/4.0/deed.es
Coleccións
Metadatos
Mostrar o rexistro completo do ítemTítulo
Dimensionality reduction in images for appearance-based camera localizationData
2022Cita bibliográfica
Luengo, S., Jaenal, A., Moreno, F.A., Gonzalez-Jimenez, J. (2022) Dimensionality reduction in images for appearance-based camera localization. XLIII Jornadas de Automática: libro de actas, pp. 721-727. https://doi.org/10.17979/spudc.9788497498418.0721
Resumo
[Abstract] 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 technique 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.
Palabras chave
Appearance-based localization
Dimensionality reduction
Manifold learning
Dimensionality reduction
Manifold learning
Versión do editor
Dereitos
Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
https://creativecommons.org/licenses/by-nc-sa/4.0/deed.es
ISBN
978-84-9749-841-8