Keypoint detection in RGBD images based on an efficient viewpoint-covariant multiscale representation - Département Image, Données, Signal Accéder directement au contenu
Communication Dans Un Congrès Année : 2016

Keypoint detection in RGBD images based on an efficient viewpoint-covariant multiscale representation

Résumé

Texture+depth (RGBD) images provide information about the geometry of a scene, which could help improve current image matching performance, e.g., in presence of large viewpoint changes. While depth has been mainly used for processing keypoint descriptors, in this paper we focus on the keypoint detection problem. In order to produce a computationally efficient viewpoint-covariant multiscale representation, we design an image smoothing procedure which locally smooths a texture image based on the corresponding depth. This yields an approximated scale space, where we can find keypoints using a multiscale detector approach. Our experiments on both synthetic and real-world images show substantial gains with respect to 2D and other RGBD feature extraction approaches.
Fichier principal
Vignette du fichier
doc.pdf (1.26 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01349025 , version 1 (26-07-2016)

Identifiants

  • HAL Id : hal-01349025 , version 1

Citer

Maxim Karpushin, Giuseppe Valenzise, Frédéric Dufaux. Keypoint detection in RGBD images based on an efficient viewpoint-covariant multiscale representation. European Signal Processing Conference (EUSIPCO 2016), Aug 2016, Budapest, Hungary. ⟨hal-01349025⟩

Relations

132 Consultations
640 Téléchargements

Partager

Gmail Facebook X LinkedIn More