Article Dans Une Revue IEEE Transactions on Multimedia Année : 2016

Keypoint Detection in RGBD Images Based on an Anisotropic Scale Space

Résumé

The increasing availability of texture+depth (RGBD) content has recently motivated research towards the design of image features able to employ the additional geometrical information provided by depth. Indeed, such features are supposed to provide higher robustness than conventional 2D features in presence of large changes of camera viewpoint. In this paper we consider the first stage of RGBD image matching, i.e., keypoint detection. In order to obtain viewpoint-covariant keypoints, we design a filtering process, which approximates a diffusion process along the surfaces of the scene, by means of the information provided by depth. Next, we employ this multiscale representation to find keypoints through a multiscale keypoint detector. The keypoints obtained by the proposed detector provide substantially higher stability to viewpoint changes than alternative 2D and RGBD feature extraction approaches, both in terms of repeatability and image classification accuracy. Furthermore, the proposed detector can be efficiently implemented on a GPU.

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A comme partie hal-01299841 Objet présenté à une conférence Maxim Karpushin, Giuseppe Valenzise, Frédéric Dufaux. A scale space for texture+depth images based on a discrete Laplacian operator. IEEE International Conference on Multimedia and Expo, Jun 2015, Turin, Italy. ⟨10.1109/ICME.2015.7177500⟩. ⟨hal-01299841⟩

The current paper is an extended version of the referenced one
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Fait partie de tel-01483314 Thèse de doctorat Maxim Karpushin. Local features for RGBD image matching under viewpoint changes. Computer Vision and Pattern Recognition [cs.CV]. Télécom ParisTech, 2016. English. ⟨NNT : ⟩. ⟨tel-01483314⟩

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hal-01348978 , version 1 (29-07-2016)

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Maxim Karpushin, Giuseppe Valenzise, Frédéric Dufaux. Keypoint Detection in RGBD Images Based on an Anisotropic Scale Space. IEEE Transactions on Multimedia, 2016, ⟨10.1109/TMM.2016.2590305⟩. ⟨hal-01348978⟩
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