Interactive multi-label segmentation of RGB-D images
Diebold J, Demmel N, Hazırbaş C, Moeller M, Cremers D (2015)
Publication Type: Conference contribution
Publication year: 2015
Journal
Publisher: Springer Verlag
Book Volume: 9087
Pages Range: 294-306
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Lege-Cap Ferret, FRA
ISBN: 9783319184609
DOI: 10.1007/978-3-319-18461-6_24
Abstract
We propose a novel interactive multi-label RGB-D image segmentation method by extending spatially varying color distributions [14] to additionally utilize depth information in two different ways. On the one hand, we consider the depth image as an additional data channel. On the other hand, we extend the idea of spatially varying color distributions in a plane to volumetrically varying color distributions in 3D. Furthermore, we improve the data fidelity term by locally adapting the influence of nearby scribbles around each pixel. Our approach is implemented for parallel hardware and evaluated on a novel interactive RGB-D image segmentation benchmark with pixel-accurate ground truth.We show that depth information leads to considerably more precise segmentation results. At the same time significantly less user scribbles are required for obtaining the same segmentation accuracy as without using depth clues.
Involved external institutions
Germany (DE)How to cite
APA:
Diebold, J., Demmel, N., Hazırbaş, C., Moeller, M., & Cremers, D. (2015). Interactive multi-label segmentation of RGB-D images. In Mila Nikolova, Jean-François Aujol, Nicolas Papadakis (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 294-306). Lege-Cap Ferret, FRA: Springer Verlag.
MLA:
Diebold, Julia, et al. "Interactive multi-label segmentation of RGB-D images." Proceedings of the 5th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2015, Lege-Cap Ferret, FRA Ed. Mila Nikolova, Jean-François Aujol, Nicolas Papadakis, Springer Verlag, 2015. 294-306.
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