Region Merging Driven by Deep Learning for RGB-D Segmentation and Labeling

Umberto Michieli, Maria Camporese, Andrea Agiollo, Giampaolo Pagnutti, Pietro Zanuttigh

Nicola Conci, Caifeng Shan, Lucio Marcenaro, Jungong Han (eds.)
13th International Conference on Distributed Smart Cameras (ICDSC2019), pages 9:1–9:6
ACM
September 2019

Among the various segmentation techniques, a widely used family of approaches are the ones based on region merging, where an initial oversegmentation is progressively refined by joining segments with similar characteristics. Instead of using deterministic approaches to decide which segments are going to be merged we propose to exploit a convolutional neural network which takes a couple of segments as input and decides whether to join or not the segments. We fitted this idea into an existent iterative semantic segmentation scheme for RGB-D data. We were able to lower the number of free parameters and to greatly speedup the procedure while achieving comparable or even higher results, thus allowing for its usage in free navigation systems. Furthermore, our method could be extended straightforwardly to other fields where region merging strategies are exploited.

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Publication

— status

published

— sort

paper in proceedings

who

— authors

Umberto Michieli    Maria Camporese    Andrea Agiollo    Giampaolo Pagnutti    Pietro Zanuttigh

— editors

Nicola Conci    Caifeng Shan    Lucio Marcenaro    Jungong Han

where & when

— publication date

September 2019

— volume

13th International Conference on Distributed Smart Cameras (ICDSC2019)

— pages

9:1–9:6

— article no.

9

— number of pages

6

BibTeX alignment

— location

Trento, Italy

URL

identifiers

— IRIS

11585/842656

— WoS / ISI

000519116500009

ISBN & ISSN

— print ISBN

978-1-4503-7189-6

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