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Authors: Henrique Gonçalves 1 ; Gustavo Q. de Vasconcelos 1 ; Paola Rangel 2 ; Murilo Carvalho 3 ; Nathaly Archilha 4 and Thiago Spina 4

Affiliations: 1 Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, SP, Brazil, Institute of Computing, University of Campinas, Campinas, SP and Brazil ; 2 Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, SP, Brazil, Institute of Geosciences, University of Campinas, Campinas, SP and Brazil ; 3 Brazilian Bioscences National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, SP and Brazil ; 4 Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, SP and Brazil

ISBN: 978-989-758-354-4

Keyword(s): Image Foresting Transform, GPU, Watershed, Image Segmentation, Superpixels.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Segmentation and Grouping

Abstract: We propose a GPU-based version of the Image Foresting Transform by Seed Competition (IFT-SC) operator and instantiate it to produce compact watershed-based superpixels (Waterpixels). Superpixels are usually applied as a pre-processing step to reduce the amount of processed data to perform object segmentation. However, recent advances in image acquisition techniques can easily produce 3D images with billions of voxels in roughly 1 second, making the time necessary to compute Waterpixels using the CPU-version of the IFT-SC quickly escalate. We aim to address this fundamental issue, since the efficiency of the entire object segmentation methodology may be hindered by the initial process of estimating superpixels. We demonstrate that our CUDA-based version of the sequential IFT-SC operator can speed up computation by a factor of up to 180x for 2D images, with consistent optimum-path forests without requiring additional CPU post-processing.

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Paper citation in several formats:
Gonçalves, H.; Q. de Vasconcelos, G.; Rangel, P.; Carvalho, M.; Archilha, N. and Vallin Spina, T. (2019). cudaIFT: 180x Faster Image Foresting Transform for Waterpixel Estimation using CUDA.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP, ISBN 978-989-758-354-4, pages 395-404. DOI: 10.5220/0007402703950404

@conference{visapp19,
author={Henrique M. Gon\c{C}alves. and Gustavo J. Q. de Vasconcelos. and Paola R. R. Rangel. and Murilo Carvalho. and Nathaly L. Archilha. and Vallin Spina, T.},
title={cudaIFT: 180x Faster Image Foresting Transform for Waterpixel Estimation using CUDA},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP,},
year={2019},
pages={395-404},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007402703950404},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP,
TI - cudaIFT: 180x Faster Image Foresting Transform for Waterpixel Estimation using CUDA
SN - 978-989-758-354-4
AU - Gonçalves, H.
AU - Q. de Vasconcelos, G.
AU - Rangel, P.
AU - Carvalho, M.
AU - Archilha, N.
AU - Vallin Spina, T.
PY - 2019
SP - 395
EP - 404
DO - 10.5220/0007402703950404

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