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Global scale integral volumes

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Global scale integral volumes. / Bhattacharya, Sounak; Fan, Lixin; Babahajiani, Pouria; Gabbouj, Moncef.

Computer Vision - ECCV 2016 Workshops, Proceedings. Vol. 9913 Springer International Publishing, 2016. p. 192-204 (Lecture Notes in Computer Science; Vol. 9913).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Bhattacharya, S, Fan, L, Babahajiani, P & Gabbouj, M 2016, Global scale integral volumes. in Computer Vision - ECCV 2016 Workshops, Proceedings. vol. 9913, Lecture Notes in Computer Science, vol. 9913, Springer International Publishing, pp. 192-204, European Conference on Computer Vision, 1/01/00. https://doi.org/10.1007/978-3-319-46604-0_14

APA

Bhattacharya, S., Fan, L., Babahajiani, P., & Gabbouj, M. (2016). Global scale integral volumes. In Computer Vision - ECCV 2016 Workshops, Proceedings (Vol. 9913, pp. 192-204). (Lecture Notes in Computer Science; Vol. 9913). Springer International Publishing. https://doi.org/10.1007/978-3-319-46604-0_14

Vancouver

Bhattacharya S, Fan L, Babahajiani P, Gabbouj M. Global scale integral volumes. In Computer Vision - ECCV 2016 Workshops, Proceedings. Vol. 9913. Springer International Publishing. 2016. p. 192-204. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-46604-0_14

Author

Bhattacharya, Sounak ; Fan, Lixin ; Babahajiani, Pouria ; Gabbouj, Moncef. / Global scale integral volumes. Computer Vision - ECCV 2016 Workshops, Proceedings. Vol. 9913 Springer International Publishing, 2016. pp. 192-204 (Lecture Notes in Computer Science).

Bibtex - Download

@inproceedings{94b5b47d823e419e9f6c68cd9ee35431,
title = "Global scale integral volumes",
abstract = "Integral volume is an important image representation technique, which is useful in many computer vision applications. Processing integral volumes for large scale 3D datasets is challenging due to high memory requirements. The difficulties lie in efficiently computing, storing, querying and updating the integral volume values. In this work, we address the above problems and present a novel solution for processing integral volumes for large scale 3D datasets efficiently. We propose an octree-based method where the worst-case complexity for querying the integral volume of arbitrary regions is O(log n), here n is the number of nodes in the octree. We evaluate our proposed method on multiresolution LiDAR point cloud data. Our work can serve as a tool to fast extract features from large scale 3D datasets, which can be beneficial for computer vision applications.",
keywords = "Integral volume, LiDAR, Octree, Point cloud",
author = "Sounak Bhattacharya and Lixin Fan and Pouria Babahajiani and Moncef Gabbouj",
note = "EXT={"}Babahajiani, Pouria{"}",
year = "2016",
doi = "10.1007/978-3-319-46604-0_14",
language = "English",
isbn = "9783319466033",
volume = "9913",
series = "Lecture Notes in Computer Science",
publisher = "Springer International Publishing",
pages = "192--204",
booktitle = "Computer Vision - ECCV 2016 Workshops, Proceedings",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Global scale integral volumes

AU - Bhattacharya, Sounak

AU - Fan, Lixin

AU - Babahajiani, Pouria

AU - Gabbouj, Moncef

N1 - EXT="Babahajiani, Pouria"

PY - 2016

Y1 - 2016

N2 - Integral volume is an important image representation technique, which is useful in many computer vision applications. Processing integral volumes for large scale 3D datasets is challenging due to high memory requirements. The difficulties lie in efficiently computing, storing, querying and updating the integral volume values. In this work, we address the above problems and present a novel solution for processing integral volumes for large scale 3D datasets efficiently. We propose an octree-based method where the worst-case complexity for querying the integral volume of arbitrary regions is O(log n), here n is the number of nodes in the octree. We evaluate our proposed method on multiresolution LiDAR point cloud data. Our work can serve as a tool to fast extract features from large scale 3D datasets, which can be beneficial for computer vision applications.

AB - Integral volume is an important image representation technique, which is useful in many computer vision applications. Processing integral volumes for large scale 3D datasets is challenging due to high memory requirements. The difficulties lie in efficiently computing, storing, querying and updating the integral volume values. In this work, we address the above problems and present a novel solution for processing integral volumes for large scale 3D datasets efficiently. We propose an octree-based method where the worst-case complexity for querying the integral volume of arbitrary regions is O(log n), here n is the number of nodes in the octree. We evaluate our proposed method on multiresolution LiDAR point cloud data. Our work can serve as a tool to fast extract features from large scale 3D datasets, which can be beneficial for computer vision applications.

KW - Integral volume

KW - LiDAR

KW - Octree

KW - Point cloud

U2 - 10.1007/978-3-319-46604-0_14

DO - 10.1007/978-3-319-46604-0_14

M3 - Conference contribution

SN - 9783319466033

VL - 9913

T3 - Lecture Notes in Computer Science

SP - 192

EP - 204

BT - Computer Vision - ECCV 2016 Workshops, Proceedings

PB - Springer International Publishing

ER -