research-article

Point cloud capture and segmentation of animal images using classification and clustering

Authors:

Justin Petluk,

Wendy OsbornAuthors Info & Claims

HANIMOB '21: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility

Pages 1 - 4

https://doi.org/10.1145/3486637.3489485

Published: 02 November 2021 Publication History

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Abstract

Measuring characteristics of animals in the wild is not always possible, due to their demeanour and lack of human contact. Remote capture and processing methods, including the segmentation of animal data into relevant body parts, are required. Existing solutions are either costly or too cumbersome to use in the wild. This study explores the use of RGB depth (RGB-D) cameras for data capture of a target animal from a distance. In addition, this study explores the extraction and segmentation of the resulting animal data into point clouds, and the creation of machine learning models for the automated segmentation of this data. Results of this study, including an experimental evaluation, demonstrate the feasibility of utilizing RGB-D cameras for animal data capture, and that classification outperformed clustering for automated animal data segmentation.

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Ossi FHachem FCagnacci FDemšar UDamiani M(2022)HaniMob 2021 Workshop Report: The 1st ACM SIGSPATIAL Workshop on Animal Movement Ecology and Human MobilitySIGSPATIAL Special10.1145/3578484.357849213:3(33-36)Online publication date: 23-Dec-2022
https://dl.acm.org/doi/10.1145/3578484.3578492

Index Terms

Point cloud capture and segmentation of animal images using classification and clustering
1. Applied computing
  1. Law, social and behavioral sciences
    1. Psychology
2. Information systems

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HANIMOB '21: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility

November 2021

53 pages

ISBN:9781450391221

DOI:10.1145/3486637

Editors:
Federico Ossi
Fondazione Edmund Mach, Italy
,
Fatima Hachem
University of Milan, Italy
,
Francesca Cagnacci
Fondazione Edmund Mach, Italy
,
Urška Demšar
University of St Andrews, Scotland, UK
,
Maria Luisa Damiani
University of Milan, Italy

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Published: 02 November 2021

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SIGSPATIAL

SIGSPATIAL '21: 29th International Conference on Advances in Geographic Information Systems

November 2, 2021

Beijing, China

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Ossi FHachem FCagnacci FDemšar UDamiani M(2022)HaniMob 2021 Workshop Report: The 1st ACM SIGSPATIAL Workshop on Animal Movement Ecology and Human MobilitySIGSPATIAL Special10.1145/3578484.357849213:3(33-36)Online publication date: 23-Dec-2022
https://dl.acm.org/doi/10.1145/3578484.3578492

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MRI Brain Images Classification: A Multi-Level Threshold Based Region Optimization Technique

Fully automatic method for segmentation of brain tumor from multimodal magnetic resonance images using wavelet transformation and clustering technique

Segmentation of brain MR images using intuitionistic fuzzy clustering algorithm

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