Unsupervised TOF Image Segmentation through Spectral Clustering and Region Merging

Time of Flight (TOF) cameras generate two simultaneous images, one of intensity and one of range. This allows to tackle segmentation problems in which the separate use of intensity or range information is not enough to extract objects of interest from the 3D scene. In turn, range information allows...

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Detalles Bibliográficos
Autores principales: Lorenti, Luciano, Giacomantone, Javier, Bria, Oscar N.
Formato: Articulo
Lenguaje:Inglés
Publicado: 2018
Materias:
Ciencias Informáticas
spectral clustering
TOF images
unsupervised image segmentation
agrupamiento espectral
segmentación de imágenes no supervisada
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/70111
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-70111
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
spectral clustering
TOF images
unsupervised image segmentation
agrupamiento espectral
segmentación de imágenes no supervisada
spellingShingle Ciencias Informáticas
spectral clustering
TOF images
unsupervised image segmentation
agrupamiento espectral
segmentación de imágenes no supervisada
Lorenti, Luciano
Giacomantone, Javier
Bria, Oscar N.
Unsupervised TOF Image Segmentation through Spectral Clustering and Region Merging
topic_facet Ciencias Informáticas
spectral clustering
TOF images
unsupervised image segmentation
agrupamiento espectral
segmentación de imágenes no supervisada
description Time of Flight (TOF) cameras generate two simultaneous images, one of intensity and one of range. This allows to tackle segmentation problems in which the separate use of intensity or range information is not enough to extract objects of interest from the 3D scene. In turn, range information allows to obtain a normal vector estimation of each point of the captured surfaces. This article presents a semi-supervised spectral clustering method which combines intensity and range information as well as normal vector orientations to improve segmentation results. The main contribution of this article consists in the use of a statistical region merging as a final step of the segmentation method. The region merging process combines adjacent regions which satisfy a similarity criterion. The performance of the proposed method was evaluated over real images. The use of this final step presents preliminary improvements in the metrics evaluated.
format Articulo
Articulo
author Lorenti, Luciano
Giacomantone, Javier
Bria, Oscar N.
author_facet Lorenti, Luciano
Giacomantone, Javier
Bria, Oscar N.
author_sort Lorenti, Luciano
title Unsupervised TOF Image Segmentation through Spectral Clustering and Region Merging
title_short Unsupervised TOF Image Segmentation through Spectral Clustering and Region Merging
title_full Unsupervised TOF Image Segmentation through Spectral Clustering and Region Merging
title_fullStr Unsupervised TOF Image Segmentation through Spectral Clustering and Region Merging
title_full_unstemmed Unsupervised TOF Image Segmentation through Spectral Clustering and Region Merging
title_sort unsupervised tof image segmentation through spectral clustering and region merging
publishDate 2018
url http://sedici.unlp.edu.ar/handle/10915/70111
work_keys_str_mv AT lorentiluciano unsupervisedtofimagesegmentationthroughspectralclusteringandregionmerging
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AT giacomantonejavier segmentacionnosupervisadadeimagenestofviaclusteringespectralyunionderegiones
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