Unsupervised TOF Image Segmentation through Spectral Clustering and Region Merging

  • Luciano Lorenti Instituto de Investigacion en Inform´atica (III-LIDI), Facultad de Inform´atica - Universidad Nacional de La Plata - Argentina., La Plata, Buenos Aires, Argentina
  • Javier Giacomantone Instituto de Investigacion en Inform´atica (III-LIDI), Facultad de Inform´atica - Universidad Nacional de La Plata - Argentina., La Plata, Buenos Aires, Argentina
  • Oscar Bria Instituto de Investigación en Inform´atica (III-LIDI), Facultad de Inform´atica - Universidad Nacional de La Plata - Argentina., La Plata, Buenos Aires, Argentina
Keywords: Spectral Clustering, TOF images, Unsupervised image segmentation

Abstract

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.

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Published
2018-10-04
How to Cite
Lorenti, L., Giacomantone, J., & Bria, O. (2018). Unsupervised TOF Image Segmentation through Spectral Clustering and Region Merging. Journal of Computer Science and Technology, 18(02), e11. https://doi.org/10.24215/16666038.18.e11
Section
Original Articles