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RGB-D Segmentation of Poultry Entrails

  • Mark Philip PhilipsenEmail author
  • Anders Jørgensen
  • Sergio Escalera
  • Thomas B. Moeslund
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9756)

Abstract

This paper presents an approach for automatic visual inspection of chicken entrails in RGB-D data. The point cloud is first over-segmented into supervoxels based on color, spatial and geometric information. Color, position and texture features are extracted from each of the resulting supervoxels and passed to a Random Forest classifier, which classifies the supervoxels as either belonging to heart, lung, liver or misc. The dataset consists of 150 individual entrails, with 30 of these being reserved for evaluation. Segmentation performance is evaluated on a voxel-by-voxel basis, achieving an average Jaccard index of 61.5 % across the four classes of organs. This is a 5.9 % increase over the 58.1 % achieved with features derived purely from 2D.

Keywords

Point Cloud Random Forest Hyper Spectral Imaging Ultra Violet Conditional Random Field 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Thanks to GUDP for financial support and to Danpo for providing access to their facilities. The work has been partially supported by Spanish project TIN2013-43478-P.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Mark Philip Philipsen
    • 1
    Email author
  • Anders Jørgensen
    • 1
    • 3
  • Sergio Escalera
    • 2
  • Thomas B. Moeslund
    • 1
  1. 1.Visual Analysis of People LaboratoryAalborg UniversityAalborgDenmark
  2. 2.University of Barcelona and Computer Vision CenterBarcelonaSpain
  3. 3.IH FoodCopenhagenDenmark

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