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)


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.


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.



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.


  1. 1.
    Amaral, T., Kyriazakis, I., Mckenna, S.J., Ploetz, T.: Weighted atlas auto-context with application to multiple organ segmentation. In: Proceedings of WACV (2016)Google Scholar
  2. 2.
    Chao, K., Yang, C.C., Kim, M.S., Chan, D.E.: High throughput spectral imaging system for wholesomeness inspection of chicken. Appl. Eng. Agric. 24(4), 475–485 (2008)CrossRefGoogle Scholar
  3. 3.
    Dey, B.P., Chen, Y.R., Hsieh, C., Chan, D.E.: Detection of septicemia in chicken livers by spectroscopy. Poult. Sci. 82(2), 199–206 (2003)CrossRefGoogle Scholar
  4. 4.
    Elmasry, G., Sun, D.W., Allen, P.: Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef. J. Food Eng. 110(1), 127–140 (2012)CrossRefGoogle Scholar
  5. 5.
    Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge Results (VOC 2008) (2008)Google Scholar
  6. 6.
    Huang, H., Liu, L., Ngadi, M.O.: Recent developments in hyperspectral imaging for assessment of food quality and safety. Sensors 14(4), 7248–7276 (2014)CrossRefGoogle Scholar
  7. 7.
    Jørgensen, A., Moeslund, T.B., Jensen, E.M.: Detecting gallbladders in chicken livers using spectral analysis. In: Proceedings of the British Machine Vision Conference 2015. British Machine Vision Association (2015)Google Scholar
  8. 8.
    Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Panagou, E.Z., Papadopoulou, O., Carstensen, J.M., Nychas, G.J.E.: Potential of multispectral imaging technology for rapid and non-destructive determination of the microbiological quality of beef filets during aerobic storage. Int. J. Food Microbiol. 174, 1–11 (2014). CrossRefGoogle Scholar
  10. 10.
    Papon, J., Abramov, A., Schoeler, M., Wtter, F.: Voxel cloud connectivity segmentation - supervoxels for point clouds. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2027–2034, June 2013Google Scholar
  11. 11.
    Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: IEEE International Conference on Robotics and Automation (ICRA 2009), pp. 3212–3217, May 2009Google Scholar
  12. 12.
    Silberman, N., Fergus, R.: Indoor scene segmentation using a structured light sensor. In: Proceedings of the International Conference on Computer Vision - Workshop on 3D Representation and Recognition (2011)Google Scholar
  13. 13.
    Tao, Y., Shao, J., Skeeles, K., Chen, Y.R.: Detection of splenomegaly in poultry carcasses by UV and color imaging. Trans. ASAE 43(2), 469–474 (2000)CrossRefGoogle Scholar
  14. 14.
    Trinderup, C.H., Dahl, A.L., Michael, J., Jensen, K., Conradsen, K.: Utilization of multispectral images for meat color measurements, pp. 43–48 (2013)Google Scholar
  15. 15.
    Wolf, D., Prankl, J., Vincze, M.: Fast semantic segmentation of 3D point clouds using a dense CRF with learned parameters. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 4867–4873, May 2015Google Scholar

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

Personalised recommendations