Optimization-based Automatic Segmentation of Organic Objects of Similar Types

Enrico Gutzeit, Martin Radolko, Arjan Kuijper, Uwe von Lukas

2015

Abstract

For the segmentation of multiple objects on unknown background in images, some approaches for specific objects exist. However, no approach is general enough to segment an arbitrary group of organic objects of similar type, like wood logs, apples, or tomatoes. Each approach contains restrictions in the object shape, texture, color or in the image background. Many methods are based on probabilistic inference on Markov Random Fields – summarized in this work as optimization based segmentation. In this paper, we address the automatic segmentation of organic objects of similar types by using optimization based methods. Based on the result of object detection, a fore- and background model is created enabling an automatic segmentation of images. Our novel and more general approach for organic objects is a first and important step in a measuring or inspection system. We evaluate and compare our approaches on images with different organic objects on very different backgrounds, which vary in color and texture. We show that the results are very accurate.

References

  1. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., and Sü sstrunk, S. (2012). SLIC Superpixels Compared to State-of-the-Art Superpixel Methods. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34(11):2274-2282.
  2. Akin, C., Kirci, M., Gunes, E. O., and Cakir, Y. (2012). Detection of the pomegranate fruits on tree using image processing. In Agro-Geoinformatics (AgroGeoinformatics), 2012 First International Conference on, pages 1-4.
  3. Aloisio, C., Mishra, R., Chang, C.-Y., and English, J. (2012). Next generation image guided citrus fruit picker. In Technologies for Practical Robot Applications (TePRA), 2012 IEEE International Conference on, pages 37 -41.
  4. Boykov, Y. and Kolmogorov, V. (2004). An experimental comparision of min-cut/max-flow algorithms for energy minimation in vision. In PAMI, pages 1124- 1137.
  5. Chan, T. F. and Vese, L. A. (2001). Active contours without edges. IEEE Transactions on Image Processing, 10(2):266-277.
  6. Dahl, A. B., Guo, M., and Madsen, K. H. (2006). Scalespace and watershed segmentation for detection of wood logs. In Vision Day, Informatics and Mathematical Modelling.
  7. Felzenszwalb, P. F. and Huttenlocher, D. P. (2006). Efficient belief propagation for early vision. Int. J. Comput. Vision, 70(1):41-54.
  8. Gutzeit, E., Ohl, S., Kuijper, A., Voskamp, J., and Urban, B. (2010). Setting graph cut weights for automatic foreground extraction in wood log images. In VISAPP 2010, pages 60-67.
  9. Gutzeit, E. and Voskamp, J. (2012). Automatic segmentation of wood logs by combining detection and segmentation. In ISVC 2012 - 8th International Symposium on Visual Computing, volume LNCS 7431, pages 252-261.
  10. Herbon, C., Tnnies, K. D., and Stock, B. (2014). Detection and segmentation of clustered objects by using iterative classification, segmentation, and gaussian mixture models and application to wood log detection. In GCPR, pages 354-364.
  11. Huang, Q. and Dom, B. (1995). Quantitative methods of evaluating image segmentation. In Image Processing, 1995. Proceedings., International Conference on, volume 3, pages 53 -56 vol.3.
  12. Kass, M., Witkin, A., and Terzopoulos, D. (1988). Snakes: Active Contour Models. International Journal of Computer Vision, 1(4):321-331.
  13. Lee, J.-H., Wu, M.-Y., and Guo, Z.-C. (2010). A tank fish recognition and tracking system using computer vision techniques. In Computer Science and Information Technology (ICCSIT), volume 4, pages 528-532.
  14. Li, S. Z. (1995). Markov random field modeling in computer vision. Springer-Verlag, London, UK, UK.
  15. Medina Rodriguez, P., Fernandez Garcia, E., and Diaz Urrestarazu, A. (1992). Adaptive method for image segmentation based in local feature. Cybernetics and Systems, 23(3-4):299-312.
  16. Otsu, N. (1979). A threshold selection method from graylevel histograms. In IEEE Transactions on Systems, Man and Cybernetics, pages 62-66.
  17. Roth, S. and Black, M. J. (2005). Fields of experts: A framework for learning image priors. In In CVPR, pages 860-867.
  18. Rother, C., Kolmogorov, V., and Blake, A. (2004). Grabcut - interactive forground extraction using iterated graph cuts. In ACM Transactions on Graphics, pages 309- 314. ACM Press.
  19. Rui, G., Gang, L., and Yongsheng, S. (2010). A recognition method of apples based on texture features and em algorithm. In World Automation Congress (WAC), 2010, pages 225 -229.
  20. Sezgin, M. and Sankur, B. (2004). Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 13(1):146- 168.
  21. Shi, J. and Malik, J. (2000). Normalized cuts and image segmentation. In IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 888-905.
  22. Viola, P. A. and Jones, M. J. (2001). Rapid object detection using a boosted cascade of simple features. In CVPR (1), pages 511-518.
  23. White, D., Svellingen, C., and Strachan, N. (2006). Automated measurement of species and length of fish by computer vision. Fisheries Research, 80(2-3):203- 210.
  24. Wijethunga, P., Samarasinghe, S., Kulasiri, D., and Woodhead, I. (2008). Digital image analysis based automated kiwifruit counting technique. In Image and Vision Computing New Zealand, pages 1 -6.
  25. Zhang, X., Yang, Y.-H., Han, Z., Wang, H., and Gao, C. (2013). Object class detection: A survey. ACM Comput. Surv., 46(1):10:1-10:53.
  26. Zhao, J., Tow, J., and Katupitiya, J. (2005). On-tree fruit recognition using texture properties and color data. In Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on, pages 263 - 268.
  27. Zhu, L.-M., Zhang, Y.-L., Zhang, W., Tao, Z.-C., and Liu, C.-F. (2012). Fish motion tracking based on rgb color space and interframe global nerest neighbour. In Automatic Control and Artificial Intelligence (ACAI 2012), International Conference on, pages 1061-1064.
Download


Paper Citation


in Harvard Style

Gutzeit E., Radolko M., Kuijper A. and von Lukas U. (2015). Optimization-based Automatic Segmentation of Organic Objects of Similar Types . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 591-598. DOI: 10.5220/0005314905910598


in Bibtex Style

@conference{visapp15,
author={Enrico Gutzeit and Martin Radolko and Arjan Kuijper and Uwe von Lukas},
title={Optimization-based Automatic Segmentation of Organic Objects of Similar Types},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={591-598},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005314905910598},
isbn={978-989-758-089-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Optimization-based Automatic Segmentation of Organic Objects of Similar Types
SN - 978-989-758-089-5
AU - Gutzeit E.
AU - Radolko M.
AU - Kuijper A.
AU - von Lukas U.
PY - 2015
SP - 591
EP - 598
DO - 10.5220/0005314905910598