Optimal Object Categorization under Application Specific Conditions

Steven Puttemans, Toon Goedemé

Abstract

Day-to-day industrial computer vision applications focusing on object detection have the need of robust, fast and accurate object detection techniques. However, current state-of-the-art object categorization techniques only reach about 85% detection rate when performing in the wild detections who try to cope with as much scene and object variation as possible. However several industrial applications show many known characteristics like constant lighting, known camera position, constant background, … giving lead to several constraints on the actual algorithms. With a complete new universal object categorization framework, we want to prove the detection rate of these object categorization algorithms by exploiting the application specific knowledge which can help to reach a robust detector with detection rates of 99.9% or higher. We will use the same constraints to effectively reduce the number of false positive detections. Furthermore we will introduce an innovative active learning system based on this application specific knowledge that will drastically reduce the amount of positive and negative training samples, leading to a shorter and more effective annotation and training phase.

References

  1. Aggarwal, C. C. (2013). Supervised outlier detection. In Outlier Analysis, pages 169-198. Springer.
  2. Ascenzi, M.-G. (2013). Determining orientation of cilia in connective tissue. US Patent 8,345,946.
  3. Bay, H., Tuytelaars, T., and Van Gool, L. (2006). Surf: Speeded up robust features. ECCV, pages 404-417.
  4. Benenson, R., Mathias, M., Timofte, R., and Van Gool, L. (2012). Pedestrian detection at 100 frames per second. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 2903-2910. IEEE.
  5. Benenson, R., Mathias, M., Tuytelaars, T., and Van Gool, L. (2013). Seeking the strongest rigid detector. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.
  6. Cant, R., Langensiepen, C. S., and Rhodes, D. (2013). Fourier texture filtering. In UKSim, pages 123-128.
  7. Conaire, C. O., O'Connor, N. E., Cooke, E., and Smeaton, A. F. (2006). Multispectral object segmentation and retrieval in surveillance video. In Image Processing, 2006 IEEE International Conference on, pages 2381- 2384. IEEE.
  8. De Smedt, F., Van Beeck, K., Tuytelaars, T., and Goedemé, T. (2013). Pedestrian detection at warp speed: Exceeding 500 detections per second. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.
  9. Dollár, P., Belongie, S., and Perona, P. (2010). The fastest pedestrian detector in the west. In BMVC, volume 2-3, page 7.
  10. Dollár, P., Tu, Z., Perona, P., and Belongie, S. (2009). Integral channel features. In BMVC, volume 2-4, page 5.
  11. Felzenszwalb, P., Girshick, R., and McAllester, D. (2010). Cascade object detection with deformable part models. In CVPR, pages 2241-2248.
  12. Freund, Y., Schapire, R., and Abe, N. (1999). A short introduction to boosting. Journal-Japanese Society For Artificial Intelligence, 14(771-780):1612.
  13. Gall, J. and Lempitsky, V. (2013). Class-specific hough forests for object detection. In Decision Forests for Computer Vision and Medical Image Analysis, pages 143-157. Springer.
  14. Hammami, M., Jarraya, S. K., and Ben-Abdallah, H. (2013). On line background modeling for moving object segmentation in dynamic scenes. Multimedia Tools and Applications, pages 1-28.
  15. Hsieh, J., Liao, H., Fan, K., Ko, M., and Hung, Y. (1997). Image registration using a new edge-based approach. CVIU, pages 112-130.
  16. Huang, C., Ai, H., Li, Y., and Lao, S. (2005). Vector boosting for rotation invariant multi-view face detection. In Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, volume 1, pages 446-453. IEEE.
  17. Kapoor, A., Grauman, K., Urtasun, R., and Darrell, T. (2007). Active learning with gaussian processes for object categorization. In Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, pages 1-8. IEEE.
  18. Kurz, G., Gilitschenski, I., Julier, S., and Hanebeck, U. D. (2013). Recursive estimation of orientation based on the bingham distribution. arXiv preprint arXiv:1304.8019.
  19. Leiva-Valenzuela, G. A. and Aguilera, J. M. (2013). Automatic detection of orientation and diseases in blueberries using image analysis to improve their postharvest storage quality. Food Control.
  20. Lewis, J. (1995). Fast normalized cross-correlation. In Vision interface, volume 10, pages 120-123.
  21. Li, X. and Guo, Y. (2013). Adaptive active learning for image classification. In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR).
  22. Mathias, M., Timofte, R., Benenson, R., and Gool, L. (2013). Traffic sign recognition-how far are we from the solution? In Proceedings of IEEE International Joint Conference on Neural Networks.
  23. Mindru, F., Tuytelaars, T., Gool, L., and Moons, T. (2004). Moment invariants for recognition under changing viewpoint and illumination. CVIU, 94:3-27.
  24. Mittal, A., Zisserman, A., and Torr, P. (2011). Hand detection using multiple proposals. BMVC 2011.
  25. Puttemans, S. and Goedemé, T. (2013). How to exploit scene constraints to improve object categorization algorithms for industrial applications? In Proceedings of the international conference on computer vision theory and applications (VISAPP 2013), volume 1, pages 827-830.
  26. Riaz, F., Hassan, A., Rehman, S., and Qamar, U. (2013). Texture classification using rotation-and scale-invariant gabor texture features. IEEE Signal Processing Letters.
  27. Shackelford, A. K. and Davis, C. H. (2003). A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas. Geoscience and Remote Sensing, IEEE Transactions on, 41(10):2354-2363.
  28. Van Beeck, K., Goedemé, T., and Tuytelaars, T. (2012). A warping window approach to real-time vision-based pedestrian detection in a truck's blind spot zone. In ICINCO, volume 2, pages 561-568.
  29. Villamizar, M., Moreno-Noguer, F., Andrade-Cetto, J., and Sanfeliu, A. (2010). Efficient rotation invariant object detection using boosted random ferns. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 1038-1045. IEEE.
  30. Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In CVPR, pages I-511.
  31. Yeh, C.-H., Lin, C.-Y., Muchtar, K., and Kang, L.-W. (2013). Real-time background modeling based on a multi-level texture description. Information Sciences.
  32. Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., and Schirokauer, D. (2006). Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering and Remote Sensing, 72(7):799.
Download


Paper Citation


in Harvard Style

Puttemans S. and Goedemé T. (2014). Optimal Object Categorization under Application Specific Conditions . In Doctoral Consortium - DCVISIGRAPP, (VISIGRAPP 2014) ISBN Not Available, pages 25-34


in Bibtex Style

@conference{dcvisigrapp14,
author={Steven Puttemans and Toon Goedemé},
title={Optimal Object Categorization under Application Specific Conditions},
booktitle={Doctoral Consortium - DCVISIGRAPP, (VISIGRAPP 2014)},
year={2014},
pages={25-34},
publisher={SciTePress},
organization={INSTICC},
doi={},
isbn={Not Available},
}


in EndNote Style

TY - CONF
JO - Doctoral Consortium - DCVISIGRAPP, (VISIGRAPP 2014)
TI - Optimal Object Categorization under Application Specific Conditions
SN - Not Available
AU - Puttemans S.
AU - Goedemé T.
PY - 2014
SP - 25
EP - 34
DO -