Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2016, Article ID 8182416, 15 pages
http://dx.doi.org/10.1155/2016/8182416
Research Article

Robust Individual-Cell/Object Tracking via PCANet Deep Network in Biomedicine and Computer Vision

1Department of Computer Science and Engineering, Huaqiao University, Xiamen, Fujian Province 361021, China
2School of Information Science and Technology, Xiamen University, China

Received 1 June 2016; Accepted 17 July 2016

Academic Editor: Qin Ma

Copyright © 2016 Bineng Zhong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. X. Chen, X. Zhou, and S. T. C. Wong, “Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 4, pp. 762–766, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. E. Meijering, O. Dzyubachyk, and I. Smal, “Methods for cell and particle tracking,” in Imaging and Spectroscopic Analysis of Living Cells, vol. 504, pp. 183–200, Elsevier, 2012. View at Google Scholar
  3. E. Meijering, O. Dzyubachyk, I. Smal, and W. A. van Cappellen, “Tracking in cell and developmental biology,” Seminars in Cell and Developmental Biology, vol. 20, no. 8, pp. 894–902, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. T. Kanade, Z. Yin, R. Bise et al., “Cell image analysis: Algorithms, system and applications,” in Proceedings of the IEEE Workshop on Applications of Computer Vision (WACV '11), pp. 374–381, Kona, Hawaii, USA, January 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. V. C. Abraham, D. L. Taylor, and J. R. Haskins, “High content screening applied to large-scale cell biology,” Trends in Biotechnology, vol. 22, no. 1, pp. 15–22, 2004. View at Publisher · View at Google Scholar · View at Scopus
  6. A. Yilmaz, O. Javed, and M. Shah, “Object tracking: a survey,” ACM Computing Surveys, vol. 38, no. 4, pp. 1–45, 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. A. W. M. Smeulders, D. M. Chu, R. Cucchiara, S. Calderara, A. Dehghan, and M. Shah, “Visual tracking: an experimental survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 7, pp. 1442–1468, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. X. Li, W. Hu, C. Shen, Z. Zhang, A. Dick, and A. van den Hengel, “A survey of appearance models in visual object tracking,” ACM Transactions on Intelligent Systems and Technology, vol. 4, no. 4, pp. 1–58, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. T. Schroeder, “Long-term single-cell imaging of mammalian stem cells,” Nature Methods, vol. 8, no. 4, pp. S30–S35, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. J. W. Young, J. C. W. Locke, A. Altinok et al., “Measuring single-cell gene expression dynamics in bacteria using fluorescence time-lapse microscopy,” Nature Protocols, vol. 7, no. 1, pp. 80–88, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. Q. Zou, J. Zeng, L. Cao, and R. Ji, “A novel features ranking metric with application to scalable visual and bioinformatics data classification,” Neurocomputing, vol. 173, pp. 346–354, 2016. View at Publisher · View at Google Scholar · View at Scopus
  12. D. A. Ross, J. Lim, R.-S. Lin, and M.-H. Yang, “Incremental learning for robust visual tracking,” International Journal of Computer Vision, vol. 77, no. 1, pp. 125–141, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. J. Gall, A. Yao, N. Razavi, L. Van Gool, and V. Lempitsky, “Hough forests for object detection, tracking, and action recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 11, pp. 2188–2202, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Avidan, “Support vector tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 8, pp. 1064–1072, 2004. View at Publisher · View at Google Scholar · View at Scopus
  15. H. Grabner, C. Leistner, and H. Bischof, “Semi-supervised on-line boosting for robust tracking,” in Proceedings of the 10th European Conference on Computer Vision, Marseille, France, October 2008.
  16. C. Lin, W. Chen, C. Qiu, Y. Wu, S. Krishnan, and Q. Zou, “LibD3C: ensemble classifiers with a clustering and dynamic selection strategy,” Neurocomputing, vol. 123, pp. 424–435, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-based object tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp. 564–577, 2003. View at Publisher · View at Google Scholar · View at Scopus
  18. F. Li, X. Zhou, J. Ma, and S. T. C. Wong, “Multiple nuclei tracking using integer programming for quantitative cancer cell cycle analysis,” IEEE Transactions on Medical Imaging, vol. 29, no. 1, pp. 96–105, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. M. Danelljan, F. S. Khan, M. Felsberg, and J. V. Weijer, “Adaptive color attributes for real-time visual tracking,” in Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '14), pp. 1090–1097, Columbus, Ohio, USA, June 2014. View at Publisher · View at Google Scholar · View at Scopus
  20. H. Grabner and H. Bischof, “On-line boosting and vision,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '06), pp. 260–267, IEEE, New York, NY, USA, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  21. S. Hare, A. Saffari, and P. H. S. Torr, “Struck: structured output tracking with kernels,” in Proceedings of the IEEE International Conference on Computer Vision, pp. 263–270, Barcelona, Spain, November 2011. View at Publisher · View at Google Scholar
  22. R. Yao, Q. Shi, C. Shen, Y. Zhang, and A. Van Den Hengel, “Part-based visual tracking with online latent structural learning,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '13), pp. 2363–2370, Portland, Ore, USA, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. V. Takala and M. Pietikäinen, “Multi-object tracking using color, texture and motion,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '07), pp. 1–7, Minneapolis, Minn, USA, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  24. Y. Lu, T. F. Wu, and S.-C. Zhu, “Online object tracking, learning, and parsing with and-or graphs,” in Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '14), pp. 3462–3469, Columbus, Ohio, USA, June 2014. View at Publisher · View at Google Scholar · View at Scopus
  25. J. Fan, X. Shen, and Y. Wu, “Scribble tracker: a matting-based approach for robust tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 8, pp. 1633–1644, 2012. View at Publisher · View at Google Scholar · View at Scopus
  26. X. Lou, M. Schiegg, and F. A. Hamprecht, “Active structured learning for cell tracking: algorithm, framework, and usability,” IEEE Transactions on Medical Imaging, vol. 33, no. 4, pp. 849–860, 2014. View at Publisher · View at Google Scholar · View at Scopus
  27. G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, vol. 313, no. 5786, pp. 504–507, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  28. G. E. Hinton, L. Deng, D. Yu et al., “Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups,” IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 82–97, 2012. View at Publisher · View at Google Scholar · View at Scopus
  29. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Proceedings of the 26th Annual Conference on Neural Information Processing Systems (NIPS '12), pp. 1097–1105, December 2012. View at Scopus
  30. Y. Bengio, A. Courville, and P. Vincent, “Representation learning: a review and new perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798–1828, 2013. View at Publisher · View at Google Scholar · View at Scopus
  31. J. Donahue, Y. Jia, O. Vinyals et al., “DeCAF: a deep convolutional activation feature for generic visual recognition,” in Proceedings of the 31st International Conference on Machine Learning (ICML '14), pp. 988–996, June 2014. View at Scopus
  32. T. Han Chan, K. Jia, S. H. Gao, J. W. Lu, Z. N. Zeng, and Y. Ma, “PCANet: a simple deep learning baseline for image classification,” IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 5017–5032, 2015. View at Google Scholar
  33. G. Carneiro and J. C. Nascimento, “Combining multiple dynamic models and deep learning architectures for tracking the left ventricle endocardium in ultrasound data,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 11, pp. 2592–2607, 2013. View at Publisher · View at Google Scholar · View at Scopus
  34. N. Y. Wang and D. Y. Yeung, Learning a Deep Compact Image Representation for Visual Tracking, Advances in Neural Information Processing Systems, 2013.
  35. J. Fan, W. Xu, Y. Wu, and Y. Gong, “Human tracking using convolutional neural networks,” IEEE Transactions on Neural Networks, vol. 21, no. 10, pp. 1610–1623, 2010. View at Publisher · View at Google Scholar · View at Scopus
  36. L. J. Wang, W. L. Ouyang, X. G. Wang, and H. C. Lu, “Visual tracking with fully convolutional networks,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV '15), pp. 3119–3127, Santiago, Chile, December 2015. View at Publisher · View at Google Scholar
  37. S. Hong, T. You, S. Kwak, and B. Han, “Online tracking by learning discriminative saliency map with convolutional neural network,” in Proceedings of the 32nd International Conference on Machine Learning (ICML '15), pp. 597–606, 2015.
  38. C. Ma, J. B. Huang, X. K. Yang, and M. H. Yang, “Hierarchical convolutional features for visual tracking,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV '15), pp. 3074–3082, Santiago, Chile, December 2015. View at Publisher · View at Google Scholar
  39. H. S. Nam and B. Y. Han, “Learning multi-domain convolutional neural networks for visual tracking,” http://arxiv.org/abs/1510.07945.
  40. http://www.mitocheck.org/cgi-bin/mtc?action=show_movie;query=243867.
  41. Y. Wu, J. W. Lim, and M.-H. Yang, “Online object tracking: a benchmark,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '13), pp. 2411–2418, IEEE, Portland, Ore, USA, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  42. X. Mei and H. Ling, “Robust visual tracking using L1 minimization,” in Proceedings of the IEEE 12th International Conference on Computer Vision, September 2009.
  43. C. Bao, Y. Wu, H. Ling, and H. Ji, “Real time robust L1 tracker using accelerated proximal gradient approach,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '12), pp. 1830–1837, Providence, RI, USA, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  44. K. H. Zhang, L. Zhang, and M. H. Yang, “Real-time compressive tracking,” in Proceedings of the European Conference on Computer Vision (ECCV '12), Florence, Italy, October 2012.
  45. T. Zhang, S. Liu, N. Ahuja, M.-H. Yang, and B. Ghanem, “Robust visual tracking via consistent low-rank sparse learning,” International Journal of Computer Vision, vol. 111, no. 2, pp. 171–190, 2014. View at Publisher · View at Google Scholar · View at Scopus
  46. X. Jia, H. C. Lu, and M.-H. Yang, “Visual tracking via adaptive structural local sparse appearance model,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '12), pp. 1822–1829, IEEE, Providence, RI, USA, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  47. Z. Zhang and K. H. Wong, “Pyramid-based visual tracking using sparsity represented mean transform,” in Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '14), pp. 1226–1233, Columbus, Ohio, USA, June 2014. View at Publisher · View at Google Scholar · View at Scopus
  48. B. N. Zhong, H. X. Yao, S. Chen, R. R. Ji, T.-J. Chin, and H. Z. Wang, “Visual tracking via weakly supervised learning from multiple imperfect oracles,” Pattern Recognition, vol. 47, no. 3, pp. 1395–1410, 2014. View at Publisher · View at Google Scholar · View at Scopus
  49. Y. Zhou, X. Bai, W. Y. Liu, and L. J. Latecki, “Similarity fusion for visual tracking,” International Journal of Computer Vision, vol. 118, no. 3, pp. 337–363, 2016. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  50. Z. Kalal, K. Mikolajczyk, and J. Matas, “Tracking-learning-detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 7, pp. 1409–1422, 2012. View at Publisher · View at Google Scholar · View at Scopus
  51. B. Babenko, M.-H. Yang, and S. Belongie, “Robust object tracking with online multiple instance learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 8, pp. 1619–1632, 2011. View at Publisher · View at Google Scholar · View at Scopus
  52. J. F. Henriques, R. Caseiro, P. Martins, and J. Batista, “High-speed tracking with kernelized correlation filters,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 3, pp. 583–596, 2015. View at Publisher · View at Google Scholar · View at Scopus
  53. Z. Chen, Z. Hong, and D. Tao, “An experimental survey on correlation filter-based tracking,” http://arxiv.org/abs/1509.05520.
  54. W. M. Zuo, X. H. Wu, L. Lin, L. Zhang, and M.-H. Yang, “Learning support correlation filters for visual tracking,” http://arxiv.org/abs/1601.06032.
  55. X. Lou, U. Koethe, J. Wittbrodt, and F. A. Hamprecht, “Learning to segment dense cell nuclei with shape prior,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '12), pp. 1012–1018, IEEE, Providence, RI, USA, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  56. O. Dzyubachyk, W. A. Van Cappellen, J. Essers, W. J. Niessen, and E. Meijering, “Advanced level-set-based cell tracking in time-lapse fluorescence microscopy,” IEEE Transactions on Medical Imaging, vol. 29, no. 3, pp. 852–867, 2010. View at Publisher · View at Google Scholar · View at Scopus
  57. L. Wang, T. Liu, G. Wang, K. L. Chan, and Q. Yang, “Video tracking using learned hierarchical features,” IEEE Transactions on Image Processing, vol. 24, no. 4, pp. 1424–1435, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  58. H. X. Li, Y. Li, and F. Porikli, “Deeptrack: learning discriminative feature representations by convolutional neural networks for visual tracking,” in Proceedings of the British Machine Vision Conference (BMVC '14), BMVA Press, Nottingham, UK, September 2014.