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Journal of Sensors
Volume 2016, Article ID 4265042, 16 pages
http://dx.doi.org/10.1155/2016/4265042
Research Article

Robotic Visual Tracking of Relevant Cues in Underwater Environments with Poor Visibility Conditions

Robotics and Advanced Manufacturing Group, CINVESTAV Campus Saltillo, 25900 Ramos Arizpe, COAH, Mexico

Received 26 March 2016; Revised 18 June 2016; Accepted 28 June 2016

Academic Editor: Youcef Mezouar

Copyright © 2016 Alejandro Maldonado-Ramírez and L. Abril Torres-Méndez. 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. Y. Girdhar, P. Giguère, and G. Dudek, “Autonomous adaptive exploration using realtime online spatiotemporal topic modeling,” International Journal of Robotics Research, vol. 33, no. 4, pp. 645–657, 2014. View at Publisher · View at Google Scholar · View at Scopus
  2. Y. Girdhar and G. Dudek, “Exploring underwater environments with curiosity,” in Proceedings of the 11th Conference on Computer and Robot Vision (CRV '14), pp. 104–110, IEEE, Montreal, Canada, May 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Maldonado-Ramírez and L. A. Torres-Mndez, “Using supercolor pixels descriptors for tracking relevant cues in underwater environments with poor visibility conditions,” in Proceedings of the IEEE Workshop on Visual Place Recognition in Changing Environments (ICRA '15), May 2015.
  4. A. Maldonado-Ramírez, L. Torres-Méndez, and E. Martínez-García, “Robust detection and tracking of regions of interest for autonomous underwater robotic exploration,” in Proceedings of the 6th International Conference on Advanced Cognitive Technologies and Applications, pp. 165–171, Venice, Italy, May 2014.
  5. Y. Y. Schechner and N. Karpel, “Clear underwater vision,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '04), vol. 1, pp. I536–I543, IEEE, Washington, DC, USA, June 2004. View at Publisher · View at Google Scholar · View at Scopus
  6. CIE, Recommendations on Uniform Color Spaces, Color Difference Equations, Psychometric Color Terms, vol. 2, no. 15 (E.-1.3.1), CIE Publication, 1971.
  7. E. Reinhard, M. Ashikhmin, B. Gooch, and P. Shirley, “Color transfer between images,” IEEE Computer Graphics and Applications, vol. 21, no. 5, pp. 34–41, 2001. View at Publisher · View at Google Scholar · View at Scopus
  8. L. F. M. Vieira, E. R. D. Nascimento, F. A. Fernandes Jr., R. L. Carceroni, R. D. Vilela, and A. D. A. Araújo, “Fully automatic coloring of grayscale images,” Image and Vision Computing, vol. 25, no. 1, pp. 50–60, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. G. Bianco, M. Muzzupappa, F. Bruno, R. Garcia, and L. Neumann, “A new color correction method for underwater imaging,” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 40, no. 5, pp. 25–32, 2015. View at Google Scholar
  10. W. James, The Principles of Psychology, 1890.
  11. C. Koch and S. Ullman, “Shifts in selective visual attention: towards the underlying neural circuitry,” in Matters of Intelligence: Conceptual Structures in Cognitive Neuroscience, L. Vaina, Ed., vol. 188 of Synthese Library: Studies in Epistemology, Logic, Methodology, and Philosophy of Science, pp. 115–141, Springer, Amsterdam, The Netherlands, 1987. View at Publisher · View at Google Scholar
  12. L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 11, pp. 1254–1259, 1998. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Frintrop, E. Rome, and H. I. Christensen, “Computational visual attention systems and their cognitive foundations: a survey,” ACM Transactions on Applied Perception, vol. 7, no. 1, article 6, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. C. Harris and M. Stephens, “A combined corner and edge detector,” in Proceedings of the Alvey Vision Conference, vol. 15, p. 50, Manchester, UK, 1988.
  15. H. Bay, T. Tuytelaars, and L. Van Gool, “SURF: speeded up robust features,” in Computer Vision-ECCV 2006, A. Leonardis, H. Bischof, and A. Pinz, Eds., vol. 3951 of Lecture Notes in Computer Science, pp. 404–417, Springer, Berlin, Germany, 2006. View at Google Scholar
  16. D. G. Lowe, “Object recognition from local scale-invariant features,” in Proceedings of the 7th IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157, IEEE, 1999. View at Publisher · View at Google Scholar
  17. R. O. Duda and P. E. Hart, “Use of the Hough transformation to detect lines and curves in pictures,” Communications of the ACM, vol. 15, no. 1, pp. 11–15, 1972. View at Publisher · View at Google Scholar · View at Scopus
  18. C. Akinlar and C. Tonal, “EDCircles: real-time circle detection by Edge Drawing (ED),” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '12), pp. 1309–1312, Kyoto, Japan, March 2012. View at Publisher · View at Google Scholar · View at Scopus
  19. S. Frintrop, G. Backer, and E. Rome, “Goal-directed search with a top-down modulated computational attention system,” in Pattern Recognition, W. Kropatsch, R. Sablatnig, and A. Hanbury, Eds., vol. 3663 of Lecture Notes in Computer Science, pp. 117–124, Springer, Berlin, Germany, 2005. View at Google Scholar
  20. M. Begum and F. Karray, “Visual attention for robotic cognition: a survey,” IEEE Transactions on Autonomous Mental Development, vol. 3, no. 1, pp. 92–105, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. D. Walther, D. R. Edgington, and C. Koch, “Detection and tracking of objects in underwater video,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '04), vol. 1, pp. I-544–I-549, July 2004. View at Scopus
  22. D. Edgington, K. Salamy, M. Risi, R. E. Sherlock, D. Walther, and C. Koch, “Automated event detection in underwater video,” in Proceedings of the in OCEANS 2003, vol. 5, pp. P2749–P2753, San Diego, Calif, USA, September 2003. View at Publisher · View at Google Scholar
  23. C. Barat and M.-J. Rendas, “A robust visual attention system for detecting manufactured objects in underwater video,” in Proceedings of the OCEANS, pp. 1–6, Singapore, May 2006.
  24. P. L. Correia, P. Y. Lau, P. Fonseca, and A. Campos, “Underwater video analysis for norway lobster stock quantification using multiple visual attention features,” in Proceedings of the 15th European Signal Processing Conference, pp. 1764–1768, IEEE, Poznan, Poland, 2007.
  25. S. Frintrop, Vocus: a visual attention system for object detection and goal-directed search [Ph.D. dissertation], Rheinische Friedrich-Wilhelms-Universität, Bonn, Germany, 2006.
  26. S. Palmer, Vision Science, Photons to Phenomenology, The MIT Press, 1999.
  27. R. Adams and L. Bischof, “Seeded region growing,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 6, pp. 641–647, 1994. View at Publisher · View at Google Scholar · View at Scopus
  28. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “Slic superpixels,” Tech. Rep., EPFL, Lausanne, Switzerland, 2010. View at Google Scholar
  29. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “SLIC superpixels compared to state-of-the-art superpixel methods,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 11, pp. 2274–2281, 2012. View at Publisher · View at Google Scholar · View at Scopus
  30. D. Walther and C. Koch, “Modeling attention to salient proto-objects,” Neural Networks, vol. 19, no. 9, pp. 1395–1407, 2006. View at Publisher · View at Google Scholar · View at Scopus
  31. F. G. Rodríguez-Telles, L. A. Torres-Méndez, and E. A. Martínez-García, “A fast floor segmentation algorithm for visual-based robot navigation,” in Proceedings of the 10th International Canadian Conference on Computer and Robot Vision (CRV '13), pp. 167–173, May 2013. View at Publisher · View at Google Scholar · View at Scopus
  32. E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “ORB: an efficient alternative to SIFT or SURF,” in Proceedings of the International Conference on Computer Vision (ICCV '11), pp. 2564–2571, IEEE, Barcelona, Spain, November 2011. View at Publisher · View at Google Scholar · View at Scopus
  33. R. Laganière, OpenCV 2 Computer Vision Application Programming Cookbook: Over 50 Recipes to Master This Library of Programming Functions for Real-Time Computer Vision, Packt Publishing, 2011.
  34. G. Dudek, P. Giguere, J. Zacher et al., “Aqua: an amphibious autonomous robot,” Computer, vol. 40, no. 1, pp. 46–53, 2007. View at Publisher · View at Google Scholar