References

1. Itti, L., Koch, C. and Niebur, E. (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Transaction on Pattern Analysis and Machine Intelligence, 20 (11), 1254–1259.

2. Harel, J., Koch, C. and Perona, P. (2007) Graph-based visual saliency. Advances in Neural Information Processing Systems, 19, 545–552.

3. Hou, X. and Zhang, L. (2007) Saliency detection: a spectral residual approach. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR2007).

4. Guo, C.L., Ma, Q. and Zhang, L.M. (2008) Spatio-temporal saliency detection using phase spectrum of quaternion Fourier transform. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR2008).

5. Bruce, N.D. and Tsotsos, J.K. (2006) Saliency based on information maximization. Advances in Neural Information Processing Systems, 18, 155–162. Also http://www-sop.inria.fr/members/Neil.Bruce/#SOURCE CODE.

6. Itti, L. and Koch, C. (2001) Computational modeling of visual attention. Nature Reviews Neuroscience, 2 (3), 194–203.

7. Liu, T., Sun, J. Zheng, N. et al.(2007) Learning to detect a salient object. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR2007).

8. Gao, D. and Vasconcelos, N. (2007) Bottom-up saliency is a discriminant process. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR2007).

9. Gopalakrishnan, V., Hu, Y. and Rajan, D. (2009) Salient region detection by modeling distributions of color and orientation. IEEE Transactions on Multimedia, 11 (5), 892–905.

10. Achanta, R., Hemami, S., Estrada, F. and Susstrunk, S. (2009) Frequency-tuned salient region detection. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR2009), pp. 1597–1604, and also http://ivrg.epfl.ch/supplementary_material/RK_CVPR09/.

11. Treisman, A. and Gelade, G. (1980) A Feature-Integration theory of attention. Cognitive Psychology, 12 (1), 97–136.

12. Wolfe, J. (1994) Guided search 2.0: a revised model of guided search. Psychonomic Bulletin & Review, 1 (2), 202–238.

13. Meur, O.L., Callet, O.L., Barba, D. and Thoreau, D. (2006) A coherent computational approach to model bottom-up visual attention. IEEE Transaction on Pattern Analysis and Machine Intelligence, 28 (5), 802–817.

14. Healey, C.G. (2009)Perception in visualization, http://www.csc.ncsu.edu/faculty/healey/PP/index.html.

15. Fang, Y., Lin, W. Lee, B.-S. et al.(2011) Bottom-up saliency detection model based on human visual sensitivity and amplitude spectrum. IEEE Transactions on Multimedia, 14 (1), 187–198.

16. Toet, A., Bijl, P., Kooi, F.L. and Valeton, J.M. (1998)A high-resolution image dataset for testing search and detection models, Technical Report TNO-NM-98-A020, TNO Human Factors Research Institute, Soesterberg, The Netherlands.

17. Fawcett, T. (2006) An introduction to ROC analysis. Pattern Recognition Letters, 27, 861–874.

18. Bulling, A., Ward, J.A., Gellersen, H. and Troster, G. (2011) Eye movement analysis for activity recognition using electrooculography. IEEE Transactions On Pattern Analysis and Machine Intelligence, 33 (4), 741–773.

19. Cheng, M., Zhang, G. Mitra, N.J. et al.(2011) Global contrast based salient region detection. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 409–416.

20. Gao, D., Mahadevan, V. and Vasconcelos, N. (2007) The discriminant center-surround hypothesis for bottom-up saliency. Advances in Neural Information Processing Systems, 20, 479–504.

21. Gao, D., Mahadevan, V. and Vasconcelos, N. (2008) On the plausibility of the discriminant center-surround hypothesis for visual saliency. Journal of Vision, 8 (7), 13, 1–18.

22. Parkhurst, D., Law, K. and Niebur, E. (2002) Modeling the role of salience in the allocation of overt visual attention. Vision Research, 42 (1), 107–123.

23. Peters, R., Iyer, A., Itti, L. and Koch, C. (2005) Components of bottom-up gaze allocation in natural images. Vision Research, 45 (8), 2397–2416.

24. Meur, O. and Chevet, J. (2010) Relevance of a feed-forward model of visual attention for goal-oriented and free-viewing tasks. IEEE Transactions on Image Processing, 19 (11), 2801–2813.

25. Itti, L. (2005) Quantifying the contribution of low-level saliency to human eye movement in dynamic scenes. Visual Cognition, 12 (6), 1093–1123.

26. Carmi, R. and Itti, L. (2006) Visual causes versus correlates of attentional selection in dynamic scenes. Vision Research, 46, 4333–4345.

27. Hou, X. and Zhang, L. (2008) Dynamic Visual Attention: Searching for coding length increments. Proceedings of Neural Information Processing System (NIPS 2008), pp. 681–688.

28. Li, Z., Qin, S. and Itti, L. (2011) Visual attention guided bit allocation in video compression Image and vision computing. Image and Vision Computing, 29, 1–14.

29. Bruce, N.D.B. and Tsotsos, J.K. (2009) Saliency, attention, and visual; search: an information theoretic approach. Journal of Vision, 9 (3), 5, 1–2422.

30. Itti, L. and Baldi, P. (2009) Bayesian surprise attracts human attention. Vision Research, 49 (10), 2, 1295–1306.

31. Geisler, W.S. and Chou, K.-L. (1995) Separation of low-level and high level factors in complex tasks: visual search. Psychological Review, 102, 356–378.

32. Toet, A., Kooi, F.L., Bijl, P. and Valeton, J.M. (1998) Visual conspicuity determines human target acquisition performance. Optical Engineering, 37 (7), 1969–1975.

33. Wertheim, A.H. (2010) Visual conspicuity: a new simple standard, its reliability, validity and applicability. Ergonomics, 53 (3), 421–442.

34. Toet, A. and Bijl, P. (2003) Visual conspicuity, in Encyclopedia of Optical Engineering (ed. R.G. Driggers), Marcel Dekker Inc., 2929–2935.

35. Toet, A., Bijl, P. and Valeton, J.M. (2001) Image data set for testing search and detection models. Optical Engineering, 40 (9), 1760–1767.

36. Toet, A. (2011) Computational versus psychophysical bottom-up image saliency: a comparative evaluation study. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33 (11), 2131–2146.

37. Tatler, B.W., Baddeley, R.J. and Gilchrist, I.D. (2005) Visual correlates of fixation selection: effects of scale and time. Vision Research, 45 (5), 643–659.

38. Bruce, N.D.B. and Tsotsos, J.K. (2009) Saliency, attention, and visual search: an information theoretic approach. Journal of Vision, 9 (3), 1–24.

39. Zhang, L., Tong, M.H. Marks., T.K. et al.(2008) SUN: a Bayesian framework for saliency using natural statistics. Journal of Vision, 8 (7), 1–20.

40. Bian, P. and Zhang, L.M. (2009) Biological plausibility of spectral domain approach for spatiotemporal visual saliency. Lecture Notes in Computer Science, 5506, 251–258.

41. Seo, H.J. and Milanfar, P. (2009) Static and space-time visual saliency detection by self-resemblance. Journal of Vision, 9 (12–15), 1–27.

42. Rosin, P.L. (2009) A simple method for detecting salient regions. Pattern Recognition, 42 (11), 2363–2371.

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