References

1. Hopfinger, J.B., Buonocore, M.H. and Mangun, G.R. (2000) The neural mechanisms of top-down attentional control. Nature Neuroscience, 3, 284–291.

2. Corbetta, M., Kincade, J.M. Ollinger, J.M. et al.(2000) Voluntary orienting is dissociated from target detection in human posterior parietal cortex. Nature Neuroscience, 3, 292–297.

3. Corbetta, M. and Shulman, G.L. (2002) Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews, 3 (3), 201–215.

4. Guigon, E., Garndguilume, P. Otto, I. et al.(1994) Neural network models of cortical function based on the computational properties of the cerebral cortex. Journal of physiology, B8, 291–308.

5. Rybak, I.A., Gusakova, V.I. Golovan, A.V. et al.(1998) A model of attention-guided visual perception and recognition. Vision Research, 38, 2387–2400.

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

7. Tsotsos, J.K., Culhane, S.M. Wai, W.Y.K. et al.(1995) Modeling visual attention via selective tuning. Artificial Intelligence, 78 (1–2), 507–545.

8. Milanese, R., Wechsler, H. Gil, S. et al.(1994) Integration of bottom-up and top-down cues for visual attention using non-linear relaxation. IEEE Proceedings of conference on Computer Vision and Pattern Recognition, pp. 781–785.

9. Tagare, H., Toyama, K. and Wang, J.G. (2001) A maximum-likelihood strategy for directing attention during visual search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, 490–500.

10. Van de Laar, P., Heskes, T. and Gielen, S. (1997) Task-dependent learning of attention. Neural Networks, 10, 981–992.

11. Hamker, F.H. (2000) Distributed competition in directed attention. Proceedings in Artificial Intelligence, 9, 39–44.

12. Hamker, F.H. (2005) The emergence of attention by population-based inference and its role in distributed processing and cognitive control of vision. Journal of Computer Vision and Image Understanding, 100 (1–2), 64–106.

13. Sun, Y. and Fisher, R. (2003) Object-based visual attention for computer vision. Artificial Intelligence, 146, 77–123.

14. Navalpakkam, V., Rebesco, J. and Itti, L. (2005) Modeling the influence of task on attention. Vision Research, 45, 205–231.

15. Guo, C.L. and Zhang., L.M. (2007) An attention selection model with visual memory and online learning. Proceedings of IEEE International Joint Conference of Neural Networks (IJCNN2007), pp. 1295–1301.

16. Frintrop, S., Backer, G. and Rome, Erich. (2005) Goal-directed search with a top-down modulated computational attention system, Patten Recognition. Lecture Notes in Computer Science, 3663, 117–124.

17. Frintrop, S. (2006) Vocus: a visual attention system for object detection and goal-directed search, PhD thesis, accepted at the University of Bonn, Germany.

18. Choi, S.-B., Jung, B.-S. Ban, S.-W. et al.(2006) Biologically motivated vergence control system using human-like selective attention model. Neurocomputing, 69 (4–6), 537–558.

19. Kanan, C., Tong, M., Zhang, L. and Cottrell, G. (2009) SUN: top-down saliency using natural statistics. Visual Cognition, 17 (6), 979–1003.

20. Fang, Y., Lin, W., Lau, C. Tong and Lee, B.S. (2011) A visual attention model combining top-down and bottom-up mechanisms for salient object detection. Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2011), pp. 1293–1296.

21. Chelazzi, L.F., Duncan, J., Miller, E.K. and Desimone, R. (1998) Responses of neurons in inferior temporal cortex during memory-guided visual search. Journal of Neurophysiology, 80, 2918–2940.

22. Cohen, J.D., Perlstein, W.M. Braver, T.S. et al.(1997) Temporal dynamics of brain activation during a working memory task. Nature, 386, 604–608.

23. Courtney, S.M., Ungerleider, L.G., Keil, K. and Haxby, J.V. (1997) Transient and sustained activity in a distributed neural system for human working memory. Nature, 386, 608–611.

24. de Fockert, J.W., Rees, G., Frith, C.D. and Lavie, N. (2001) The role of working memory load in selective attention. Science, 291, 1803–1806.

25. Downing, P.E. (2000) Interactions between visual working memory and selective attention. Psychological Science, 11, 467–473.

26. Soto, D., Heinke, D., Humphreys, G.W. and Blanco, M.J. (2005) Early, involuntary top-down guidance of attention from working memory. Journal of Experimental Psychology: Human Perception and Performance, 31 (2), 248–261.

27. Schill, K., Umkehren, E. Beinlich, S. et al.(2001) Scene analysis with saccadic eye movements: top-down and bottom-up modeling. Journal Electronic imaging, 10 (1), 152–160.

28. Hamker, F.H. (2004) a dynamic model of how feature cues guide spatial attention. Vision Research, 44, 501–521.

29. Pouget, A., Dayan, P. and Zemel, R. (2000) Information processing with population codes. Nature Reviews Neuroscience, 1, 125–132.

30. Koechlin, E. and Burnod, Y. (1996) Dual population coding in the neocortex: a model of interaction between representation and attention in the visual cortex. Journal of Cognitive Neuroscience, 8 (4), 353–370.

31. 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, 1254–1262.

32. Greenspan, H., Belongie, S., Perona, P. et al.(1994) Overcomplete steerable pyramid filters and rotation invariance. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 222–228.

33. Hamker, F.H. (2005) The reentry hypothesis: the putative interaction of the frontal eye field, ventrolateral prefrontal cortex, and areas V4m IT for attention and eye movement. Cerebral Cortex, 15 (4), 431–447.

34. Hamker, F.H. (2007) the mechanisms of feature inheritance as predicted by s system-level model of vision attention and decision making. Advances in Cognitive Psychology, 3 (1–2), 111–123.

35. Duncan, J. (1998) Converging levels of analysis in the cognitive neuroscience of visual attention. Philosophical Transactions of the Royal Society of London – Series, B 353, 1307–1317.

36. Desimone, R. and Duncan, J. (1995) Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18, 193–222.

37. Desimone, R. (1998) Visual attention mediated by biased competition in extrastriate visual cortex. Philosophical Transactions of the Royal Society of London – Series, B 353, 1245–1255.

38. Logan, G.D. (1996) The CODE theory of visual attention: an integration of space-based and object-based attention. Psychological Review, 103 (4), 603–649.

39. Driver, J., Davis, G. Russell, C. et al.(2001) Segmentation, attention and phenomenal visual objects. Cognition, 80 (1–2), 61–95.

40. Scholl, B.J. (2001) Objects and attention: the state of the art. Cognition, 80, 1–46.

41. Navalpakkam, V. and Itti, L. (2006) Optimal cue selection strategy. Advances in Neural Information Processing Systems (NIPS*2005), 19, 987–994.

42. Navalpakkam, V. and Itti, L. (2006) An integrated model of top-down and bottom-up attention for optimal object detection. Proceedings: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2, pp. 2049–2056.

43. Itti, L. and Koch, C. (2001) Feature combination strategies for saliency-based visual attention system. Journal of Electronic Imaging, 10 (1), 161–169.

44. Courtney, S.M., Ungerleider, L.G., Keil, K. and Haxby, J.V. (1996) Object and spatial visual working memory activate separate neural system in human cortex. Cerebral Cortex, 6 (1), 39–49.

45. Wilson, F.A.O., Scalaidhe, S.P. and Goldman-Rakic, P.S. (1993) Dissociation of object and spatial processing domains in primate prefrontal cortex. Science, 260, 1955–1958.

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

47. Schill, K., Umkehrer, E. Beinlich, S. et al.(2001) Scene analysis with saccadic eye movements: top-down and bottom-up modeling. Journal Electronic Imaging, 10 (1), 152–160.

48. Lu, Z., Lin, W. Yang, X. et al.(2005) Modeling visual attention's modulatory aftereffects on visual sensitivity and quality evaluation. IEEE Transactions on Image Processing, 14 (11), 1928–1942.

49. Fang, Y., Lin, W., Lau, C. and Lee, B. (2011) A visual attention model combining top-down and bottom-up mechanisms for salient object detection. IEEE International Conference on Acoustics, Speech and Signal Processing.

50. Moores, E., Laiti, L. and Chelazzi, L. (2003) Associative knowledge controls deployment of visual selective attention. Nature Neuroscience, 6 (2), 182–189.

51. Hwang, W.S. and Weng, J. (2000) Incremental hierarchical discriminant regression. IEEE Transaction on Pattern Analysis and Machine Intelligence, 22 (11), 1277–1293.

52. Weng, J., McClelland, J. Pentland, A. et al.(2001) Autonomous mental development by robots and animals. Science, 291 (5504), 599–600.

53. Weng, J. and Hwang, W.S. (2007) Incremental hierarchical discriminant regression. IEEE Transaction on Neural Networks, 18 (2), 397–415.

54. Guo, C.L. and Zhang, L.M. (2007) Attention selection with self-supervised competition neural network and its applications in robot. Lecture Notes in Computer Science, 4491, 727–736.

55. Gold, J.M., Murray, R.F. Sekuler, A.B. et al.(2005) Visual memory decay is deterministic. Psychological Science, 16 (10), 769–774.

56. Hebb, D.O. (1949) The Organization of Behavior, a Neuropsychological Theory, John Wiley, New York.

57. Frintrop, S. (2006) VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search, Springer.

58. Wolfe, J.M., Horowitz, T. Kenner, N. et al.(2004) How fast can you change your mind? The speed of top-down guidance in visual search. Vision Research, 44, 1411–1426.

59. Park, S.J., An, K.H. and Lee, M. (2002) Saliency map model with adaptive masking based on independent component analysis. Neurocomputing, 49, 417–422.

60. Won, W.J., Yeo, J., Ban, S.W. and Lee, M. (2007) Biological motivated incremental object perception based on selective attention. International Journal Pattern Recognition and Artificial Intelligence, 21 (8), 1293–1305.

61. Jeong, S., Ban, S.-W. and Lee, M. (2008) Stereo saliency map considering affective factors and selective motion analysis in a dynamic environment. Neural Networks, 21, 1420–1430.

62. Ban, S., Kim, B. and Lee, M. (2010) Top-down visual selective attention model combined with bottom-up saliency map for incremental object perception. Proceedings of the international Joint Conference on Neural Networks (IJCNN2010), pp. 1–8.

63. Kim, B., Ban, S.-W. and Lee, M. (2011) Growing fuzzy topology adaptive resonance theory models with a push–pull learning algorithm affective saliency map considering psychological distance. Neurocomputing, 74, 646–655.

64. Barlow, H.B. and Tolhurst, D.J. (1992) Why do you have edge detectors? Optical Society of America Technical Digest, 23 (1992), 172.

65. Bell, A.J. and Sejnowski, T.J. (1997) The independent components of natural scenes are edge filters. Vision Research, 37, 3327–3338.

66. Frank, T., Kraiss, K.F. and Kuklen, T. (1998) Comparative analysis of Fuzzy ART and ART-2A network clustering performance. IEEE Transactions on Neural Networks, 9 (3), 544–559.

67. Reisfeld, D., Wolfson, H. and Yeshurun, Y. (1995) Context-free attentional operators:the generalized symmetry transform. International Journal of Computer Vision, 14, 119–130.

68. Fukushima, K. (2005) Use of non-uniform spatial blur for image comparison: Symmetry axis extraction. Neural Networks, 18 (1), 23–32.

69. Grossberg, S. (1987) Competitive learning: From interactive activation to adaptive resonance. Cognitive Science, 11, 23–63.

70. Carpenter, G.A. and Grossberg, S. (1987) ART 2: Self-organization of stable category recognition codes for analog input patterns. Applied Optics, 26 (23), 4919–4930.

71. Zhang, L., Tong, M.H., Marks, T.K. et al.(2008) SUN: A Bayesian framework for saliency using nature statistics. Journal of Vision, 8 (7), 32, 1–20.

72. Chih-Chung, C. and Chih-Jen, L. (2001)LIBSVM: A library for support vector machines [Computer software], Retrieved from http://www.csie.ntu.edu.tw/_cjlin/lissom.

73. Bell, A. and Sejnowski, T. (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7 (6), 1129– 1159.

74. Bell, A. and Sejnowski, T. (1997) The independent components of natural scenes are edge filters. Vision Research, 37 (23), 3327– 3338.

75. Hyvärinen, A. and Oja, E. (1997) A fast fixed-point algorithm for independent component analysis. Neural Computation, 9 (7), 1483–1492.

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