1. Beare, J.I. (2007)On sense and the sensible by Aristotle written 350 BCE, Translated (ebook@adeaide).
2. Corbetta, M. (1998) Frontoparietal cortical networks for directing attention and the eye to visual locations: identical, independent or overlapping neural systems? Proceedings of the National Academy of Sciences USA, 95, 831–838.
3. Treisman, A.M. (1977) Focused attention in the perception and retrieval of multidimensional stimuli. Perception & Psychophysics, 22, 1–11.
4. Treisman, A.M. and Gelade, G. (1980) A feature-integration theory of attention. Cognitive Psychology, 12, 97–136.
5. Wolfe, J., Cave, K.R. and Franzel, S.L. (1989) Guided search: an alternative to the feature integration model for visual search. Journal of Experimental Psychology: Human Perception and Performance, 15 (3), 419–433.
6. Hubel, D.H. and Wiesel, T.N. (1959) Receptive fields of single neurons in the cat's striate cortex. The Journal of Physiology, 148, 574–591.
7. Hubel, D.H. and Wiesel, T.N. (1968) Receptive fields and functional architecture of monkey striate cortex. The Journal of Physiology, 195 (1), 215–243.
8. Livingstone, M.S. and Hubel, D.H. (1984) Anatomy and physiology of a color system in the primate visual cortex. Journal of Neuroscience, 4 (1), 309–356.
9. Itti, L. and Baldi, P. (2005) Bayesian surprise attracts human attention. Proceedings of Neural Information Processing Systems (NIPS05), pp. 547–554.
10. Bruce, N.D. (2005) Features that draw visual attention: an information theoretic perspective. Neurocomputing, 65–66, 125–133.
11. 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), 32, 1–20.
12. Gao, D. and Vasconcelos, N. (2004) Discriminant saliency for visual recognition from cluttered scenes. Proceedings of Neural Information Processing Systems (NIPS04), pp. 481–488.
13. Gao, D., Mahadevan, V. and Vasconcelos, N. (2007) The discriminant center-surround hypothesis for bottom-up saliency. Proceedings of Neural Information Processing Systems (NIPS07), pp. 497–504.
14. 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.
15. Kelly, D.H. (1962) Information capacity of a single retinal channel. IEEE Transaction on Information Theory, 8, 221–226.
16. Li, Z. and Atick, J.J. (1994) Towards a theory of the striate cortex. Neural Computation, 6 (1), 127–146.
17. Wandell, B.A. (1999) Computational neuroimaging of human visual cortex. Annual Review of Neuroscience, 22, 145–173.
18. Peters, R.J., Iyer, A., Itti, L. and Koch, C. (2005) Components of bottom-up gaze allocation in natural images. Vision Research, 45, 2397–2416.
19. Sziklai, G. (1956) Some studies in the speed of visual perception. IEEE Transactions on Information Theory, 2 (3), 125–128.
20. Li, Z. and Peter, D. (2006) Pre-attentive visual selection. Neural Networks, 19, 1437–1439.
21. James, W. (1890 /1981) The Principles of Psychology, Harvard University Press, Cambridge, Massachusetts.
22. Itti, L. and Koch, C. (2000) A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research, 40, 1489–1506.
23. Itti, L. and Koch, C. (2001) Computational modeling of visual attention. Neuroscience, 2, 194–203.
24. Yarbus, A.L. (1967) Eyes Movements and Vision (Translated from Russian by Basil Haigh), Plenum Press, New York.
25. Neisser, N. (1967) Cognitive Psychology, Appleton Century Crofts, New York.
26. Hoffman, J.E. (1975) Hierarchical stages in the processing of visual information. Perception & Psychophysics, 18, 348–354.
27. Egeth, H. (1997) Attention and pre-attention, in The Psychology of Learning and Motivation, 11 (ed. G.H. Bower), pp. 277–320, Academic Press, New York.
28. Wolfe, J.M. and Gancarz, G. (1996) Guided search 3.0: A model of visual search Catches up with Jay Enoch 40 years later, in V. basic and Clinical Applications of Vision Science (ed. V. Lakshminarayanan), pp. 189–192, Kluwer Academic, Dordrecht, Netherlands.
29. Desimone, R. and Duncan, J. (1995) Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18, 193–222.
30. Wolfe, J. (1994) Guided Search 2.0: A revised model of Guided Search. Psychonomic Bulletin & Review, 1 (2), 202–238.
31. Hubel, D.H. and Wiesel, T.N. (1962) Receptive field, binocular interaction and functional architecture in the cat's visual cortex. The Journal of Physiology, 160 (1), 106–154.
32. Tootell, R.B.H., Silverman, M.S. Hamilton, S.L. et al.(1988) Functional anatomy of macaque striate cortex. V. Spatial frequency. Journal of neuroscience, 8, 1610–1624.
33. Swindale, N.V., Matsubara, J.A. and Cynader, M.S. (1987) Surface organization of orientation and direction selectivity in cat area 18. Journal of Neuroscience, 7, 1414–1427.
34. Zeki, S.M. (1976) The function organization of projections from striate to prestriate visual cortex in the rhesus monkey. Cold Spring Harbor Symposia on Quantitative Biology, 15, 591–600.
35. Koch, C. and Ullman, S. (1985) Shift in selective visual attention: towards the underlying neural circuitry. Human Neurobiology, 4, 219–227.
36. Navalpakkam, V. and Itti, L. (2006) Top-down attention selection is fine grained. Journal of Vision, 6, 1180–1193.
37. 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.
38. Posner, M.I. (1980) Orienting of attention. Quarterly Journal of Experimental Psychology, 32, 3–25.
39. Rensink, R.A., O'Regan, J.K. and Clark, J.J. (1997) To see or not to see: the need for attention to perceive changes in scenes. Psychological Science, 8, 368–373.
40. Posner, M.I. and Cohen, Y. (1984) Components of visual orienting, in Attention and Performance, 10 (eds H. Bouma and D. Bouwhuis), pp. 531–556, Erlbaum, Hillsdale, NJ.
41. Klein, R.M. (2000) Inhibition of return. Trends in Cognitive Science, 4 (4), 138–147.
42. Posner, M.I., Rafal, R.D., Choate, L.S. and Vaughan, J. (1985) Inhibition of return: neural basis and function. Cognitive Neuropsychology, 2211–2228.
43. Itti, L., Koch, C. and Niebur, E. (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern. Analysis and Machine Intelligence, 20 (11), 1254–1259.
44. Harel, J., Koch, C. and Perona, P. (2007) Graph-based visual saliency. Advances in Neural Information Processing Systems (NIPS), 19, 545–552.
45. Hou, X. and Zhang, L. (2007) Saliency detection: a spectral residual approach. IEEE Proceedings of Computer Vision and Pattern Recognition (CVPR07).
46. Hartline, H.K., Wagner, H.G. and Ratliff, F. (1956) Inhibition in the eye of Limulus. Journal of General Physiology, 39 (5), 651–673.
47. Rodieck, R.W. (1965) Quantitative analysis of cat retinal ganglion cell response to visual stimuli. Vision Research, 5 (11), 583–601.
48. Hochstein, S. and Shapley, R.M. (1976) Linear and nonlinear spatial subunits in Y cat retinal ganglion cells. The Journal of Physiology, 262, 265–284.
49. Moran, J. and Desimone, R. (1985) Selective attention gates visual processing in the extrastriate cortex. Science, 229, 782–784.
50. Reynolds, J.H., Chelazzi, L. and Desimone, R. (1999) Competitive mechanisms subserve attention in macaque areas V2 and V4. Journal of Neuroscience, 19, 1736–1753.
51. Kastner, S. and Ungerleider, L.G. (2001) The neural basis of biased competition in human visual cortex. Neuropsychologia, 39, 1263–1276.
52. Duncan, J., Humphreys, G. and Ward, R. (1997) Competitive brain activity in visual attention. Current Opinion in Neurobiology, 7 (2), 255–261.
53. Desimone, R. (1998) Visual attention mediated by biased competition in extrastriate visual cortex. Philosophical Transactions of the Royal Society of London, 353 (1375), 1245–1255.
54. Lee, D.K., Itti, L., Koch, C. and Braun, J. (1999) Attention activates winner-take-all competition visual filter. Nature Neuroscience, 2, 375–381.
55. Carandini, M., Heeger, D.J. and Movshon, J.A. (1997) Linearity and normalization in simple cells of macaque primary visual cortex. The Journal of Neuroscience, 17 (21), 8621–8644.
56. Maffei, L. and Fiorentini, A. (1973) The visual cortex as a spatial frequency analyzer. Vision Research, 13, 1255–1267.
57. Heeger, D.J. (1991) Nonlinear model of neural responses in cat visual cortex, in Computational Models of Visual Processing (eds M. Landy and J.A. Movshon), MIT, Cambridge, MA, pp. 119–133.
58. Carandini, M. and Heeger, D.J. (1994) Summation and division by neurons in visual cortex. Science, 264, 1333–1336.
59. Reynolds, J.H. and Heeger, D.J. (2009) The normalization model of attention. Neuron, 61, 168–185.
60. Eckhorn, R., Bauer, R. and Jordan, W. (1989) Coherent oscillations: a mechanism of feature linking in visual cortex? Multiple electrode and correlation analysis in the cat. Biological Cybernetics, 60, 121–130.
61. Gray, C.M., König, P., Engel, A.K. and Singe, W. (1989) Oscillatory responses in cat visual cortex exhibit intercolumnar synchronization which reflects global stimulus properties. Nature, 338, 334–337.
62. Wang, D. and Terman, D. (1997) Image segmentation based on oscillatory correlation. Neural Computation, 9 (4), 805–836.
63. Li, Z. (1998) A neural model of contour integration in the primary visual cortex. Neural Computation, 10 (4), 903–940.
64. Yu, B. and Zhang, L. (2004) Pulse-coupled neural networks for contour and motion matching. IEEE Transactions on Neural Networks, 15 (5), 1186–1201.
65. Li, Z. (2006) Theoretical understanding of the early visual processes by data compression and data selection. Network: Computation in Neural Systems, 17 (4), 301–334.
66. Li, Z. (2002) A saliency map in primary visual cortex. Trends in Cognitive Science, 6 (1), 9–16.
67. Yantis, S. (1998) Control of visual attention, in Attention (ed. H. Pashler), Psychology Press, pp. 223–256.
68. Buschman, T.J. and Miller, E.K. (2007) Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science, 315, 1860–1862.
69. Navapakkam, V. and Itti, L. (2005) Modeling the influence of task on attention. Vision Research, 45, 205–231.
70. Downing, P.E. (2000) Interactions between visual working memory and selective attention. Psychological Science, 11, 467–473.
71. Soto, D., Heinke, D. and Humphreys, G.W. (2005) Early involuntary top-down guidance of attention from working memory. Journal of Experimental Psychology: Human Perception and Performance, 31 (2), 248–261.
72. Tsotsos, J.K., Culhane, S.M. Wai, W.Y.K. et al.(1995) Modeling visual-attention via selective tuning. Artificial Intelligence, 78, 507–545.
73. Robinson, D.L. and Petersen, S.E. (1992) The pulvinar and visual salience. Trends in Neuroscience, 15, 127–132.
74. Gottlieb, J.P., Kusunoki, M. and Goldberg, M.E. (1998) The representation of visual salience in monkey parietal cortex. Nature, 391, 481–484.
75. Itti, L., Braun, J., Lee, D.K. and Koch, C. (1999) Attentional modulation of human pattern discrimination psychophysics reproduced by a quantitative model. Neural Networks, 19, 1430–1439.
76. Itti, L. and Koch, L. (1999) Feature combination strategies for saliency-based visual attention systems. Journal of Electronic Imaging, 10 (1), 161–169.
77. Walther, D. and Koch, C. (2006) Modeling attention to salient proto-objects. Neural Networks, 19, 1395–1407.
78. Frintrop, S. (2006) VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search, Springer.
79. Itti, L. (2004) Automatic foveation for video compression using a neurobiological model of visual attention. IEEE Transactions on Image Processing, 13 (10), 1304–1318.
80. Lu, Z., Lin, W. Yang, X. et al.(2005) Modeling visual attention's modulatory after effects on visual sensitivity and quality evaluation. IEEE Transactions on Image Processing, 14 (11), 1928–1942.
81. Crick, F. and Koch, C. (1990) Towards a neurobiological theory of consciousness. Seminars in The Neuroscience, 2, 263–275.
82. Quiles, M.G., Wang, D. Zhao, L. et al.(2009) An oscillatory correlation model of object-based attention. Proceedings of IJCNN, pp. 2596–2602.
83. Tsotsos, J.K. (2011) A Computational Perspective on Visual Attention, MIT Press.
84. Barlow, H.B. (1961) The coding of sensory messages, in Current Problems in Animal Behavior (eds W.H. Thorpe and O.L. Zangwill), Cambridge University Press, Cambridge, pp. 331–360.
85. Tsotsos, J.K. (1990) Analyzing vision at the complexity level. Behavioral and Brain Sciences, 13 (3), 423–445.
86. Olshausen, B.A. (1996) Emergence of simple-cell receptive field properties by learning a sparse code for nature images. Nature, 381 (6583), 607–609.
87. Bruce, N.D. and Tsotsos, J.K. (2006) Saliency based on information maximization. Advances in Neural Information Processing Systems (NIPS), 18, 155–162.
88. Bruce, N.D. and Tsotsos, J.K. (2009) Saliency, attention, and visual search: An information theoretic approach. Journal of Vision, 9 (3), 5, 1–24.
89. Itti, L. and Baldi, P. (2009) Bayesian surprise attracts human attention. Vision Research, 49, 1295–1306.
90. Guo, C., Ma, Q. and Zhang, L. (2008) Spatio-temporal saliency detection using phase spectrum of quaternion Fourier transform. IEEE Proceedings of Computer Vision and Pattern Recognition (CVPR0).
91. Guo, C.L. and Zhang, L.M. (2010) A novel multiresolution spatio temporal saliency detection model and its applications in image and video compression. IEEE Transaction on Image Processing, 19 (1), 185–198.
92. Yu, Y., Wang, B. and Zhang, L. (2009) Pulse discrete cosine transform for saliency-based visual attention. IEEE Proceedings of International Conference on Digital Life (ICDL09).
93. Bian, P. and Zhang, L. (2010) Visual saliency: a biologically plausible contourlet-like frequency domain approach. Cognitive Neurodynamics, 4 (3), 189–198.
94. Fang, Y., Lin, W. Lee, B.-S. et al.(2012) Bottom-up saliency detection model based on human visual sensitivity and amplitude spectrum. IEEE Transactions on Multimedia, 14 (1), 187–198.
95. Fang, Y., Chen, Z., Lin, W. and Lin, C. (2012) Saliency detection in the compressed domain for adaptive image retargeting. IEEE Transactions on Image Processing, 21 (9): 3888–3901, 2012.
96. Achanta, R. and Susstrunk, S. (2010) Saliency detection using maximum symmetric surround. Proceedings of 2010 IEEE 17th International Conference on Image Processing, (ICIP2010), pp. 2653–2656.
97. Liu, T., Sun, J. Zheng, N.-N. et al.(2011) Learning to detect a salient object. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33 (2), 353–367.
98. Cheng, M.-M., Zhang, G.-X. Mitra, N.J. et al.(2011) Global contrast based salient region detection. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition.
99. Grossberg, S., Mingolla, E. and Ross, W.D. (1994) A neural theory of attentive visual search: interactions of boundary, surface, spatial and object representations. Psychological Review, 10 (3), 470–489.
100. Grossberg, S. and Kelly, F.J. (1999) How does the cerebral cortex work? Learning, attention, and grouping by the laminar circuits of visual cortex. Spatial Vision, 12 (2), 163–185.
101. Deco, G. and Schurmann, B. (2000) A hierarchical neural system with attentional top-down enhancement of the spatial resolution for object recognition. Vision Research, 40 (20), 2845–2859.
102. 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.
103. Sun, Y. and Fisher, R. (2003) Object-based visual attention for computer vision. Artificial Intelligence, 146, 77–123.
104. 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.
105. Kanan, C., Tong, M., Zhang, L. and Cottrell, G. (2009) SUN: top-down saliency using natural statistics. Visual Cognition, 17 (6), 979–1003.
106. Westin, C.F., Westelius, C.J., Knutsson, H. and Granlund, G.H. (1996) Attention control for robot vision. IEEE Proceedings of Computer Vision and Pattern Recognition (CVPR96), pp. 18–20.
107. Sela, G. and Levine, M.D. (1997) Real-time attention from robotic vision. Real-Time Imaging, 3 (3), 173–194.
108. Takacs, B. and Wechsler, H. (1998) A dynamic and multiresolution model of visual attention and its application to facial landmark detection. Computer Vision and Image Understanding, 70 (1), 63–73.
109. 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.
110. Walther, D., Rutishauser, U., Koch, C. and Perona, P. (2005) Selective visual attention enables learning and recognition of multiple objects in cluttered scenes. Computer Vision and Image Understanding, 100 (1–2), 41–63.
111. Lowe, D.G. (2004) Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60 (2), 91–110.
112. Li, Z. and Itti, L. (2011) Saliency and gist features for target detection in satellite images. IEEE Transactions on Image Processing, 20 (7), 2017–2029.
113. Yu, Y., Wang, B. and Zhang, L. (2011) Hebbian-based neural networks for bottom-up visual attention and its applications to ship detection in SAR images. Neurocomputing, 74 (11), 2008–2017.
114. Ding, Z., Yu, Y., Wang, B. and Zhang, L. (2012) An approach for visual attention based on biquaternion and its application for ship detection in multispectral imagery. Neurocomputing, 76 (1), 9–17.
115. Wang, X., Wang, B. and Zhang, L. (2011) Airport detection in remote sensing images based on visual attention. Lecture Notes in Computer Science, 7064 (Part III), 475–484.
116. Marques, O., Mayron, L.M., Borba, G.B. and Gamba, H.R. (2006) Using visual attention to extract regions of interest in the context of image retrieval. Proceedings of the 44th ACM Southeast Conference (ACMSE2006), pp. 638–643.
117. 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.
118. Liu, A. and Lin, W. (2009) Perception based down sampling for low bit rate image coding. Proceedings of International Pacific-Rim Conference on Multimedia: Advances in Multimedia Information Processing, pp. 212–221.
119. Privitera, B.M. and Stark, L.W. (2000) Algorithms for defining visual regions-of-interest: comparison with eye fixations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (9), 970–982.
120. Bradley, A.P. and Stentiford, F.W.M. (2003) Visual attention for region of interest coding in JPEG 2000. Journal of Visual Communication & Image Representation, 14, 232–250.
121. Cheng, W., Chu, W. and Wu, J. (2005) A visual attention based region-of-interest determination framework for video sequences. IEICE Transactions on Information and Systems Journal, E-88D (7), 1578–1586.
122. Yang, X., Lin, W. Lu, Z. et al.(2005) Rate Control for videophone using perceptual sensitivity cues, IEEE Transactions on. Circuits and Systems for Video Technology, 15 (4), 496–507.
123. Lin, W., Dong, L. and Xue, P. (2005) Visual distortion gauge based on discrimination of noticeable contrast changes. IEEE Transactions on Circuits and Systems for Video Technology, 15 (7), 900–908.
124. Ma, Q. and Zhang, L. (2008) Image quality assessment with visual attention. IEEE Proceedings of International Conference on Pattern Recognition (ICPR08).
125. Moorthy, A.K. and Bovik, A.C. (2009) Visual importance pooling for image quality assessment, IEEE Journal on Special Topics in Signal Processing. Special Issue on Visual Media Quality Assessment, 3 (2), 193–201.
126. Ma, Q. and Zhang, L. (2010) A new strategy for image and video quality assessment. Journal of Electronic Imaging, 19 (1), P011019, 1–14.
127. Ren, T., Liu, Y. and Wu, G. (2009) Image retargeting based on global energy optimization. IEEE International Conference on Multimedia and Expo, pp. 406–409.
128. Yu, Y., Wang, B. and Zhang, L. (2011) Saliency-based compressive sampling for image signals. IEEE Signal Processing Letters, 17 (11), 973–976.