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Computational and Mathematical Methods in Medicine
Volume 2018, Article ID 2676409, 13 pages
https://doi.org/10.1155/2018/2676409
Review Article

Computational Techniques for Eye Movements Analysis towards Supporting Early Diagnosis of Alzheimer’s Disease: A Review

1Instituto Politécnico Nacional-CITEDI, Tijuana, BC, Mexico
2CONACYT, Ciudad de México, Mexico
3LaBRI, University of Bordeaux, Bordeaux, France
4Instituto Nacional de Geriatría, Ciudad de México, Mexico
5INSERM, University of Bordeaux, Bordeaux, France

Correspondence should be addressed to Jessica Beltrán; moc.liamg@nartlebacissej

Received 3 November 2017; Accepted 3 April 2018; Published 20 May 2018

Academic Editor: Hyuntae Park

Copyright © 2018 Jessica Beltrán 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. G. Díaz, E. Romero, J. A. Hernández-Tamames, V. Molina, and N. Malpica, “Automatic classification of structural MRI for diagnosis of neurodegenerative diseases,” Acta Biologica Colombiana, vol. 15, no. 3, pp. 165–180, 2010. View at Google Scholar · View at Scopus
  2. J. Koikkalainen, H. Rhodius-Meester, A. Tolonen et al., “Differential diagnosis of neurodegenerative diseases using structural MRI data,” NeuroImage: Clinical, vol. 11, pp. 435–449, 2016. View at Publisher · View at Google Scholar · View at Scopus
  3. A. J. Stoessl, “Neuroimaging in the early diagnosis of neurodegenerative disease,” Translational Neurodegeneration, vol. 1, article no. 5, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Association, “2018 Alzheimers disease facts and figures,” Alzheimer’s & Dementia, vol. 14, no. 3, pp. 367–429, 2018. View at Publisher · View at Google Scholar
  5. J. Dauwels and S. Kannan, “Diagnosis of alzheimer's disease using electric signals of the brain—a grand challenge,” Asia-Pacific Biotech News, vol. 16, no. 10n11, pp. 22–38, 2012. View at Publisher · View at Google Scholar
  6. A. Nordberg, J. O. Rinne, A. Kadir, and B. Långström, “The use of PET in Alzheimer disease,” Nature Reviews Neurology, vol. 6, no. 2, pp. 78–87, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. W. V. Graham, A. Bonito-Oliva, and T. P. Sakmar, “Update on alzheimer's disease therapy and prevention strategies,” Annual Review of Medicine, vol. 68, no. 1, pp. 413–430, 2017. View at Publisher · View at Google Scholar
  8. M. E. De Vugt and F. R. J. Verhey, “The impact of early dementia diagnosis and intervention on informal caregivers,” Progress in Neurobiology, vol. 110, pp. 54–62, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. K. Pietrzak, K. Czarnecka, E. Mikiciuk-Olasik, and P. Szymanski, “New Perspectives of Alzheimer Disease Diagnosis – the Most Popular and Future Methods,” Medicinal Chemistry, 2017. View at Google Scholar
  10. J. Weuve, C. Proust-Lima, M. C. Power et al., “Guidelines for reporting methodological challenges and evaluating potential bias in dementia research,” Alzheimer’s & Dementia, vol. 11, no. 9, article no. 2048, pp. 1098–1109, 2015. View at Publisher · View at Google Scholar · View at Scopus
  11. G. Fernández, F. Manes, L. E. Politi et al., “Patients with Mild Alzheimer's Disease Fail When Using Their Working Memory: Evidence from the Eye Tracking Technique,” Journal of Alzheimer's Disease, vol. 50, no. 3, pp. 827–838, 2016. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Boucart, G. Bubbico, S. Szaffarczyk, and F. Pasquier, “Animal spotting in Alzheimer's disease: An eye tracking study of object categorization,” Journal of Alzheimer's Disease, vol. 39, no. 1, pp. 181–189, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. A. König, G. Sacco, G. Bensadoun et al., “The role of information and communication technologies in clinical trials with patients with Alzheimer's disease and related disorders,” Frontiers in Aging Neuroscience, vol. 7, p. 110, 2015. View at Publisher · View at Google Scholar · View at Scopus
  14. R. J. Molitor, P. C. Ko, and B. A. Ally, “Eye movements in alzheimers disease,” Journal of Alzheimers Disease, vol. 44, no. 1, pp. 1–12, 2015. View at Google Scholar
  15. M. Runxin, Y. Yu, and X. Yue, “Survey on Image Saliency Detection Methods,” in Proceedings of the 7th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2015, pp. 329–338, September 2015. View at Publisher · View at Google Scholar · View at Scopus
  16. L. Huo, L. Jiao, S. Wang, and S. Yang, “Object-level saliency detection with color attributes,” Pattern Recognition, vol. 49, pp. 162–173, 2016. View at Publisher · View at Google Scholar · View at Scopus
  17. P. K. Muthumanickam, C. Forsell, K. Vrotsou, J. Johansson, and M. Cooper, “Supporting exploration of eye tracking data: Identifying changing behaviour over long durations,” in Proceedings of the 6th Workshop Beyond Time and Errors on Novel Evaluation Methods for Visualization, BELIV 2016, pp. 70–77. View at Publisher · View at Google Scholar · View at Scopus
  18. V. Pallarés, M. Hernández, and L. Dempere-Marco, “Eye-Tracking Data in Visual Search Tasks: A, Hallmark of Cognitive Function,” Biosystems and Biorobotics, vol. 15, pp. 873–877, 2017. View at Publisher · View at Google Scholar · View at Scopus
  19. T. J. Crawford, A. Devereaux, S. Higham, and C. Kelly, “The disengagement of visual attention in Alzheimer's disease: A longitudinal eye-tracking study,” Frontiers in Aging Neuroscience, vol. 7, article no. 118, 2015. View at Publisher · View at Google Scholar · View at Scopus
  20. L. Itti, “New Eye-Tracking Techniques May Revolutionize Mental Health Screening,” Neuron, vol. 88, no. 3, pp. 442–444, 2015. View at Publisher · View at Google Scholar · View at Scopus
  21. K. Holmqvist, M. Nyström, R. Andersson et al., Eye tracking: A comprehensive guide to methods and measures, OUP Oxford, 2011.
  22. I. M. Pavisic, N. C. Firth, S. Parsons et al., “Eyetracking metrics in young onset alzheimer's disease: a window into cognitive visual functions,” Frontiers in Neurology, vol. 8, article 377, 2017. View at Google Scholar
  23. G. Fernández, L. R. Castro, M. Schumacher, and O. E. Agamennoni, “Diagnosis of mild Alzheimer disease through the analysis of eye movements during reading,” Journal of integrative neuroscience, vol. 14, no. 1, pp. 121–133, 2015. View at Publisher · View at Google Scholar · View at Scopus
  24. D. C. Niehorster, T. H. W. Cornelissen, K. Holmqvist, I. T. C. Hooge, and R. S. Hessels, “What to expect from your remote eye-tracker when participants are unrestrained,” Behavior Research Methods, pp. 1–15, 2017. View at Publisher · View at Google Scholar · View at Scopus
  25. V. Vallejo, D. Cazzoli, L. Rampa et al., “Effects of Alzheimer's disease on visual target detection: A "peripheral bias",” Frontiers in Aging Neuroscience, vol. 8, article no. 200, 2016. View at Publisher · View at Google Scholar · View at Scopus
  26. K. M. Galetta, K. R. Chapman, M. D. Essis et al., “Screening Utility of the King-Devick Test in Mild Cognitive Impairment and Alzheimer Disease Dementia,” Alzheimer Disease & Associated Disorders, vol. 31, no. 2, pp. 152–158, 2017. View at Publisher · View at Google Scholar · View at Scopus
  27. S. A. Chau, J. Chung, N. Herrmann, M. Eizenman, and K. L. Lanctôt, “Apathy and Attentional Biases in Alzheimer's Disease,” Journal of Alzheimer's Disease, vol. 51, no. 3, pp. 837–846, 2016. View at Publisher · View at Google Scholar · View at Scopus
  28. T. Suzuki, K. Yong, B. Yang et al., “Locomotion and eye behaviour under controlled environment in individuals with Alzheimer's disease,” in Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015, pp. 6594–6597, August 2015. View at Publisher · View at Google Scholar · View at Scopus
  29. G. Fernández, M. Schumacher, L. Castro, D. Orozco, and O. Agamennoni, “Patients with mild Alzheimer's disease produced shorter outgoing saccades when reading sentences,” Psychiatry Research, vol. 229, no. 1-2, pp. 470–478, 2015. View at Publisher · View at Google Scholar · View at Scopus
  30. Q. Lenoble, G. Bubbico, S. Szaffarczyk, F. Pasquier, and M. Boucart, “Scene categorization in Alzheimer's disease: A saccadic choice task,” Dementia and Geriatric Cognitive Disorders Extra, vol. 5, no. 1, pp. 1–12, 2015. View at Publisher · View at Google Scholar · View at Scopus
  31. G. Fernández, F. Manes, N. P. Rotstein et al., “Lack of contextual-word predictability during reading in patients with mild Alzheimer disease,” Neuropsychologia, vol. 62, no. 1, pp. 143–151, 2014. View at Publisher · View at Google Scholar · View at Scopus
  32. G. Fernández, J. Laubrock, P. Mandolesi, O. Colombo, and O. Agamennoni, “Registering eye movements during reading in Alzheimers disease: Difficulties in predicting upcoming words,” Journal of Clinical and Experimental Neuropsychology, vol. 36, no. 3, pp. 302–316, 2014. View at Publisher · View at Google Scholar · View at Scopus
  33. G. Fernández, P. Mandolesi, N. P. Rotstein, O. Colombo, O. Agamennoni, and L. E. Politi, “Eye movement alterations during reading in patients with early Alzheimer disease.,” Investigative ophthalmology & visual science, vol. 54, no. 13, pp. 8345–8352, 2013. View at Publisher · View at Google Scholar · View at Scopus
  34. Z. Kapoula, Q. Yang, J. Otero-Millan et al., “Distinctive features of microsaccades in Alzheimer's disease and in mild cognitive impairment,” AGE, vol. 36, no. 2, pp. 535–543, 2014. View at Publisher · View at Google Scholar · View at Scopus
  35. H. W. Heuer, J. B. Mirsky, E. L. Kong et al., “Antisaccade task reflects cortical involvement in mild cognitive impairment,” Neurology, vol. 81, no. 14, pp. 1235–1243, 2013. View at Publisher · View at Google Scholar · View at Scopus
  36. T. J. Crawford, S. Higham, J. Mayes, M. Dale, S. Shaunak, and G. Lekwuwa, “The role of working memory and attentional disengagement on inhibitory control: Effects of aging and Alzheimer's disease,” AGE, vol. 35, no. 5, pp. 1637–1650, 2013. View at Publisher · View at Google Scholar · View at Scopus
  37. Q. Yang, T. Wang, N. Su, S. Xiao, and Z. Kapoula, “Specific saccade deficits in patients with Alzheimer's disease at mild to moderate stage and in patients with amnestic mild cognitive impairment,” AGE, vol. 35, no. 4, pp. 1287–1298, 2013. View at Publisher · View at Google Scholar · View at Scopus
  38. A. C. McKee, R. Au, H. J. Cabral et al., “Visual association pathology in preclinical Alzheimer disease,” Journal of Neuropathology & Experimental Neurology, vol. 65, no. 6, pp. 621–630, 2006. View at Publisher · View at Google Scholar · View at Scopus
  39. A. A. Brewer and B. Barton, “Visual cortex in aging and Alzheimer's disease: Changes in visual field maps and population receptive fields,” Frontiers in Psychology, vol. 5, Article ID Article 74, 2014. View at Publisher · View at Google Scholar · View at Scopus
  40. Y. Kusne, A. B. Wolf, K. Townley, M. Conway, and G. A. Peyman, “Visual system manifestations of Alzheimer's disease,” Acta Ophthalmologica, 2016. View at Google Scholar · View at Scopus
  41. J. K. H. Lim, Q.-X. Li, Z. He et al., “The eye as a biomarker for Alzheimer's disease,” Frontiers in Neuroscience, vol. 10, article no. 536, 2016. View at Publisher · View at Google Scholar · View at Scopus
  42. R. A. Armstrong, “Alzheimer's disease and the eye,” Journal of Optometry, vol. 2, no. 3, pp. 103–111, 2009. View at Publisher · View at Google Scholar · View at Scopus
  43. F. Z. Javaid, J. Brenton, L. Guo, and M. F. Cordeiro, “Visual and ocular manifestations of Alzheimer's disease and their use as biomarkers for diagnosis and progression,” Frontiers in Neurology, vol. 7, article no. 55, 2016. View at Publisher · View at Google Scholar · View at Scopus
  44. M. R. MacAskill and T. J. Anderson, “Eye movements in neurodegenerative diseases,” Current Opinion in Neurology, vol. 29, no. 1, pp. 61–68, 2016. View at Publisher · View at Google Scholar · View at Scopus
  45. R. Tzekov and M. Mullan, “Vision function abnormalities in Alzheimer disease,” Survey of Ophthalmology, vol. 59, no. 4, pp. 414–433, 2014. View at Publisher · View at Google Scholar · View at Scopus
  46. U. Rüb, K. Del Tredici, C. Schultz, J. A. Büttner-Ennever, and H. Braak, “The premotor region essential for rapid vertical eye movements shows early involvement in Alzheimer's disease-related cytoskeletal pathology,” Vision Research, vol. 41, no. 16, pp. 2149–2156, 2001. View at Publisher · View at Google Scholar · View at Scopus
  47. A. L. Boxer, S. Garbutt, W. W. Seeley et al., “Saccade abnormalities in autopsy-confirmed frontotemporal lobar degeneration and alzheimer disease,” JAMA Neurology, vol. 69, no. 4, pp. 509–517, 2012. View at Publisher · View at Google Scholar · View at Scopus
  48. M. L. G. Freitas Pereira, M. von Zuben A Camargo, I. Aprahamian, and O. V. Forlenza, “Eye movement analysis and cognitive processing: Detecting indicators of conversion to Alzheimer's disease,” Neuropsychiatric Disease and Treatment, vol. 10, pp. 1273–1285, 2014. View at Publisher · View at Google Scholar · View at Scopus
  49. T. A. Amor, S. D. S. Reis, D. Campos, H. J. Herrmann, and J. S. Andrade, “Persistence in eye movement during visual search,” Scientific Reports, vol. 6, Article ID 20815, 2016. View at Publisher · View at Google Scholar · View at Scopus
  50. O. A. Coubard, “What do we know about eye movements in Alzheimer's disease? The past 37 years and future directions,” Biomarkers in Medicine, vol. 10, no. 7, pp. 677–680, 2016. View at Publisher · View at Google Scholar · View at Scopus
  51. T. J. Crawford and S. Higham, “Distinguishing between impairments of working memory and inhibitory control in cases of early dementia,” Neuropsychologia, vol. 81, pp. 61–67, 2016. View at Publisher · View at Google Scholar · View at Scopus
  52. Q. Yang, T. Wang, N. Su, Y. Liu, S. Xiao, and Z. Kapoula, “Long Latency and High Variability in Accuracy-Speed of Prosaccades in Alzheimer’s Disease at Mild to Moderate Stage,” Dementia and Geriatric Cognitive Disorders Extra, vol. 1, no. 1, pp. 318–329, 2011. View at Publisher · View at Google Scholar
  53. S. Garbutt, A. Matlin, J. Hellmuth et al., “Oculomotor function in frontotemporal lobar degeneration, related disorders and Alzheimer's disease,” Brain, vol. 131, no. 5, pp. 1268–1281, 2008. View at Publisher · View at Google Scholar · View at Scopus
  54. L. D. Kaufman, J. Pratt, B. Levine, and S. E. Black, “Antisaccades: A probe into the dorsolateral prefrontal cortex in Alzheimer's disease. A critical review,” Journal of Alzheimer's Disease, vol. 19, no. 3, pp. 781–793, 2010. View at Publisher · View at Google Scholar · View at Scopus
  55. A. Peltsch, A. Hemraj, A. Garcia, and D. P. Munoz, “Saccade deficits in amnestic mild cognitive impairment resemble mild Alzheimer's disease,” European Journal of Neuroscience, vol. 39, no. 11, pp. 2000–2013, 2014. View at Publisher · View at Google Scholar · View at Scopus
  56. L. D. Kaufman, J. Pratt, B. Levine, and S. E. Black, “Executive deficits detected in mild Alzheimer's disease using the antisaccade task,” Brain and Behavior, vol. 2, no. 1, pp. 15–21, 2012. View at Publisher · View at Google Scholar · View at Scopus
  57. S. H. Brooks, E. M. Klier, S. D. Red et al., “Slowed prosaccades and increased antisaccade errors as a potential behavioral biomarker of multiple system atrophy,” Frontiers in Neurology, vol. 8, article no. 261, 2017. View at Publisher · View at Google Scholar · View at Scopus
  58. N. Noiret, N. Carvalho, É. Laurent et al., “Saccadic Eye Movements and Attentional Control in Alzheimer's Disease,” Archives of Clinical Neuropsychology, vol. 33, no. 1, pp. 1–13, 2018. View at Publisher · View at Google Scholar
  59. A. C. Bowling, P. Lindsay, B. G. Smith, and K. Storok, “Saccadic eye movements as indicators of cognitive function in older adults,” Aging, Neuropsychology, and Cognition, vol. 22, no. 2, pp. 201–219, 2015. View at Publisher · View at Google Scholar · View at Scopus
  60. H. Amieva, H. Mokri, M. Le Goff et al., “Compensatory mechanisms in higher-educated subjects with Alzheimer's disease: A study of 20 years of cognitive decline,” Brain, vol. 137, no. 4, pp. 1167–1175, 2014. View at Publisher · View at Google Scholar · View at Scopus
  61. F. W. Bylsma, D. X. Rasmusson, G. W. Rebok, P. M. Keyl, L. Tune, and J. Brandt, “Changes in visual fixation and saccadic eye movements in Alzheimer's disease,” International Journal of Psychophysiology, vol. 19, no. 1, pp. 33–40, 1995. View at Publisher · View at Google Scholar · View at Scopus
  62. L. A. Hershey, L. Whicker, L. A. Abel, L. F. Dell'osso, S. Traccis, and D. Grossniklaus, “Saccadic Latency Measurements in Dementia,” JAMA Neurology, vol. 40, no. 9, pp. 592-593, 1983. View at Publisher · View at Google Scholar · View at Scopus
  63. W. A. Fletcher and J. A. Sharpe, “Saccadic eye movement dysfunction in Alzheimer's disease,” Annals of Neurology, vol. 20, no. 4, pp. 464–471, 1986. View at Publisher · View at Google Scholar · View at Scopus
  64. F. J. Pirozzolo and E. C. Hansch, “Oculomotor reaction time in dementia reflects degree of cerebral dysfunction,” Science, vol. 214, no. 4518, pp. 349–351, 1981. View at Publisher · View at Google Scholar · View at Scopus
  65. S. A. Chau, N. Herrmann, C. Sherman et al., “Visual Selective Attention Toward Novel Stimuli Predicts Cognitive Decline in Alzheimer's Disease Patients,” Journal of Alzheimer's Disease, vol. 55, no. 4, pp. 1–11, 2017. View at Publisher · View at Google Scholar · View at Scopus
  66. Y. Zhang, T. Wilcockson, K. I. Kim, T. Crawford, H. Gellersen, and P. Sawyer, “Monitoring dementia with automatic eye movements analysis,” Smart Innovation, Systems and Technologies, vol. 57, pp. 299–309, 2016. View at Publisher · View at Google Scholar · View at Scopus
  67. S. Chaabouni, J. Benois-pineau, F. Tison, C. Ben Amar, and A. Zemmari, “Prediction of visual attention with deep CNN on artificially degraded videos for studies of attention of patients with Dementia,” Multimedia Tools and Applications, vol. 76, no. 21, pp. 1–20, 2017. View at Publisher · View at Google Scholar · View at Scopus
  68. S. Chaabouni, F. Tison, J. Benois-Pineau, and C. Ben Amar, “Prediction of visual attention with Deep CNN for studies of neurodegenerative diseases,” in Proceedings of the 14th International Workshop on Content-Based Multimedia Indexing, CBMI 2016, pp. 1–6, June 2016. View at Publisher · View at Google Scholar · View at Scopus
  69. P.-H. Tseng, I. G. M. Cameron, G. Pari, J. N. Reynolds, D. P. Munoz, and L. Itti, “High-throughput classification of clinical populations from natural viewing eye movements,” Journal of Neurology, vol. 260, no. 1, pp. 275–284, 2013. View at Publisher · View at Google Scholar · View at Scopus
  70. M. Land, N. Mennie, and J. Rusted, “The roles of vision and eye movements in the control of activities of daily living,” Perception, vol. 28, no. 11, pp. 1311–1328, 1999. View at Publisher · View at Google Scholar · View at Scopus
  71. S. C. Seligman and T. Giovannetti, “The Potential Utility of Eye Movements in the Detection and Characterization of Everyday Functional Difficulties in Mild Cognitive Impairment,” Neuropsychology Review, vol. 25, no. 2, pp. 199–215, 2015. View at Publisher · View at Google Scholar · View at Scopus
  72. M. F. Land and M. Hayhoe, “In what ways do eye movements contribute to everyday activities?” Vision Research, vol. 41, no. 25-26, pp. 3559–3565, 2001. View at Publisher · View at Google Scholar · View at Scopus
  73. M. F. Land, “Eye movements and the control of actions in everyday life,” Progress in Retinal and Eye Research, vol. 25, no. 3, pp. 296–324, 2006. View at Publisher · View at Google Scholar · View at Scopus
  74. F. Donnarumma, M. Costantini, E. Ambrosini, K. Friston, and G. Pezzulo, “Action perception as hypothesis testing,” Cortex, vol. 89, pp. 45–60, 2017. View at Publisher · View at Google Scholar · View at Scopus
  75. J. F. G. Boisvert and N. D. B. Bruce, “Predicting task from eye movements: On the importance of spatial distribution, dynamics, and image features,” Neurocomputing, vol. 207, pp. 653–668, 2016. View at Publisher · View at Google Scholar · View at Scopus
  76. E. M. E. Forde, J. Rusted, N. Mennie, M. Land, and G. W. Humphreys, “The eyes have it: An exploration of eye movements in action disorganisation syndrome,” Neuropsychologia, vol. 48, no. 7, pp. 1895–1900, 2010. View at Publisher · View at Google Scholar · View at Scopus
  77. ClinicalTrials.gov [Internet], National Library of Medicine (US), Centre Hospitalier Universitaire de Nice, Identifier NCT02557464, “Identification of early markers of alzheimer’s disease by using eye tracking in reading. (adal),” 2015, this study is currently recruiting participants. Available: https://clinicaltrials.gov/ct2/show/NCT02557464?term=eye&cond=Alzheimer+Disease&cntry=FR&rank=1.
  78. ClinicalTrials.gov [Internet], National Library of Medicine (US), Centre Hospitalier Universitaire de Nice, Identifier NCT02941289 , “Visuospatial attention, eye movements and instrumental activities of daily living (iadls) in alzheimer’s disease (arva-ma),” 2016, this study is currently recruiting participants. Available: https://clinicaltrials.gov/ct2/show/NCT02941289?term=Eye+movements&recrs=ab&cond=Alzheimer+Disease&rank=1.
  79. M. Mancas, V. P. Ferrera, N. Riche, and J. G. Taylor, From Human Attention to Computational Attention: A Multidisciplinary Approach, vol. 10, Springer, 2016.
  80. S. Frintrop, “Computational visual attention,” in Computer Analysis of Human Behavior, pp. 69–101, Springer, 2011. View at Google Scholar
  81. L. Itti and C. Koch, “Computational modelling of visual attention,” Nature Reviews Neuroscience, vol. 2, no. 3, pp. 194–203, 2001. View at Publisher · View at Google Scholar · View at Scopus
  82. J. K. Tsotsos and A. Rothenstein, “Computational models of visual attention,” Scholarpedia, vol. 6, no. 1, article 6201, 2011. View at Publisher · View at Google Scholar
  83. S. Wang, M. Jiang, X. M. Duchesne et al., “Atypical Visual Saliency in Autism Spectrum Disorder Quantified through Model-Based Eye Tracking,” Neuron, vol. 88, no. 3, pp. 604–616, 2015. View at Publisher · View at Google Scholar · View at Scopus
  84. S. Singh, C. Arora, and C. V. Jawahar, “First person action recognition using deep learned descriptors,” in Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, pp. 2620–2628, July 2016. View at Scopus
  85. A. Betancourt, P. Morerio, C. S. Regazzoni, and M. Rauterberg, “The evolution of first person vision methods: A survey,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 5, pp. 744–760, 2015. View at Publisher · View at Google Scholar · View at Scopus
  86. J. Pan, C. Canton-Ferrer, K. McGuinness et al., “Salgan: Visual saliency prediction with generative adversarial networks,” CoRR, https://arxiv.org/abs/1701.01081v2.
  87. M. Cornia, L. Baraldi, G. Serra, and R. Cucchiara, “Predicting human eye fixations via an lstm-based saliency attentive model,” CoRR, https://arxiv.org/abs/1611.09571v3.
  88. P. Reinagel and A. M. Zador, “Natural scene statistics at the centre of gaze,” Network: Computation in Neural Systems, vol. 10, no. 1-10, article 4, 1999. View at Publisher · View at Google Scholar · View at Scopus
  89. T. Jost, N. Ouerhani, R. V. Wartburg, R. Müri, and H. Hügli, “Assessing the contribution of color in visual attention,” Computer Vision and Image Understanding, vol. 100, no. 1-2, pp. 107–123, 2005. View at Publisher · View at Google Scholar · View at Scopus
  90. R. J. Baddeley and B. W. Tatler, “High frequency edges (but not contrast) predict where we fixate: A Bayesian system identification analysis,” Vision Research, vol. 46, no. 18, pp. 2824–2833, 2006. View at Publisher · View at Google Scholar · View at Scopus
  91. A. M. Treisman and G. Gelade, “A feature-integration theory of attention,” Cognitive Psychology, vol. 12, no. 1, pp. 97–136, 1980. View at Publisher · View at Google Scholar · View at Scopus
  92. A. Borji, M. Cheng, H. Jiang, and J. Li, “Salient object detection: a survey,” CoRR, https://arxiv.org/abs/1411.5878.
  93. M. Cerf, E. P. Frady, and C. Koch, “Faces and text attract gaze independent of the task: Experimental data and computer model,” Journal of vision, vol. 9, no. 12, pp. 10–10, 2009. View at Google Scholar
  94. S. S. S. Kruthiventi, V. Gudisa, J. H. Dholakiya, and R. V. Babu, “Saliency unified: A deep architecture for simultaneous eye fixation prediction and salient object segmentation,” in Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, pp. 5781–5790, July 2016. View at Scopus
  95. G. Li and Y. Yu, “Deep contrast learning for salient object detection,” in Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, pp. 478–487, July 2016. View at Scopus
  96. N. Liu and J. Han, “DHSNet: Deep hierarchical saliency network for salient object detection,” in Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, pp. 678–686, July 2016. View at Scopus
  97. Q. Zhao and C. Koch, “Learning saliency-based visual attention: A review,” Signal Processing, vol. 93, no. 6, pp. 1401–1407, 2013. View at Publisher · View at Google Scholar · View at Scopus
  98. A. Borji and L. Itti, “State-of-the-art in visual attention modeling,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 1, pp. 185–207, 2013. View at Publisher · View at Google Scholar · View at Scopus
  99. J. Kuen, Z. Wang, and G. Wang, “Recurrent attentional networks for saliency detection,” in Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, pp. 3668–3677, July 2016. View at Scopus
  100. R. Veale, Z. M. Hafed, and M. Yoshida, “How is visual salience computed in the brain? Insights from behaviour, neurobiology and modeling,” Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 372, no. 1714, Article ID 20160113, 2017. View at Publisher · View at Google Scholar · View at Scopus
  101. A. Borji, D. N. Sihite, and L. Itti, “Quantitative analysis of human-model agreement in visual saliency modeling: a comparative study,” IEEE Transactions on Image Processing, vol. 22, no. 1, pp. 55–69, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  102. L. Duan, J. Gu, Z. Yang, J. Miao, W. Ma, and C. Wu, “Bio-inspired Visual Attention Model and Saliency Guided Object Segmentation,” in Genetic and Evolutionary Computing, vol. 238 of Advances in Intelligent Systems and Computing, pp. 291–298, Springer, 2014. View at Publisher · View at Google Scholar
  103. W.-T. Li, H.-S. Chang, K.-C. Lien, H.-T. Chang, and Y.-C. F. Wang, “Exploring visual and motion saliency for automatic video object extraction,” IEEE Transactions on Image Processing, vol. 22, no. 7, pp. 2600–2610, 2013. View at Publisher · View at Google Scholar · View at Scopus
  104. Y.-C. Su and K. Grauman, “Detecting engagement in egocentric video,” in Proceedings of the European Conference on Computer Vision, pp. 454–471, Springer, 2016.
  105. H. Boujut, V. Buso, J. Benois-Pineau et al., “Visual saliency maps for studies of behavior of patients with neurodegenerative diseases: Observer’s versus actor’s points of view,” in Innovation in Medicine & Healthcare, KES, 2013. View at Google Scholar
  106. V. Buso, I. González-Díaz, and J. Benois-Pineau, “Goal-oriented top-down probabilistic visual attention model for recognition of manipulated objects in egocentric videos,” Signal Processing: Image Communication, vol. 39, pp. 418–431, 2015. View at Publisher · View at Google Scholar · View at Scopus
  107. A. Fathi, X. Ren, and J. M. Rehg, “Learning to recognize objects in egocentric activities,” in Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011, pp. 3281–3288, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  108. X. Ren and M. Philipose, “Egocentric recognition of handled objects: Benchmark and analysis,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, pp. 1–8, IEEE, 2009.
  109. Y. Li, A. Fathi, and J. M. Rehg, “Learning to predict gaze in egocentric video,” in Proceedings of the 2013 14th IEEE International Conference on Computer Vision, ICCV 2013, pp. 3216–3223, December 2013. View at Publisher · View at Google Scholar · View at Scopus
  110. K. Matsuo, K. Yamada, S. Ueno, and S. Naito, “An attention-based activity recognition for egocentric video,” in Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014, pp. 551–556, USA, June 2014. View at Publisher · View at Google Scholar · View at Scopus
  111. P. Le Bek, Learning to recognise actions in egocentric video, [MSc, thesis], University Glasgow, School of Computing Science, 2014.
  112. S. Singh, C. Arora, and C. V. Jawahar, “Trajectory aligned features for first person action recognition,” Pattern Recognition, vol. 62, pp. 45–55, 2017. View at Publisher · View at Google Scholar · View at Scopus
  113. M. Ma, H. Fan, and K. M. Kitani, “Going deeper into first-person activity recognition,” in Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, pp. 1894–1903, July 2016. View at Scopus
  114. T.-H. Nguyen, J.-C. Nebel, and F. Florez-Revuelta, “Recognition of activities of daily living with egocentric vision: A review,” Sensors, vol. 16, no. 1, article no. 72, 2016. View at Publisher · View at Google Scholar · View at Scopus
  115. K. Yamada, Y. Sugano, T. Okabe et al., “Attention prediction in egocentric video using motion and visual saliency,” Advances in Image and Video Technology, pp. 277–288, 2012. View at Google Scholar
  116. X. Sun, H. Yao, R. Ji, and X.-M. Liu, “Toward statistical modeling of saccadic eye-movement and visual saliency,” IEEE Transactions on Image Processing, vol. 23, no. 11, pp. 4649–4662, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  117. J. K. Foster, M. Behrmann, and D. T. Stuss, “Visual attention deficits in Alzheimer's disease: Simple versus conjoined feature search,” Neuropsychology, vol. 13, no. 2, pp. 223–245, 1999. View at Publisher · View at Google Scholar · View at Scopus
  118. A. Tales, J. Muir, R. Jones, A. Bayer, and R. J. Snowden, “The effects of saliency and task difficulty on visual search performance in ageing and Alzheimer's disease,” Neuropsychologia, vol. 42, no. 3, pp. 335–345, 2004. View at Publisher · View at Google Scholar · View at Scopus