Scanpath modeling and classification with hidden Markov models

[1]  Tim Chuk,et al.  Is having similar eye movement patterns during face learning and recognition beneficial for recognition performance? Evidence from hidden Markov modeling , 2017, Vision Research.

[2]  Michele Nappi,et al.  Eye movement analysis for human authentication: a critical survey , 2016, Pattern Recognit. Lett..

[3]  Antoine Coutrot,et al.  Face exploration dynamics differentiate men and women. , 2016, Journal of vision.

[4]  Neil D. B. Bruce,et al.  Predicting task from eye movements: On the importance of spatial distribution, dynamics, and image features , 2016, Neurocomputing.

[5]  Jean-Pierre Thibaut,et al.  An evaluation of scanpath-comparison and machine-learning classification algorithms used to study the dynamics of analogy making , 2016, Behavior Research Methods.

[6]  Tony Movshon.  Using artifice to understand nature , 2016 .

[7]  Stanislav Popelka,et al.  ScanGraph: A Novel Scanpath Comparison Method Using Visualisation of Graph Cliques , 2016 .

[8]  Bin Li,et al.  Using SVD on Clusters to Improve Precision of Interdocument Similarity Measure , 2016, Comput. Intell. Neurosci..

[9]  Wolfgang Rosenstiel,et al.  SubsMatch 2.0: Scanpath comparison and classification based on subsequence frequencies , 2016, Behavior Research Methods.

[10]  Patrick Le Callet,et al.  Quantifying the relation between perceived interest and visual salience during free viewing using trellis based optimization , 2016, 2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP).

[11]  Antoine Coutrot,et al.  Pupil dilation as an index of preferred mutual gaze duration , 2016, Royal Society Open Science.

[12]  Adrian Basarab,et al.  On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey , 2016, Artif. Intell. Medicine.

[13]  Wojciech Matusik,et al.  Eye Tracking for Everyone , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Zachary Chase Lipton.  The mythos of model interpretability , 2016, ACM Queue.

[15]  Calden Wloka,et al.  Spatially Binned ROC: A Comprehensive Saliency Metric , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Krishna R. Pattipati,et al.  On the use of hidden Markov models for gaze pattern modeling , 2016, SPIE Defense + Security.

[17]  Frédo Durand,et al.  What Do Different Evaluation Metrics Tell Us About Saliency Models? , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  O. Meur,et al.  Introducing context-dependent and spatially-variant viewing biases in saccadic models , 2016, Vision Research.

[19]  Ziho Kang,et al.  Characterization of Visual Scanning Patterns in Air Traffic Control , 2016, Comput. Intell. Neurosci..

[20]  Michael J. Haass,et al.  A new method for categorizing scanpaths from eye tracking data , 2016, ETRA.

[21]  Peter Guttorp,et al.  What we look at in paintings: A comparison between experienced and inexperienced art viewers , 2016, 1603.01066.

[22]  Reinhold Kliegl,et al.  Microsaccades Are Coupled to Heartbeat , 2016, The Journal of Neuroscience.

[23]  Yeliz Yesilada,et al.  Eye tracking scanpath analysis techniques on web pages: A survey, evaluation and comparison , 2015 .

[24]  Thomas S. A. Wallis,et al.  Information-theoretic model comparison unifies saliency metrics , 2015, Proceedings of the National Academy of Sciences.

[25]  Zhi-Cheng Zhao,et al.  On semantic-instructed attention: From video eye-tracking dataset to memory-guided probabilistic saliency model , 2015, Neurocomputing.

[26]  L. Itti.  New Eye-Tracking Techniques May Revolutionize Mental Health Screening , 2015, Neuron.

[27]  Daniel P. Kennedy,et al.  Atypical Visual Saliency in Autism Spectrum Disorder Quantified through Model-Based Eye Tracking , 2015, Neuron.

[28]  Zhi Liu,et al.  Saccadic model of eye movements for free-viewing condition , 2015, Vision Research.

[29]  Z. Chi,et al.  Eye-tracking-aided digital system for strabismus diagnosis , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[30]  R. Shapley,et al.  Noise masking of White's illusion exposes the weakness of current spatial filtering models of lightness perception. , 2015, Journal of vision.

[31]  Samia Nefti-Meziani,et al.  Predicting the Valence of a Scene from Observers’ Eye Movements , 2015, PloS one.

[32]  Junpeng Lao,et al.  iMap4: An open source toolbox for the statistical fixation mapping of eye movement data with linear mixed modeling , 2015, Behavior Research Methods.

[33]  Dennis M. Levi,et al.  Characteristics of fixational eye movements in amblyopia: Limitations on fixation stability and acuity? , 2015, Vision Research.

[34]  Antoine Coutrot,et al.  Tell me how you look and I will tell you what you are looking at. , 2015, Journal of vision.

[35]  C. Pernet,et al.  iMap 4: An Open Source Toolbox for the Statistical Fixation Mapping of Eye Movement data with Linear Mixed Modeling. , 2015, Journal of vision.

[36]  Giuseppe Boccignone,et al.  Advanced statistical methods for eye movement analysis and modeling: a gentle introduction , 2015, Eye Movement Research.

[37]  T. Giovannetti,et al.  The Potential Utility of Eye Movements in the Detection and Characterization of Everyday Functional Difficulties in Mild Cognitive Impairment , 2015, Neuropsychology Review.

[38]  Dirk B. Walther,et al.  Dissociation of salience-driven and content-driven spatial attention to scene category with predictive decoding of gaze patterns. , 2015, Journal of vision.

[39]  Michele Nappi,et al.  GANT: Gaze analysis technique for human identification , 2015, Pattern Recognit..

[40]  Alex S. Baldwin,et al.  Modeling probability and additive summation for detection across multiple mechanisms under the assumptions of signal detection theory. , 2015, Journal of vision.

[41]  G. Cottrell,et al.  Humans have idiosyncratic and task-specific scanpaths for judging faces , 2015, Vision Research.

[42]  Ali Borji,et al.  What do eyes reveal about the mind?: Algorithmic inference of search targets from fixations , 2015, Neurocomputing.

[43]  Steven J. Landry,et al.  An Eye Movement Analysis Algorithm for a Multielement Target Tracking Task: Maximum Transition-Based Agglomerative Hierarchical Clustering , 2015, IEEE Transactions on Human-Machine Systems.

[44]  Alan Kingstone,et al.  A comparison of scanpath comparison methods , 2014, Behavior Research Methods.

[45]  Jan-Louis Kruger,et al.  Attention distribution and cognitive load in a subtitled academic lecture: L1 vs. L2 , 2014 .

[46]  Haogang Zhu,et al.  What's on TV? Detecting age-related neurodegenerative eye disease using eye movement scanpaths , 2014, Front. Aging Neurosci..

[47]  James J. Clark,et al.  An inverse Yarbus process: Predicting observers’ task from eye movement patterns , 2014, Vision Research.

[48]  Tim Chuk,et al.  Understanding eye movements in face recognition using hidden Markov models. , 2014, Journal of vision.

[49]  Girish Kumar,et al.  Characteristics of fixational eye movements in people with macular disease. , 2014, Investigative ophthalmology & visual science.

[50]  Thierry Baccino,et al.  Discriminating cognitive processes with eye movements in a decision-making driving task. , 2014 .

[51]  A. Coutrot,et al.  How saliency, faces, and sound influence gaze in dynamic social scenes. , 2014, Journal of vision.

[52]  Yeliz Yesilada,et al.  Identifying Patterns in Eyetracking Scanpaths in Terms of Visual Elements of Web Pages , 2014, ICWE.

[53]  Simon Barthelmé,et al.  Spatial statistics and attentional dynamics in scene viewing. , 2014, Journal of vision.

[54]  T. Smith,et al.  Individual Differences in Infant Oculomotor Behavior During the Viewing of Complex Naturalistic Scenes , 2014, Infancy : the official journal of the International Society on Infant Studies.

[55]  Garrison W. Cottrell,et al.  Predicting an observer's task using multi-fixation pattern analysis , 2014, ETRA.

[56]  L. Itti,et al.  Defending Yarbus: eye movements reveal observers' task. , 2014, Journal of vision.

[57]  Shenmin Zhang,et al.  What do saliency models predict? , 2014, Journal of vision.

[58]  Giuseppe Boccignone,et al.  Ecological Sampling of Gaze Shifts , 2014, IEEE Transactions on Cybernetics.

[59]  Werner X. Schneider,et al.  Functionally sequenced scanpath similarity method (FuncSim): Comparing and evaluating scanpath similarity based on a task’s inherent sequence of functional (action) units , 2013 .

[60]  G. Zelinsky,et al.  Eye can read your mind: decoding gaze fixations to reveal categorical search targets. , 2013, Journal of vision.

[61]  Björn Peters,et al.  Hearing loss and a supportive tactile signal in a navigation system : effects on driving behavior and eye movements , 2013 .

[62]  Nicolas Riche,et al.  Saliency and Human Fixations: State-of-the-Art and Study of Comparison Metrics , 2013, 2013 IEEE International Conference on Computer Vision.

[63]  L. Maffei,et al.  Environmental enrichment strengthens corticocortical interactions and reduces amyloid-β oligomers in aged mice , 2013, Front. Aging Neurosci..

[64]  B. P. Klein,et al.  Macular degeneration affects eye movement behavior during visual search , 2013, Front. Psychol..

[65]  James J. Clark,et al.  A computational model for task inference in visual search. , 2013, Journal of vision.

[66]  Tom Foulsham,et al.  Look who's talking? Sound changes gaze behaviour in a dynamic social scene , 2013 .

[67]  John M. Henderson,et al.  Predicting Cognitive State from Eye Movements , 2013, PloS one.

[68]  T. Anderson,et al.  Eye movements in patients with neurodegenerative disorders , 2013, Nature Reviews Neurology.

[69]  Alan Kingstone,et al.  Recurrence quantification analysis of eye movements , 2013, Behavior Research Methods.

[70]  Nishan Canagarajah,et al.  Eye Movements to Natural Images as a Function of Sex and Personality , 2012, PloS one.

[71]  A. Coutrot,et al.  Influence of soundtrack on eye movements during video exploration , 2012 .

[72]  R. Savin-Williams,et al.  The Eyes Have It: Sex and Sexual Orientation Differences in Pupil Dilation Patterns , 2012, PloS one.

[73]  Felix A Wichmann,et al.  When luminance increment thresholds depend on apparent lightness. , 2012, Journal of vision.

[74]  J. Theeuwes,et al.  Top-down versus bottom-up attentional control: a failed theoretical dichotomy , 2012, Trends in Cognitive Sciences.

[75]  Simon Barthelmé,et al.  Modeling fixation locations using spatial point processes. , 2012, Journal of vision.

[76]  Thierry Baccino,et al.  Methods for comparing scanpaths and saliency maps: strengths and weaknesses , 2012, Behavior Research Methods.

[77]  Abdallah Namoune,et al.  Predicting user attention in complex web pages , 2012, Behav. Inf. Technol..

[78]  Ali Borji,et al.  Boosting bottom-up and top-down visual features for saliency estimation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[79]  Christof Koch,et al.  Learning visual saliency by combining feature maps in a nonlinear manner using AdaBoost. , 2012, Journal of vision.

[80]  Michelle R. Greene,et al.  Reconsidering Yarbus: A failure to predict observers’ task from eye movement patterns , 2012, Vision Research.

[81]  Jl Vazquez Aguirre,et al.  The prototype effect revisited: Evidence for an abstract feature model of face recognition (vol 8, pg 1, 2008) , 2012 .

[82]  T. Foulsham,et al.  It depends on how you look at it: Scanpath comparison in multiple dimensions with MultiMatch, a vector-based approach , 2012, Behavior Research Methods.

[83]  Toby A. Patterson,et al.  Spatial Variation in Foraging Behaviour of a Marine Top Predator (Phoca vitulina) Determined by a Large-Scale Satellite Tagging Program , 2012, PloS one.

[84]  Kayo Miura,et al.  Perceived duration of plaid motion increases with pattern speed rather than component speed. , 2012, Journal of vision.

[85]  Ioannis Rigas,et al.  Biometric identification based on the eye movements and graph matching techniques , 2012, Pattern Recognit. Lett..

[86]  Roman Bednarik,et al.  What do you want to do next: a novel approach for intent prediction in gaze-based interaction , 2012, ETRA.

[87]  Jan Theeuwes,et al.  A simple way to estimate similarity between pairs of eye movement sequences , 2012 .

[88]  Frédo Durand,et al.  A Benchmark of Computational Models of Saliency to Predict Human Fixations , 2012 .

[89]  Alexander Toet,et al.  Computational versus Psychophysical Bottom-Up Image Saliency: A Comparative Evaluation Study , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[90]  E. Buffalo,et al.  Detecting cognitive impairment by eye movement analysis using automatic classification algorithms , 2011, Journal of Neuroscience Methods.

[91]  R. Säljö,et al.  Expertise Differences in the Comprehension of Visualizations: a Meta-Analysis of Eye-Tracking Research in Professional Domains , 2011 .

[92]  Michael D. Dodd,et al.  Examining the influence of task set on eye movements and fixations. , 2011, Journal of Vision.

[93]  Sébastien Miellet,et al.  iMap: a novel method for statistical fixation mapping of eye movement data , 2011, Behavior research methods.

[94]  John M. Henderson,et al.  Clustering of Gaze During Dynamic Scene Viewing is Predicted by Motion , 2011, Cognitive Computation.

[95]  J. Theeuwes,et al.  ScanMatch: A novel method for comparing fixation sequences , 2010, Behavior research methods.

[96]  Peter König,et al.  Influence of Low-Level Stimulus Features, Task Dependent Factors, and Spatial Biases on Overt Visual Attention , 2010, PLoS Comput. Biol..

[97]  Joseph H. Goldberg,et al.  Scanpath clustering and aggregation , 2010, ETRA.

[98]  Sheriff Jolaoso,et al.  Scanpath comparison revisited , 2010, ETRA.

[99]  Thomas Couronné,et al.  A statistical mixture method to reveal bottom-up and top-down factors guiding the eye-movements , 2010 .

[100]  Marcus Nyström,et al.  An adaptive algorithm for fixation, saccade, and glissade detection in eyetracking data , 2010, Behavior research methods.

[101]  Gerhard Tröster,et al.  Eye Movement Analysis for Activity Recognition Using Electrooculography , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[102]  Xoana G. Troncoso,et al.  Microsaccades: a neurophysiological analysis , 2009, Trends in Neurosciences.

[103]  R. Baddeley,et al.  Do we look at lights? Using mixture modelling to distinguish between low- and high-level factors in natural image viewing , 2009 .

[104]  D. M. Titterington,et al.  VARIATIONAL BAYESIAN ANALYSIS FOR HIDDEN MARKOV MODELS , 2009 .

[105]  Alastair G. Gale,et al.  Radiology image perception and observer performance: How does expertise and clinical information alter interpretation? Stroke detection explored through eye-tracking , 2009, Medical Imaging.

[106]  Gary S. Rubin,et al.  The Role of Eye Movements During Reading in Patients with Age-Related Macular Degeneration (AMD) , 2009 .

[107]  Ilpo Kojo,et al.  Using hidden Markov model to uncover processing states from eye movements in information search tasks , 2008, Cognitive Systems Research.

[108]  Laurent Itti,et al.  Applying computational tools to predict gaze direction in interactive visual environments , 2008, TAP.

[109]  George D. Stetten,et al.  Effectiveness of augmented-reality visualization versus cognitive mediation for learning actions in near space , 2008, TAP.

[110]  Benjamin W Tatler,et al.  The central fixation bias in scene viewing: selecting an optimal viewing position independently of motor biases and image feature distributions. , 2007, Journal of vision.

[111]  J. Todd,et al.  The effects of viewing angle, camera angle, and sign of surface curvature on the perception of three-dimensional shape from texture. , 2007, Journal of vision.

[112]  A. Williams,et al.  Mechanisms Underpinning Successful Decision Making in Skilled Youth Soccer Players: An Analysis of Visual Search Behaviors , 2007, Journal of motor behavior.

[113]  Antonio Torralba,et al.  Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search. , 2006, Psychological review.

[114]  Ulf Ahlstrom,et al.  Using eye movement activity as a correlate of cognitive workload , 2006 .

[115]  Patrick Le Callet,et al.  A coherent computational approach to model bottom-up visual attention , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[116]  Anne R. Haake,et al.  eyePatterns: software for identifying patterns and similarities across fixation sequences , 2006, ETRA.

[117]  Geri Gay,et al.  Averaging scan patterns and what they can tell us , 2006, ETRA.

[118]  John K. Tsotsos,et al.  Saliency Based on Information Maximization , 2005, NIPS.

[119]  Asha Iyer,et al.  Components of bottom-up gaze allocation in natural images , 2005, Vision Research.

[120]  B. Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[121]  Alan C. Bovik,et al.  Point-of-gaze analysis reveals visual search strategies , 2004, IS&T/SPIE Electronic Imaging.

[122]  Jitendra Malik,et al.  Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons , 2001, International Journal of Computer Vision.

[123]  Theo Geisel,et al.  The ecology of gaze shifts , 2000, Neurocomputing.

[124]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[125]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[126]  A. L. I︠A︡rbus.  Eye Movements and Vision , 1967 .

[127]  J. Henderson,et al.  Journal of Experimental Psychology : Human Perception and Performance Classifying Mental States From Eye Movements During Scene Viewing , 2015 .

[128]  Marisa Carrasco,et al.  Attention enhances contrast appearance via increased baseline neural responses , 2014 .

[129]  L. Itti,et al.  High-throughput classification of clinical populations from natural viewing eye movements , 2012, Journal of Neurology.

[130]  Antón García-Díaz,et al.  Saliency from hierarchical adaptation through decorrelation and variance normalization , 2012, Image Vis. Comput..

[131]  Kari-Jouko Räihä,et al.  Some Applications of String Algorithms in Human-Computer Interaction , 2010, Algorithms and Applications.

[132]  Antonio Torralba,et al.  Building the gist of a scene: the role of global image features in recognition. , 2006, Progress in brain research.

[133]  Francesco Di Nocera,et al.  Another look at scanpath: distance to nearest neighbour as a measure of mental workload , 2006 .

[134]  Giuseppe Boccignone,et al.  Modelling gaze shift as a constrained random walk , 2004 .

[135]  George Eastman House,et al.  Sparse Bayesian Learning and the Relevance Vector Machine , 2001 .

[136]  D. S. Wooding,et al.  The relationship between the locations of spatial features and those of fixations made during visual examination of briefly presented images. , 1996, Spatial vision.

[137]  A. L. Yarbus,et al.  Eye Movements and Vision , 1967, Springer US.

[138]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

[139]  R. C. Langford.  How People Look at Pictures, A Study of the Psychology of Perception in Art. , 1936 .