SUN: A Bayesian framework for saliency using natural statistics.
暂无分享,去创建一个
Tim K Marks | Garrison W Cottrell | Tim K. Marks | Lingyun Zhang | Matthew H Tong | Honghao Shan | G. Cottrell | Lingyun Zhang | Honghao Shan
[1] I. P. Christensen,et al. Psychophysics , 2019, Encyclopedia of Evolutionary Psychological Science.
[2] J. Ward. Theory of Attention. , 1918 .
[3] R. L. Fantz. Visual Experience in Infants: Decreased Attention to Familiar Patterns Relative to Novel Ones , 1964, Science.
[4] A. J. Caron,et al. The effects of repeated exposure and stimulus complexity on visual fixation in infants , 1968 .
[5] J. Fagan. Memory in the infant. , 1970, Journal of experimental child psychology.
[6] Stephen J. Boies,et al. Components of attention. , 1971 .
[7] S. Friedman. Habituation and recovery of visual response in the alert human newborn. , 1972, Journal of experimental child psychology.
[8] U. Frith. Acurious effect with reversed letters explained by a theory of schema , 1974 .
[9] A. Treisman,et al. A feature-integration theory of attention , 1980, Cognitive Psychology.
[10] S Ullman,et al. Shifts in selective visual attention: towards the underlying neural circuitry. , 1985, Human neurobiology.
[11] A. Treisman,et al. Search asymmetry: a diagnostic for preattentive processing of separable features. , 1985, Journal of experimental psychology. General.
[12] M. Posner,et al. Inhibition of return : Neural basis and function , 1985 .
[13] C. Eriksen,et al. Visual attention within and around the field of focal attention: A zoom lens model , 1986, Perception & psychophysics.
[14] A Treisman,et al. Feature analysis in early vision: evidence from search asymmetries. , 1988, Psychological review.
[15] Susan L. Franzel,et al. Guided search: an alternative to the feature integration model for visual search. , 1989, Journal of experimental psychology. Human perception and performance.
[16] John K. Tsotsos. Analyzing vision at the complexity level , 1990, Behavioral and Brain Sciences.
[17] C. Bundesen. A theory of visual attention. , 1990, Psychological review.
[18] Michael I. Jordan,et al. Advances in Neural Information Processing Systems 30 , 1995 .
[19] Louette R. Johnson Lutjens. Research , 2006 .
[20] H. Nothdurft. Faces and Facial Expressions do not Pop Out , 1993, Perception.
[21] D. Ruderman. The statistics of natural images , 1994 .
[22] P Cavanagh,et al. Familiarity and pop-out in visual search , 1994, Perception & psychophysics.
[23] Joel L. Davis,et al. Large-Scale Neuronal Theories of the Brain , 1994 .
[24] Horace Barlow,et al. What is the computational goal of the neocortex , 1994 .
[25] John K. Tsotsos,et al. Modeling Visual Attention via Selective Tuning , 1995, Artif. Intell..
[26] J. V. van Hateren,et al. Modelling the power spectra of natural images: statistics and information. , 1996, Vision research.
[27] D. Levin. CLASSIFYING FACES BY RACE : THE STRUCTURE OF FACE CATEGORIES , 1996 .
[28] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[29] J. H. van Hateren,et al. Modelling the Power Spectra of Natural Images: Statistics and Information , 1996, Vision Research.
[30] Aapo Hyvärinen,et al. A Fast Fixed-Point Algorithm for Independent Component Analysis , 1997, Neural Computation.
[31] D. Chakrabarti,et al. A fast fixed - point algorithm for independent component analysis , 1997 .
[32] Terrence J. Sejnowski,et al. The “independent components” of natural scenes are edge filters , 1997, Vision Research.
[33] R. Rosenholtz. A simple saliency model predicts a number of motion popout phenomena , 1999, Vision Research.
[34] C. Koch,et al. A saliency-based search mechanism for overt and covert shifts of visual attention , 2000, Vision Research.
[35] D. Levin. Race as a visual feature: using visual search and perceptual discrimination tasks to understand face categories and the cross-race recognition deficit. , 2000, Journal of experimental psychology. General.
[36] C. Koch,et al. Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.
[37] David J. Fleet,et al. Probabilistic Models of the Brain : Perception and Neural Function , 2001 .
[38] E. Reingold,et al. Visual search asymmetry: The influence of stimulus familiarity and low-level features , 2001, Perception & psychophysics.
[39] Refractor. Vision , 2000, The Lancet.
[40] J. Wolfe. Asymmetries in visual search: An introduction , 2001, Perception & psychophysics.
[41] Rajesh P. N. Rao,et al. Eye movements in iconic visual search , 2002, Vision Research.
[42] Jeanny Hérault,et al. NATURAL SCENE PERCEPTION: VISUAL ATTRACTORS AND IMAGES PROCESSING , 2002 .
[43] Eero P. Simoncelli,et al. Natural image statistics and divisive normalization: Modeling nonlinearity and adaptation in cortical neurons , 2002 .
[44] Michel Vidal-Naquet,et al. Visual features of intermediate complexity and their use in classification , 2002, Nature Neuroscience.
[45] Derrick J. Parkhurst,et al. Modeling the role of salience in the allocation of overt visual attention , 2002, Vision Research.
[46] Antonio Torralba,et al. Top-down control of visual attention in object detection , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).
[47] Derrick J. Parkhurst,et al. Scene content selected by active vision. , 2003, Spatial vision.
[48] M. Lewicki,et al. Learning higher-order structures in natural images , 2003, Network.
[49] M. Lewicki,et al. Learning higher-order structures in natural images. , 2003 .
[50] Michael Brady,et al. Saliency, Scale and Image Description , 2001, International Journal of Computer Vision.
[51] Nuno Vasconcelos,et al. Discriminant Saliency for Visual Recognition from Cluttered Scenes , 2004, NIPS.
[52] Jitendra Malik,et al. An Information Maximization Model of Eye Movements , 2004, NIPS.
[53] Garrison W. Cottrell,et al. A model of scan paths applied to face recognition , 2004 .
[54] Amos Storkey,et al. Advances in Neural Information Processing Systems 20 , 2007 .
[55] D. Ballard,et al. Eye movements in natural behavior , 2005, Trends in Cognitive Sciences.
[56] John K. Tsotsos,et al. Saliency Based on Information Maximization , 2005, NIPS.
[57] Pierre Baldi,et al. A principled approach to detecting surprising events in video , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[58] Nuno Vasconcelos,et al. Integrated learning of saliency, complex features, and object detectors from cluttered scenes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[59] Iain D. Gilchrist,et al. Visual correlates of fixation selection: effects of scale and time , 2005, Vision Research.
[60] Kai-Sheng Song,et al. A globally convergent and consistent method for estimating the shape parameter of a generalized Gaussian distribution , 2006, IEEE Transactions on Information Theory.
[61] Bernhard Schölkopf,et al. A Nonparametric Approach to Bottom-Up Visual Saliency , 2006, NIPS.
[62] P. Subramanian. Active Vision: The Psychology of Looking and Seeing , 2006 .
[63] P. König,et al. Differences of monkey and human overt attention under natural conditions , 2006, Vision Research.
[64] 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.
[65] Laurent Itti,et al. The role of memory in guiding attention during natural vision. , 2006, Journal of vision.
[66] Geoffrey E. Hinton,et al. Topographic Product Models Applied to Natural Scene Statistics , 2006, Neural Computation.
[67] Pietro Perona,et al. Graph-Based Visual Saliency , 2006, NIPS.
[68] Garrison W. Cottrell,et al. Recursive ICA , 2006, NIPS.
[69] Preeti Verghese,et al. Where to look next? Eye movements reduce local uncertainty. , 2007, Journal of vision.
[70] O. Meur,et al. Predicting visual fixations on video based on low-level visual features , 2007, Vision Research.
[71] Nuno Vasconcelos,et al. Bottom-up saliency is a discriminant process , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[72] T. Sejnowski,et al. Cone selectivity derived from the responses of the retinal cone mosaic to natural scenes. , 2007, Journal of vision.
[73] 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.
[74] Matthew H Tong,et al. Information Attracts Attention: A Probabilistic Account of the Cross-Race Advantage in Visual Search , 2007 .
[75] P. Čičovački. SOCIETY , 2008, Society.
[76] Max Coltheart,et al. Cognitive Neuropsychology , 2014, Scholarpedia.
[77] Christof Koch,et al. A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .
[78] Pierre Baldi,et al. Bayesian surprise attracts human attention , 2005, Vision Research.