Retina-V1 model of detectability across the visual field.

A practical model is proposed for predicting the detectability of targets at arbitrary locations in the visual field, in arbitrary gray scale backgrounds, and under photopic viewing conditions. The major factors incorporated into the model include (a) the optical point spread function of the eye, (b) local luminance gain control (Weber's law), (c) the sampling array of retinal ganglion cells, (d) orientation and spatial frequency-dependent contrast masking, (e) broadband contrast masking, and (f) efficient response pooling. The model is tested against previously reported threshold measurements on uniform backgrounds (the ModelFest data set and data from Foley, Varadharajan, Koh, & Farias, 2007) and against new measurements reported here for several ModelFest targets presented on uniform, 1/f noise, and natural backgrounds at retinal eccentricities ranging from 0° to 10°. Although the model has few free parameters, it is able to account quite well for all the threshold measurements.

[1]  Paul M Bays,et al.  Temporal dynamics of encoding, storage, and reallocation of visual working memory. , 2011, Journal of vision.

[2]  M. Carandini,et al.  Normalization as a canonical neural computation , 2011, Nature Reviews Neuroscience.

[3]  A. Burgess.  Visual perception studies and observer models in medical imaging. , 2011, Seminars in nuclear medicine.

[4]  W. Geisler,et al.  Models of overt attention , 2011 .

[5]  S. Liversedge,et al.  Oxford handbook of eye movements , 2011 .

[6]  Norma V. Graham,et al.  Beyond multiple pattern analyzers modeled as linear filters (as classical V1 simple cells): Useful additions of the last 25 years , 2011, Vision Research.

[7]  Miguel P Eckstein,et al.  Visual search: a retrospective. , 2011, Journal of vision.

[8]  W. Geisler,et al.  Contributions of ideal observer theory to vision research , 2011, Vision Research.

[9]  E. Seidemann,et al.  Complex Dynamics of V1 Population Responses Explained by a Simple Gain-Control Model , 2009, Neuron.

[10]  Ehsan Samei,et al.  The Handbook of Medical Image Perception and Techniques , 2009 .

[11]  D. Levi.  Crowding—An essential bottleneck for object recognition: A mini-review , 2008, Vision Research.

[12]  N. Drasdo,et al.  The length of Henle fibers in the human retina and a model of ganglion receptive field density in the visual field , 2007, Vision Research.

[13]  G D Field,et al.  Information processing in the primate retina: circuitry and coding. , 2007, Annual review of neuroscience.

[14]  Mylène C. Q. Farias,et al.  Detection of Gabor patterns of different sizes, shapes, phases and eccentricities , 2007, Vision Research.

[15]  Miguel P. Eckstein,et al.  The effect of nonlinear human visual system components on performance of a channelized Hotelling observer in structured backgrounds , 2006, IEEE Transactions on Medical Imaging.

[16]  A. Watson,et al.  A standard model for foveal detection of spatial contrast. , 2005, Journal of vision.

[17]  Wilson S. Geisler,et al.  Optimal eye movement strategies in visual search , 2005, Nature.

[18]  M. Mcmahon,et al.  The origin of the oblique effect examined with pattern adaptation and masking. , 2003, Journal of vision.

[19]  B. Dosher,et al.  Characterizing human perceptual inefficiencies with equivalent internal noise. , 1999, Journal of the Optical Society of America. A, Optics, image science, and vision.

[20]  Alexander Toet,et al.  Visual conspicuity determines human target acquisition performance , 1998 .

[21]  A. M. Rohaly,et al.  Object detection in natural backgrounds predicted by discrimination performance and models , 1997, Vision Research.

[22]  J. Movshon,et al.  Linearity and Normalization in Simple Cells of the Macaque Primary Visual Cortex , 1997, The Journal of Neuroscience.

[23]  M P Eckstein,et al.  Visual signal detection in structured backgrounds. II. Effects of contrast gain control, background variations, and white noise. , 1997, Journal of the Optical Society of America. A, Optics, image science, and vision.

[24]  J A Solomon,et al.  Model of visual contrast gain control and pattern masking. , 1997, Journal of the Optical Society of America. A, Optics, image science, and vision.

[25]  D. G. Albrecht,et al.  Visual cortex neurons in monkeys and cats: Detection, discrimination, and identification , 1997, Visual Neuroscience.

[26]  Miguel P. Eckstein,et al.  Image discrimination models predict signal detection in natural medical image backgrounds , 1997, Electronic Imaging.

[27]  Wilson S. Geisler,et al.  Visual detection following retinal damage: predictions of an inhomogeneous retino-cortical model , 1996, Photonics West.

[28]  W. Geisler,et al.  Separation of low-level and high-level factors in complex tasks: visual search. , 1995, Psychological review.

[29]  J. M. Foley,et al.  Human luminance pattern-vision mechanisms: masking experiments require a new model. , 1994, Journal of the Optical Society of America. A, Optics, image science, and vision.

[30]  D. Dacey.  The mosaic of midget ganglion cells in the human retina , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[31]  David Williams,et al.  Modulation transfer of the human eye as a function of retinal eccentricity , 1993 .

[32]  D. Dacey,et al.  Dendritic field size and morphology of midget and parasol ganglion cells of the human retina. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[33]  D. Heeger.  Normalization of cell responses in cat striate cortex , 1992, Visual Neuroscience.

[34]  D. G. Albrecht,et al.  Motion selectivity and the contrast-response function of simple cells in the visual cortex , 1991, Visual Neuroscience.

[35]  E. Peli,et al.  Image invariance with changes in size: the role of peripheral contrast thresholds. , 1991, Journal of the Optical Society of America. A, Optics and image science.

[36]  J. Maunsell,et al.  The effects of parvocellular lateral geniculate lesions on the acuity and contrast sensitivity of macaque monkeys , 1991, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[37]  C. Curcio,et al.  Topography of ganglion cells in human retina , 1990, The Journal of comparative neurology.

[38]  N. Graham.  Visual Pattern Analyzers , 1989 .

[39]  D. Burr,et al.  Feature detection in human vision: a phase-dependent energy model , 1988, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[40]  A E Burgess,et al.  Visual signal detection. IV. Observer inconsistency. , 1988, Journal of the Optical Society of America. A, Optics and image science.

[41]  H H Barrett,et al.  Addition of a channel mechanism to the ideal-observer model. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[42]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[43]  G. Legge,et al.  Contrast discrimination in noise. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[44]  R. Watt,et al.  A theory of the primitive spatial code in human vision , 1985, Vision Research.

[45]  P. Lennie,et al.  Spatial and temporal contrast sensitivities of neurones in lateral geniculate nucleus of macaque. , 1984, The Journal of physiology.

[46]  R. L. Valois,et al.  The orientation and direction selectivity of cells in macaque visual cortex , 1982, Vision Research.

[47]  D. G. Albrecht,et al.  Spatial frequency selectivity of cells in macaque visual cortex , 1982, Vision Research.

[48]  R. F. Wagner,et al.  Efficiency of human visual signal discrimination. , 1981, Science.

[49]  J. M. Foley,et al.  Contrast masking in human vision. , 1980, Journal of the Optical Society of America.

[50]  A. Watson.  Probability summation over time , 1979, Vision Research.

[51]  J. Bergen,et al.  A four mechanism model for threshold spatial vision , 1979, Vision Research.

[52]  N. Graham.  Visual detection of aperiodic spatial stimuli by probability summation among narrowband channels , 1977, Vision Research.

[53]  J R Bloomfield,et al.  Visual Search in Complex Fields: Size Differences between Target Disc and Surrounding Discs , 1972, Human factors.

[54]  F. L. Engel.  Visual conspicuity, directed attention and retinal locus. , 1971, Vision research.

[55]  F. Campbell,et al.  The effect of orientation on the visual resolution of gratings , 1966, The Journal of physiology.

[56]  R. W. Rodieck.  Quantitative analysis of cat retinal ganglion cell response to visual stimuli. , 1965, Vision research.

[57]  Robbe L. T. Goris,et al.  A neural population model for visual pattern detection. , 2013, Psychological review.

[58]  B. Dosher,et al.  Characterizing observers using external noise and observer models: assessing internal representations with external noise. , 2008, Psychological review.

[59]  D. Hood,et al.  Lower-level visual processing and models of light adaptation. , 1998, Annual review of psychology.

[60]  L. Croner,et al.  Receptive fields of P and M ganglion cells across the primate retina , 1995, Vision Research.

[61]  John H. R. Maunsell,et al.  How parallel are the primate visual pathways? , 1993, Annual review of neuroscience.

[62]  D. Heeger.  Nonlinear model of neural responses in cat visual cortex. , 1991 .

[63]  Michael S. Landy,et al.  Nonlinear Model of Neural Responses in Cat Visual Cortex , 1991 .

[64]  G. J. Burton,et al.  Color and spatial structure in natural scenes. , 1987, Applied optics.

[65]  Donald C. Hood,et al.  Sensitivity to Light , 1986 .

[66]  L. Kaufman,et al.  Handbook of perception and human performance , 1986 .

[67]  P. Lennie,et al.  Spatial frequency analysis in the visual system. , 1985, Annual review of neuroscience.

[68]  Quick Rf.  A vector-magnitude model of contrast detection. , 1974 .

[69]  D. M. Green,et al.  Signal detection theory and psychophysics , 1966 .