David J. Field
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David J. Fieldengineering Degrees
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David J. Fieldcomputer-science Degrees
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Machine Learning
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Engineering Computer Science
David J. Field's Degrees
- PhD Computer Science Stanford University
- Masters Electrical Engineering Stanford University
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(Suggest an Edit or Addition)David J. Field's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- Emergence of simple-cell receptive field properties by learning a sparse code for natural images (1996) (5883)
- Sparse coding with an overcomplete basis set: A strategy employed by V1? (1997) (3710)
- Relations between the statistics of natural images and the response properties of cortical cells. (1987) (3381)
- Contour integration by the human visual system: Evidence for a local “association field” (1993) (1652)
- What Is the Goal of Sensory Coding? (1994) (1372)
- Sparse coding of sensory inputs (2004) (1353)
- Natural image statistics and efficient coding. (1996) (657)
- How Close Are We to Understanding V1? (2005) (449)
- Visual sensitivity, blur and the sources of variability in the amplitude spectra of natural scenes (1997) (263)
- Integration of contours: new insights (1999) (222)
- Human discrimination of fractal images. (1990) (204)
- The structure and symmetry of simple-cell receptive-field profiles in the cat’s visual cortex (1986) (188)
- Contour integration and cortical processing (2003) (173)
- Statistical regularities of art images and natural scenes: spectra, sparseness and nonlinearities. (2007) (161)
- Wavelets, vision and the statistics of natural scenes (1999) (132)
- What's constant in contrast constancy? The effects of scaling on the perceived contrast of bandpass patterns (1995) (120)
- Local Contrast in Natural Images: Normalisation and Coding Efficiency (2000) (119)
- Is the spatial deficit in strabismic amblyopia due to loss of cells or an uncalibrated disarray of cells? (1994) (111)
- Contour integration in strabismic amblyopia: The sufficiency of an explanation based on positional uncertainty (1997) (106)
- Vision and the Coding of Natural Images (2000) (105)
- Can the theory of “whitening” explain the center-surround properties of retinal ganglion cell receptive fields? (2006) (90)
- What The Statistics Of Natural Images Tell Us About Visual Coding (1989) (81)
- The roles of polarity and symmetry in the perceptual grouping of contour fragments. (2000) (74)
- The role of “contrast enhancement” in the detection and appearance of visual contours (1998) (74)
- What is the other 85% of V1 doing? (2004) (69)
- Estimates of the information content and dimensionality of natural scenes from proximity distributions. (2007) (67)
- Variations in Intensity Statistics for Representational and Abstract Art, and for Art from the Eastern and Western Hemispheres (2008) (65)
- Phase reversal discrimination (1984) (61)
- Is the increased spatial uncertainty in the normal periphery due to spatial undersampling or uncalibrated disarray? (1993) (59)
- Contour integration across depth (1995) (55)
- Local masking in natural images: a database and analysis. (2014) (52)
- Mapping the similarity space of paintings: Image statistics and visual perception (2010) (50)
- Vision: The puzzle of amblyopia (1991) (50)
- Sensitivity to contrast histogram differences in synthetic wavelet-textures (2001) (48)
- What Is the Other 85 Percent of V1 Doing (2006) (48)
- Innate Visual Learning through Spontaneous Activity Patterns (2008) (45)
- Sparse Coding in the Neocortex (2007) (43)
- What Image Properties Regulate Eye Growth? (2006) (42)
- Sparse Coding of Natural Images Produces Localized, Oriented, Bandpass Receptive Fields (1995) (36)
- Does spatial invariance result from insensitivity to change? (2016) (32)
- Natural Image Statistics and Eecient Coding (1996) (32)
- Wavelet-Like Receptive Fields Emerge From a Network That Learns Sparse Codes for Natural Images (1996) (30)
- Natural Images: Coding Efficiency (2009) (24)
- Local edge statistics provide information regarding occlusion and nonocclusion edges in natural scenes. (2014) (19)
- Conjectures regarding the nonlinear geometry of visual neurons (2016) (16)
- Wavelets, blur, and the sources of variability in the amplitude spectra of natural scenes (1996) (16)
- Visual coding, redundancy, and “feature detection” (1998) (16)
- Global nonlinear compression of natural luminances in painted art (2008) (15)
- An attempt towards a unified account of non-linearities in visual neurons (2004) (11)
- Method for estimating the relative contribution of phase and power spectra to the total information in natural-scene patches. (2012) (11)
- Uncalibrated Distortions vs Undersampling (1996) (10)
- Selectivity, hyperselectivity, and the tuning of V1 neurons. (2017) (9)
- Matched filters, wavelets and the statistics of natural scenes (1999) (9)
- Contour integration and the association field (2013) (7)
- Contour integration: new insights (1999) (6)
- Learning efficient linear codes for natural images: the roles of sparseness, overcompleteness, and statistical independence (1996) (5)
- The Role of Hierarchy in Learning to Categorize Images (2011) (4)
- Role of contrast in monocular rivalry (A) (1979) (3)
- Towards a state-space geometry of neural responses to natural scenes: A steady-state approach (2019) (3)
- EFFICIENT NEURAL CODING OF NATURAL IMAGES (2008) (3)
- Finding a face on Mars: a study on the priors for illusory objects (2016) (2)
- The sparse structure of natural chemical environments (2017) (2)
- Vision and brain : how the brains sees : new approaches to computer vision (2004) (2)
- Contour Integration Across (1995) (2)
- Decorrelation and response equalization with center-surround receptive fields (2004) (2)
- On the statistics of soothing natural scenes (2020) (1)
- Introduction: vision and brain (2004) (1)
- Mapping the similarity space of paintings: is there a role for image statistics? (2008) (1)
- Dynamic Electrode-to-Image (DETI) mapping reveals the human brain’s spatiotemporal code of visual information (2021) (1)
- Measuring the Information Content of Visually-Evoked Neuroelectric Activity (2019) (1)
- On the Role of LGN/V1 Spontaneous Activity as an Innate Learning Pattern for Visual Development (2021) (1)
- Revealing the cortical transformations of real-world scenes using dynamic electrode-to-image (DETI) mapping (2021) (0)
- Revealing the hidden responses of a sparse coding network (2016) (0)
- A method of estimating the information content of natural scenes (2010) (0)
- Efficient Coding of Natural Images Outline: Abstract Introduction Efficient for What Task? Defining Efficiency A. Representational Efficiency Correlation and Decorrelation Optimal Information Transfer beyond Correlations: Sparseness and Independence Optimality with Nonlinear Systems B. Metabolic Eff (2007) (0)
- The geometry of selectivity and invariance in the early visual system (2014) (0)
- Translation invariance with a contour integration task (2012) (0)
- 2 Sparse Coding in the Neocortex (2006) (0)
- Innate Visual Learning Through Spontaneous Patterns of Activity (1999) (0)
- How much information is carried by the power and phase spectra of natural scenes (2010) (0)
- What steady state visual evoked potentials (SSVEP) tell us about the early representation of natural scenes. (2019) (0)
- Normative Visual Development: efficient coding principles for adult V1 predict properties of LGN waves prior to eye opening (2010) (0)
- Octave-wide channels and their relation to natural images (1990) (0)
- Mapping the neuroelectric state-space geometry of natural scenes (2018) (0)
- Selective and invariant features of neural response surfaces measured with principal curvature (2019) (0)
- A geometric approach to sparse coding yields insight into nonlinear responses (2016) (0)
- Statistics of edge profiles in natural scenes (2012) (0)
- How sensitive are we to distortions in natural scenes (2004) (0)
- A geometric state-space framework reveals the evoked potential topography of the visual field (2020) (0)
- Parameters that influence the distribution of activity in visual codes (1990) (0)
- Uncovering the Spatiotemporal Dynamics of Goal-driven Efficient Coding with a Brain-supervised Sparse coding Network (2022) (0)
- How do behavioral goals shape the spatiotemporal evolution of the sparse code for scenes? (2022) (0)
- Neural Correlates of Efficient Coding of Visual Scenes (2021) (0)
- Contour change detection in the periphery: threshold as a function of temporal interval (2012) (0)
- How alike are natural scenes and paintings? Characterising the spatial statistical properties of a set of digitised, grey-scale images of painted art (2005) (0)
- A new model and a new demonstration of contrast sensitivity (2020) (0)
- Gabor limits and hyper-selectivity in the tuning of V1 neurons (2017) (0)
- What’s constant in contrast constancy? (1991) (0)
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