ArticleVision research2015
A perceptual space of local image statistics.
Article in Vision research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Article
- Generation of surrogate brain maps preserving spatial autocorrelation through random rotation of geometric eigenmodes.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- The Geometry of Low- and High-Level Perceptual Spaces.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Local texture statistics augment the power spectrum in modeling radiographic judgments of breast density.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Discrimination of textures with spatial correlations and multiple gray levels.Journal of the Optical Society of America. A, Optics, image science, and vision · 2023Article
- Sensitivity to naturalistic texture relies primarily on high spatial frequencies.Journal of vision · 2023Article
- Geometry of spiking patterns in early visual cortex: a topological data analytic approach.Journal of the Royal Society, Interface · 2022Article
- Functional recursion of orientation cues in figure-ground separation.Vision research · 2022Article
- Luminance texture boundaries and luminance step boundaries are segmented using different mechanisms.Vision research · 2022Article
- Efficient coding of natural scene statistics predicts discrimination thresholds for grayscale textures.eLife · 2020Article
- Image segmentation driven by elements of form.Vision research · 2019Article
- The features that control discrimination of an isodipole texture pair.Vision research · 2019Article
- Evaluation of non-Gaussian statistical properties in virtual breast phantoms.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Systematic Differences Between Perceptually Relevant Image Statistics of Brain MRI and Natural Images.Frontiers in neuroinformatics · 2019Article
- Deep Synthesis of Realistic Medical Images: A Novel Tool in Clinical Research and Training.Frontiers in neuroinformatics · 2018Article
- Textures as Probes of Visual Processing.Annual review of vision science · 2017Review
- Two representations of a high-dimensional perceptual space.Vision research · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Local image statistics are important for visual analysis of textures, surfaces, and form. There are many kinds of local statistics, including those that capture luminance distributions, spatial contrast, oriented segments, and corners. While sensitivity to each of these kinds of statistics have been well-studied, much less is known about visual processing when multiple kinds of statistics are relevant, in large part because the dimensionality of the problem is high and different kinds of statistics interact. To approach this problem, we focused on binary images on a square lattice - a reduced set of stimuli which nevertheless taps many kinds of local statistics. In this 10-parameter space, we determined psychophysical thresholds to each kind of statistic (16 observers) and all of their pairwise combinations (4 observers). Sensitivities and isodiscrimination contours were consistent across observers. Isodiscrimination contours were elliptical, implying a quadratic interaction rule, which in turn determined ellipsoidal isodiscrimination surfaces in the full 10-dimensional space, and made predictions for sensitivities to complex combinations of statistics. These predictions, including the prediction of a combination of statistics that was metameric to random, were verified experimentally. Finally, check size had only a mild effect on sensitivities over the range from 2.8 to 14min, but sensitivities to second- and higher-order statistics was substantially lower at 1.4min. In sum, local image statistics form a perceptual space that is highly stereotyped across observers, in which different kinds of statistics interact according to simple rules.
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Registered trials
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