Surface orientation from texture: ideal observers, generic observers and the information content of texture cues
Open accessAbstract
Perspective views of textured, planar surfaces provide a number of cues about the orientations of the surfaces. These include the information created by perspective scaling of texture elements (scaling), the information created by perspective foreshortening of texels (foreshortening) and, for textures composed of discrete elements, the information created by the effects of both scaling and foreshortening on the relative positions of texels (position). We drive a general form for ideal observers for each of these cues as they appear in images of spatially extended textures, (e.g. those composed of solid 2-D figures). As an application of the formulation, we derive a set of 'generic' observers which we show perform near optimally for images of a broad range of surface textures, without special prior knowledge about the statistics of the textures. Using simulations of ideal observers, we analyze the informational structure of texture cues, including a quantification of lower bounds on reliability for the three different cues, how cue reliability varies with slant angle and how it varies with field of view. We also quantify how strongly the reliability of the foreshortening cue depends on a prior assumption of isotropy. Finally, we extend the analysis to a naturalistic class of textures, showing that the information content of textures particularly suited to psychophysical investigation can be quantified, at least to a first-order approximation. The results provide an important computational foundation for psychophysical work on perceiving surface orientation from texture.
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The gradient of change in element size and density in a homogenously tiled surface's projection ("texture") provides information about its slant.
“The gradient of change in element size and density in a homogenously tiled surface's projection (“texture”) provides information about its slant (15).”
From Fusion of visual cues is not mandatory in children (2010) · cites this paper as [15]
Texture was much faster than disparity at the easier stimulus levels.
“Texture was much faster than disparity at the easier stimulus levels (Fig. 3), in line with texture-based slant discriminations becoming progressively easier toward the horizontal (15) and with binocular fusion for stereopsis requiring additional time irrespective of stimulus difficulty.”
From Fusion of visual cues is not mandatory in children (2010) · cites this paper as [15]
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Flagged first, then by citations
Research works printed but not linked: 2
- Knill DC. Discriminating surface slant from texture: comparing human and ideal observers. Vis Res 1998;38:1683 -1711.
- Rosenholtz R, Malik J. Shape from texture: Isotropy or homo- geneity (or both)? Vis Res 1997;37:2283-2294.
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- Authors
- Knill, D. C.
- Affiliations
- University of Pennsylvania (US)
- Corresponding author
- Knill, David C.
- Venue
- Vision Research
- Publisher
- Elsevier BV
- Published
- 1998-06
- Volume
- 38
- Issue
- 11
- Pages
- 1655-1682
- ISSN
- 0042-6989 (ISSN-L)
- OpenAlex
- W2048245740
- Cited by
- 147
- References
- 38
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 3 · 2023: 2 · 2022: 5 · 2021: 2 · 2020: 3 · 2019: 20 · 2018: 2 · 2017: 2 · 2016: 6 · 2015: 7 · 2014: 3 · 2013: 6 · 2012: 3
- FWCI
- 2.12
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Vision Research (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Keywords
- surface orientation, texture cues, ideal observer, foreshortening, cue reliability, slant perception, field of view, isotropy assumption
- MeSH terms
- Cues, Humans, Mathematics, Models, Neurological, Pattern Recognition, Visual, Psychophysics, Rotation, Likelihood Functions
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