Texture perception Lavanya Sharan February 23rd, 2011.
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Texture perception Lavanya Sharan February 23rd, 2011
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Transcript of Texture perception Lavanya Sharan February 23rd, 2011.
Texture perceptionLavanya Sharan
February 23rd, 2011
Typical texture perception display
Image source: Landy & Graham (2004)
Typical texture perception display
Image source: Landy & Graham (2004)
Typical texture perception display
Image source: Landy & Graham (2004)
Typical texture perception display
Image source: VPfaCGP Fig 8.5
Typical texture perception display
Image source: VPfaCGP Fig 8.5
These are examples of texture segregation/segmentation tasks.
Similar tasks in visual search (e.g., find a T among Ls)
Explaining performance at these tasksThe ‘back pocket’ model.
Image source: Landy & Graham (2004)
‘Back pocket’/LNL/FRF/Second-order model etc.
Image source: Landy & Graham (2004)
Input After 1st stage After 2nd stage Output
‘Back pocket’/LNL/FRF/Second-order model etc.
Image source: Landy & Graham (2004)
Lots of work on these models.Not tied to specific features (e.g., line terminations).Explain performance on many texture segregation tasks.Biological plausibility.
For example, Malik & Perona (1990)
Image source: VPfaCGP Fig 8.3
For example, Bergen & Adelson (1988)
Back pocket model works on most lab stimuli.
Image source: Ben-Shahar 2006
An failure case for the back pocket model.
Image source: Ben-Shahar 2006
Text
Textures Manual annotations
The orientation gradient is negligible across the perceptually salient boundaries.
Lab stimuli vs. Real world stimuli
Image source: Landy & Graham (2004) Image source: VPfaCGP Fig 8.2
Lots of psychophysics.
Many computational models of perception.
Hardly any psychophysics.
Very few computational models of perception (mostly in computer vision).
Modeling texture appearance (Portilla & Simoncelli 2001)
• Like Heeger & Bergen, impose constraints iteratively.
• Four classes of constraints. Each set adds something about real world texture appearance.
• Analytical model (as opposed to patch-based models) allows a framework for understanding texture perception.
Modeling texture appearance (Portilla & Simoncelli 2001)
• Like Heeger & Bergen, impose constraints iteratively.
• Four classes of constraints. Each set adds something about real world texture appearance.
• Analytical model (as opposed to patch-based models) allows a framework for understanding texture perception.
Shape from texture
Under assumption of isotropic texture patterns, one can estimate slant and tilt of surfaces.
Image source: VPfaCGP Fig 8.7
Slant, tilt & perspective interact to produce texture distortions
Image source: Todd et al. 2005
What about real world images?
Torralba & Oliva (2002)
Summary
✓ Most perceptual studies think of texture as black-and-white simple shapes.
✓ We have learnt a lot from these stimuli.
✓ Time to examine real-world textures. Some methods to manipulate these exist (e.g., computer vision methods).
✓ Real world texture overlaps with real world materials. More next time.
