The rough side of texture: texture analysis through the lens of HVEI

Pappas, Thrasyvoulos N.

doi:10.1117/12.2012991

Cited by 6 publications

(6 citation statements)

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“…Experimentally, we closely follow the approach of Balas (2006), described above. 1 Using images that are all physically different measures the extent to which model syntheses are categorically or structurally lossless (in that they could both be considered samples from original images; Pappas, 2013), as opposed to being perceptually lossless (unable to be told apart) compared either to each other (Freeman & Simoncelli, 2011) or the original source images (Wallis et al, 2016). Perceptual losslessness could be important for understanding visual encoding in general but categorical losslessness is arguably more useful for understanding the perceptual representation of texture.…”

Section: Studying Texture Perception With Parametric Texture Modelsmentioning

confidence: 99%

“…Where surfaces of different textures form occlusion boundaries, texture can provide a powerful segmentation cue; conversely, occlusion borders of similarly textured surfaces can camouflage the occlusion (hiding a tiger among the leaves). Given the importance and ubiquity of visual textures, it is little wonder that they have received much scientific attention, not only from within vision science but also in computer vision, graphics, and art (see Dakin, 2014;Landy, 2013;Pappas, 2013;Rosenholtz, 2014, for comprehensive recent reviews of this field).…”

Section: Introductionmentioning

confidence: 99%

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A parametric texture model based on deep convolutional features closely matches texture appearance for humans

et al. 2017

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Our visual environment is full of texture-"stuff" like cloth, bark, or gravel as distinct from "things" like dresses, trees, or paths-and humans are adept at perceiving subtle variations in material properties. To investigate image features important for texture perception, we psychophysically compare a recent parametric model of texture appearance (convolutional neural network [CNN] model) that uses the features encoded by a deep CNN (VGG-19) with two other models: the venerable Portilla and Simoncelli model and an extension of the CNN model in which the power spectrum is additionally matched. Observers discriminated model-generated textures from original natural textures in a spatial three-alternative oddity paradigm under two viewing conditions: when test patches were briefly presented to the near-periphery ("parafoveal") and when observers were able to make eye movements to all three patches ("inspection"). Under parafoveal viewing, observers were unable to discriminate 10 of 12 original images from CNN model images, and remarkably, the simpler Portilla and Simoncelli model performed slightly better than the CNN model (11 textures). Under foveal inspection, matching CNN features captured appearance substantially better than the Portilla and Simoncelli model (nine compared to four textures), and including the power spectrum improved appearance matching for two of the three remaining textures. None of the models we test here could produce indiscriminable images for one of the 12 textures under the inspection condition. While deep CNN (VGG-19) features can often be used to synthesize textures that humans cannot discriminate from natural textures, there is currently no uniformly best model for all textures and viewing conditions.

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Section: Studying Texture Perception With Parametric Texture Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A parametric texture model based on deep convolutional features closely matches texture appearance for humans

et al. 2017

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“…Our experiments show that humans cannot tell which of three physically-different images were "generated by a different process" (for all but one of the images we test). This condition could be termed "categorical" or "structural" losslessness (Pappas, 2013): under our experimental conditions, the model syntheses can pass as natural textures (they are perceived as the same category). Images that are perceptually equivalent along some dimension can also be called "eidolons" (Koenderink, Valsecchi, van Doorn, Wagemans, & Gegenfurtner, 2017).…”

Section: Categorical Losslessnessmentioning

confidence: 99%

“…Experimentally, we closely follow the approach of Balas (2006), described above 1 . Using images that are all physically different measures the extent to which model syntheses are categorically or structurally lossless (in that they could both be considered samples from original images; Pappas, 2013), as opposed to being perceptually lossless (unable to be told apart) compared either to each other (Freeman & Simoncelli, 2011) or the original source images (Wallis, Bethge, & Wichmann, 2016). Perceptual losslessness could be important for understanding visual encoding in general but categorical losslessness is arguably more useful for understanding the perceptual representation of texture.…”

Section: Studying Texture Perception With Parametric Texture Modelsmentioning

confidence: 99%

“…Where surfaces of different textures form occlusion boundaries, texture can provide a powerful segmentation cue; conversely, occlusion borders of similarly-textured surfaces can camoflage the occlusion (hiding a tiger among the leaves). Given the importance and ubiquity of visual textures, it is little wonder that they have received much scientific attention, not only from within vision science but also in computer vision, graphics and art (see Dakin, 2014;Landy, 2013;Pappas, 2013;Rosenholtz, 2014, for comprehensive recent reviews of this field).…”

Section: Introductionmentioning

confidence: 99%

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A parametric texture model based on deep convolutional features closely matches texture appearance for humans

Wallis

Funke

Ecker

et al. 2017

Preprint

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Our visual environment is full of texture-"stuff" like cloth, bark or gravel as distinct from "things" like dresses, trees or paths-and humans are adept at perceiving subtle variations in material properties. To investigate image features important for texture perception, we psychophysically compare a recent parameteric model of texture appearance (CNN model) that uses the features encoded by a deep convolutional neural network with two other models: the venerable Portilla and Simoncelli model (PS) and an extension of the CNN model in which the power spectrum is additionally matched. Observers discriminated model-generated textures from original natural textures in a spatial three-alternative oddity paradigm under two viewing conditions: when test patches were briefly presented to the near-periphery ("parafoveal") and when observers were able to make eye movements to all three patches ("inspection"). Under parafoveal viewing, observers were unable to discriminate 10 of 12 original images from CNN model images, and remarkably, the simpler PS model performed slightly better than the CNN model (11 textures). Under foveal inspection, matching CNN features captured appearance substantially better than the PS model (9 compared to 4 textures), and including the power spectrum improved appearance matching for two of the three remaining textures. None of the models we test here could produce indiscriminable images for one of the 12 textures under the inspection condition. While deep CNN (VGG-19) features can often be used to synthesise textures that humans cannot discriminate from natural textures, there is currently no uniformly best model for all textures and viewing conditions. Keywords: spatial vision, natural scenes, texture perception, peripheral vision . CC-BY 4.0 International license not peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/165761 doi: bioRxiv preprint first posted online IntroductionTextures are characterised by the repetition of smaller elements, sometimes with variation, to make up a pattern. Significant portions of the visual environment can be thought of as textures ("stuff" as distinct from "things"; Adelson & Bergen, 1991): your neighbour's pink floral wallpaper, the internal structure of dark German bread, the weave of a wicker basket, the gnarled bark of an old tree trunk, a bowl full of prawns ready for the barbie. Texture is an important material property whose perception is of adaptive value (Adelson, 2001;Fleming, 2014). For example, we can readily discriminate wet from dry stones (e.g. Ho, Landy, & Maloney, 2008), separating the underlying spatial texture from potentially temporary characteristics like glossiness. Where surfaces of different textures form occlusion boundaries, texture can provide a powerful segmentation cue; conversely, occlusion borders of similarly-textured surfaces can camoflage the occlusion (hiding a tiger among the leaves). Given the importance and ubiquity of visu...

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Cluster Indexing and GR Encoding with Similarity Measure for CBIR Applications

Shaila

Vadivel

2018

Textual and Visual Information Retrieval Using Query Refinement and Pattern Analysis

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The rough side of texture: texture analysis through the lens of HVEI

Cited by 6 publications

References 88 publications

A parametric texture model based on deep convolutional features closely matches texture appearance for humans

A parametric texture model based on deep convolutional features closely matches texture appearance for humans

A parametric texture model based on deep convolutional features closely matches texture appearance for humans

Cluster Indexing and GR Encoding with Similarity Measure for CBIR Applications

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