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Visual perception of procedural textures: identifying perceptual dimensions and predicting generation models.

Liu J, Dong J, Cai X, Qi L, Chantler M - PLoS ONE (2015)

Bottom Line: The results suggested that existing dimensions in literature cannot accommodate random textures.We therefore utilized isometric feature mapping (Isomap) to establish a three-dimensional perceptual texture space which better explains the features used by humans in texture similarity judgment.Finally, we proposed computational models to map perceptual features to the perceptual texture space, which can suggest a procedural model to produce textures according to user-defined perceptual scales.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Technology, Ocean University of China, 238 Songling Road, Qingdao, Shandong, China; Science and Information College, Qingdao Agricultural University, 700 Changcheng Road, Qingdao, Shandong, China.

ABSTRACT
Procedural models are widely used in computer graphics for generating realistic, natural-looking textures. However, these mathematical models are not perceptually meaningful, whereas the users, such as artists and designers, would prefer to make descriptions using intuitive and perceptual characteristics like "repetitive," "directional," "structured," and so on. To make up for this gap, we investigated the perceptual dimensions of textures generated by a collection of procedural models. Two psychophysical experiments were conducted: free-grouping and rating. We applied Hierarchical Cluster Analysis (HCA) and Singular Value Decomposition (SVD) to discover the perceptual features used by the observers in grouping similar textures. The results suggested that existing dimensions in literature cannot accommodate random textures. We therefore utilized isometric feature mapping (Isomap) to establish a three-dimensional perceptual texture space which better explains the features used by humans in texture similarity judgment. Finally, we proposed computational models to map perceptual features to the perceptual texture space, which can suggest a procedural model to produce textures according to user-defined perceptual scales.

No MeSH data available.


Example samples in the dataset.
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pone.0130335.g001: Example samples in the dataset.

Mentions: In order to understand how humans naturally categorize texture surfaces, it is important to present them in imageries so that they can be envisaged as being of real surfaces [60]. Natural-like textures can be produced by using a ray tracing algorithm, which is able to simulate realistic lighting and a wide range of optical effects, the most important of these being inter-reflections. The resulted effects such as reflections and shadows will produce high degree of visual realism. We employed a physics-based rendering engine—LuxRender. Each height map of textured surface was rendered under Lambertian conditions and constant albedo; and all were rendered at the slant angle of 45° and tilt angle 135°. Each rendered texture was printed on a 4 * 4 inch photographic paper with the resolution of 128 pixels per inch. The advantage of using photos in the experiments was that subjects were able to look through the whole texture dataset, and it was more favorable for subjects to make judgments according to experimental requirements. Fig 1 shows some example textures from our dataset. The full list of samples was included in the supporting information(S1 Fig, S2 Fig, S3 Fig, S4 Fig, S5 Fig, S6 Fig).


Visual perception of procedural textures: identifying perceptual dimensions and predicting generation models.

Liu J, Dong J, Cai X, Qi L, Chantler M - PLoS ONE (2015)

Example samples in the dataset.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4481328&req=5

pone.0130335.g001: Example samples in the dataset.
Mentions: In order to understand how humans naturally categorize texture surfaces, it is important to present them in imageries so that they can be envisaged as being of real surfaces [60]. Natural-like textures can be produced by using a ray tracing algorithm, which is able to simulate realistic lighting and a wide range of optical effects, the most important of these being inter-reflections. The resulted effects such as reflections and shadows will produce high degree of visual realism. We employed a physics-based rendering engine—LuxRender. Each height map of textured surface was rendered under Lambertian conditions and constant albedo; and all were rendered at the slant angle of 45° and tilt angle 135°. Each rendered texture was printed on a 4 * 4 inch photographic paper with the resolution of 128 pixels per inch. The advantage of using photos in the experiments was that subjects were able to look through the whole texture dataset, and it was more favorable for subjects to make judgments according to experimental requirements. Fig 1 shows some example textures from our dataset. The full list of samples was included in the supporting information(S1 Fig, S2 Fig, S3 Fig, S4 Fig, S5 Fig, S6 Fig).

Bottom Line: The results suggested that existing dimensions in literature cannot accommodate random textures.We therefore utilized isometric feature mapping (Isomap) to establish a three-dimensional perceptual texture space which better explains the features used by humans in texture similarity judgment.Finally, we proposed computational models to map perceptual features to the perceptual texture space, which can suggest a procedural model to produce textures according to user-defined perceptual scales.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Technology, Ocean University of China, 238 Songling Road, Qingdao, Shandong, China; Science and Information College, Qingdao Agricultural University, 700 Changcheng Road, Qingdao, Shandong, China.

ABSTRACT
Procedural models are widely used in computer graphics for generating realistic, natural-looking textures. However, these mathematical models are not perceptually meaningful, whereas the users, such as artists and designers, would prefer to make descriptions using intuitive and perceptual characteristics like "repetitive," "directional," "structured," and so on. To make up for this gap, we investigated the perceptual dimensions of textures generated by a collection of procedural models. Two psychophysical experiments were conducted: free-grouping and rating. We applied Hierarchical Cluster Analysis (HCA) and Singular Value Decomposition (SVD) to discover the perceptual features used by the observers in grouping similar textures. The results suggested that existing dimensions in literature cannot accommodate random textures. We therefore utilized isometric feature mapping (Isomap) to establish a three-dimensional perceptual texture space which better explains the features used by humans in texture similarity judgment. Finally, we proposed computational models to map perceptual features to the perceptual texture space, which can suggest a procedural model to produce textures according to user-defined perceptual scales.

No MeSH data available.