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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.


Related in: MedlinePlus

Magnitude of correlation coefficients between the 3 axes ((A) X, (B) Y and (C) Z) in the perceptual texture space and the average scales of 12 perceptual features for eight subsets.
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pone.0130335.g007: Magnitude of correlation coefficients between the 3 axes ((A) X, (B) Y and (C) Z) in the perceptual texture space and the average scales of 12 perceptual features for eight subsets.

Mentions: To gain insight into the perceptual dimensions for different subsets, we plotted the correlation of all 8 subsets for each axe without considering the polarity of correlation. As can be seen in Fig 7a, for axis X, the significant correlation occurred at the features “feature density” and “coarse” for all the subsets. In Fig 7b, for subsets 1, 5 and 7, axis Y was significantly correlated with the features “repetitive”, “random”, “direction”, “regular”, “oriented” and “uniform”. For others, the correlations were not obvious, but it seemed that “contrast” and “structural complexity” were the common features related to axis Y. In Fig 7c, for subsets 1, 5 and 7, axis Z correlated with the features “contrast”, “granular” and “structural complexity”, while the others significantly correlated with the features “repetitive”, “random”, “direction”, “regular”, “oriented” and “uniform”.


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)

Magnitude of correlation coefficients between the 3 axes ((A) X, (B) Y and (C) Z) in the perceptual texture space and the average scales of 12 perceptual features for eight subsets.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0130335.g007: Magnitude of correlation coefficients between the 3 axes ((A) X, (B) Y and (C) Z) in the perceptual texture space and the average scales of 12 perceptual features for eight subsets.
Mentions: To gain insight into the perceptual dimensions for different subsets, we plotted the correlation of all 8 subsets for each axe without considering the polarity of correlation. As can be seen in Fig 7a, for axis X, the significant correlation occurred at the features “feature density” and “coarse” for all the subsets. In Fig 7b, for subsets 1, 5 and 7, axis Y was significantly correlated with the features “repetitive”, “random”, “direction”, “regular”, “oriented” and “uniform”. For others, the correlations were not obvious, but it seemed that “contrast” and “structural complexity” were the common features related to axis Y. In Fig 7c, for subsets 1, 5 and 7, axis Z correlated with the features “contrast”, “granular” and “structural complexity”, while the others significantly correlated with the features “repetitive”, “random”, “direction”, “regular”, “oriented” and “uniform”.

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.


Related in: MedlinePlus