title: Visual perception of procedural textures: identifying perceptual dimensions and predicting generation models.
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.
authors.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.
authors.forename: Jun
authors.initials: J
authors.lastname: Liu
authors.affiliation: Department of Computer Science and Technology, Ocean University of China, 238 Songling Road, Qingdao, Shandong, China.
authors.forename: Junyu
authors.initials: J
authors.lastname: Dong
authors.affiliation: Department of Computer Science and Technology, Ocean University of China, 238 Songling Road, Qingdao, Shandong, China.
authors.forename: Xiaoxu
authors.initials: X
authors.lastname: Cai
authors.affiliation: Department of Computer Science and Technology, Ocean University of China, 238 Songling Road, Qingdao, Shandong, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Qi
authors.affiliation: Computer Science Department, Heriot-Watt University, Edinburgh, Scotland.
authors.forename: Mike
authors.initials: M
authors.lastname: Chantler
mesh_terms: D016000:Cluster Analysis; D003116:Color; D005556:Form Perception; D017709:Fractals; D006801:Humans; D021621:Imaging, Three-Dimensional; D008962:Models, Theoretical; D010364:Pattern Recognition, Visual; D011601:Psychophysics / Q000379:methods*; D012044:Regression Analysis; D013028:Space Perception; D014796:Visual Perception