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10.3389/fncom.2015.00134 http://scihub22266oqcxt.onion/10.3389/fncom.2015.00134 C4631837!4631837 !26582987     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26582987 &cmd=llinks): Failed to open stream: HTTP request failed! 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Aesthetic perception of visual textures: a holistic exploration using texture analysis, psychological experiment, and perception modeling |pdf=|usr=}}{{26582987 }} gab.com Text Aesthetic perception of visual textures: a holistic exploration using texture analysis, psychological experiment, and perception modeling JO: Front Comput Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=26582987 #FOAvip Modeling human aesthetic perception of visual textures is important and valuable in numerous industrial domains, such as product design, architectural design, and decoration. Based on results from a semantic differential rating experiment, we modeled the relationship between low-level basic texture features and aesthetic properties involved in human aesthetic texture perception. First, we compute basic texture features from textural images using four classical methods. These features are neutral, objective, and independent of the socio-cultural context of the visual textures. Then, we conduct a semantic differential rating experiment to collect from evaluators their aesthetic perceptions of selected textural stimuli. In semantic differential rating experiment, eights pairs of aesthetic properties are chosen, which are strongly related to the socio-cultural context of the selected textures and to human emotions. They are easily understood and connected to everyday life. We propose a hierarchical feed-forward layer model of aesthetic texture perception and assign 8 pairs of aesthetic properties to different layers. Finally, we describe the generation of multiple linear and non-linear regression models for aesthetic prediction by taking dimensionality-reduced texture features and aesthetic properties of visual textures as dependent and independent variables, respectively. Our experimental results indicate that the relationships between each layer and its neighbors in the hierarchical feed-forward layer model of aesthetic texture perception can be fitted well by linear functions, and the models thus generated can successfully bridge the gap between computational texture features and aesthetic texture properties. Twit Text FOAVip Aesthetic perception of visual textures: a holistic exploration using texture analysis, psychological experiment, and perception modeling ????Front Comput Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=26582987 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26582987 #FOAvip English Wikipedia <ref>{{Cite pmid|26582987 }}</ref> Aesthetic perception of visual textures: a holistic exploration using texture analysis, psychological experiment, and perception modeling #MMPMID26582987 Liu J ; Lughofer E ; Zeng X Front Comput Neurosci 2015[]; 9 (ä): 134 PMID26582987 show ga Modeling human aesthetic perception of visual textures is important and valuable in numerous industrial domains, such as product design, architectural design, and decoration. Based on results from a semantic differential rating experiment, we modeled the relationship between low-level basic texture features and aesthetic properties involved in human aesthetic texture perception. First, we compute basic texture features from textural images using four classical methods. These features are neutral, objective, and independent of the socio-cultural context of the visual textures. Then, we conduct a semantic differential rating experiment to collect from evaluators their aesthetic perceptions of selected textural stimuli. In semantic differential rating experiment, eights pairs of aesthetic properties are chosen, which are strongly related to the socio-cultural context of the selected textures and to human emotions. They are easily understood and connected to everyday life. We propose a hierarchical feed-forward layer model of aesthetic texture perception and assign 8 pairs of aesthetic properties to different layers. Finally, we describe the generation of multiple linear and non-linear regression models for aesthetic prediction by taking dimensionality-reduced texture features and aesthetic properties of visual textures as dependent and independent variables, respectively. Our experimental results indicate that the relationships between each layer and its neighbors in the hierarchical feed-forward layer model of aesthetic texture perception can be fitted well by linear functions, and the models thus generated can successfully bridge the gap between computational texture features and aesthetic texture properties. äAesthetic perception of visual textures: a holistic exploration using (epmc).pdf DeepDyve Pubget Overpricing