Measuring streetscape complexity based on the statistics of local contrast and spatial frequency
Streetscapes are basic urban elements which play a major role in the livability of a city. The visual complexity of streetscapes is known to influence how people behave in such built spaces. However, how and which characteristics of a visual scene influence our perception of complexity have yet to b...
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| Veröffentlicht in: | PloS one 2014-02, Vol.9 (2), p.e87097-e87097 |
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| creator | Cavalcante, André Mansouri, Ahmed Kacha, Lemya Barros, Allan Kardec Takeuchi, Yoshinori Matsumoto, Naoji Ohnishi, Noboru |
| description | Streetscapes are basic urban elements which play a major role in the livability of a city. The visual complexity of streetscapes is known to influence how people behave in such built spaces. However, how and which characteristics of a visual scene influence our perception of complexity have yet to be fully understood. This study proposes a method to evaluate the complexity perceived in streetscapes based on the statistics of local contrast and spatial frequency. Here, 74 streetscape images from four cities, including daytime and nighttime scenes, were ranked for complexity by 40 participants. Image processing was then used to locally segment contrast and spatial frequency in the streetscapes. The statistics of these characteristics were extracted and later combined to form a single objective measure. The direct use of statistics revealed structural or morphological patterns in streetscapes related to the perception of complexity. Furthermore, in comparison to conventional measures of visual complexity, the proposed objective measure exhibits a higher correlation with the opinion of the participants. Also, the performance of this method is more robust regarding different time scenarios. |
| doi_str_mv | 10.1371/journal.pone.0087097 |
| format | Article |
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The visual complexity of streetscapes is known to influence how people behave in such built spaces. However, how and which characteristics of a visual scene influence our perception of complexity have yet to be fully understood. This study proposes a method to evaluate the complexity perceived in streetscapes based on the statistics of local contrast and spatial frequency. Here, 74 streetscape images from four cities, including daytime and nighttime scenes, were ranked for complexity by 40 participants. Image processing was then used to locally segment contrast and spatial frequency in the streetscapes. The statistics of these characteristics were extracted and later combined to form a single objective measure. The direct use of statistics revealed structural or morphological patterns in streetscapes related to the perception of complexity. Furthermore, in comparison to conventional measures of visual complexity, the proposed objective measure exhibits a higher correlation with the opinion of the participants. Also, the performance of this method is more robust regarding different time scenarios.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0087097</identifier><identifier>PMID: 24498292</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algeria ; Algorithms ; Analysis ; Cities ; Coding standards ; Complexity ; Computer Science ; Contrast Sensitivity ; Correlation analysis ; Engineering ; Entropy ; Environment Design - standards ; Environment Design - statistics & numerical data ; Female ; Frequency dependence ; Humans ; Image contrast ; Image processing ; Image Processing, Computer-Assisted - classification ; Image Processing, Computer-Assisted - standards ; Information science ; Japan ; Male ; Mathematics ; Measurement ; Medicine ; Neural networks ; Pattern Recognition, Visual ; Perception ; Perceptions ; Photography - classification ; Photography - standards ; Physiology ; Skewness ; Social and Behavioral Sciences ; Spatial discrimination ; Statistics ; Time Factors ; Visual perception</subject><ispartof>PloS one, 2014-02, Vol.9 (2), p.e87097-e87097</ispartof><rights>COPYRIGHT 2014 Public Library of Science</rights><rights>2014 Cavalcante et al. 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The visual complexity of streetscapes is known to influence how people behave in such built spaces. However, how and which characteristics of a visual scene influence our perception of complexity have yet to be fully understood. This study proposes a method to evaluate the complexity perceived in streetscapes based on the statistics of local contrast and spatial frequency. Here, 74 streetscape images from four cities, including daytime and nighttime scenes, were ranked for complexity by 40 participants. Image processing was then used to locally segment contrast and spatial frequency in the streetscapes. The statistics of these characteristics were extracted and later combined to form a single objective measure. The direct use of statistics revealed structural or morphological patterns in streetscapes related to the perception of complexity. 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The visual complexity of streetscapes is known to influence how people behave in such built spaces. However, how and which characteristics of a visual scene influence our perception of complexity have yet to be fully understood. This study proposes a method to evaluate the complexity perceived in streetscapes based on the statistics of local contrast and spatial frequency. Here, 74 streetscape images from four cities, including daytime and nighttime scenes, were ranked for complexity by 40 participants. Image processing was then used to locally segment contrast and spatial frequency in the streetscapes. The statistics of these characteristics were extracted and later combined to form a single objective measure. The direct use of statistics revealed structural or morphological patterns in streetscapes related to the perception of complexity. 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| subjects | Algeria Algorithms Analysis Cities Coding standards Complexity Computer Science Contrast Sensitivity Correlation analysis Engineering Entropy Environment Design - standards Environment Design - statistics & numerical data Female Frequency dependence Humans Image contrast Image processing Image Processing, Computer-Assisted - classification Image Processing, Computer-Assisted - standards Information science Japan Male Mathematics Measurement Medicine Neural networks Pattern Recognition, Visual Perception Perceptions Photography - classification Photography - standards Physiology Skewness Social and Behavioral Sciences Spatial discrimination Statistics Time Factors Visual perception |
| title | Measuring streetscape complexity based on the statistics of local contrast and spatial frequency |
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