Computational aesthetics in architecture: a framework for quantifying preferences using computer vision and artificial neural networks
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Tipo
Tese
Data de publicação
2024-07-04
Periódico
Citações (Scopus)
Autores
Sardenberg, Victor Carrilho
Orientador
Becker, Mirco
Título da Revista
ISSN da Revista
Título de Volume
Membros da banca
Hoffman, Holger
Nolte, Tobias
Castro, Luiz Guilherme Rivera de
Nolte, Tobias
Castro, Luiz Guilherme Rivera de
Programa
Arquitetura e Urbanismo
Resumo
This research develops a computational aesthetics framework to predict the hedonic response of
groups of people to architectural images. The theoretical basis relies on classical aesthetic theories of
parts to whole, such as Alberti´s, combined with early 20th-century quantitative aesthetic metrics by
G. D. Birkhoff and digitally-enabled contemporary technologies, such as Computer Vision (CV) and
Artificial Neural Networks (ANN).
This work focuses on the visual perception of architecture through perspectival images. CV is applied
to identify parts in images, such as walls, doors, and windows. These parts are reorganized in
diagrams to analyze the number of parts (DSP) and quantify their relations to the whole (DCG). The
quantities derived from the diagrams inform two methods for quantifying and predicting the hedonic
response of other images:
1. Birkhoff's Aesthetic Measure (AM) formula is adopted to reduce the complicated aesthetic
experience into numbers. CV is applied to automate it, speeding up its application and
making it unambiguous. The formula is calibrated to fit the audience's preferences better,
producing a Calibrated Aesthetic Measure (cAM).
2. ANNs are trained because of their ability to find patterns in data. The numerical output from
the DCG and DSP, the AM, and the cAM are used as inputs to train the model. This model is
named the Predicted Hedonic Response (PHR) model.
The described framework requires surveying specific audiences to incorporate their bias. Therefore,
this research does not aim to develop a universal model of aesthetic evaluation but to embrace the
specificity of each group of individuals. The framework is applied to navigate design spaces for
parametric models and generative adversarial networks. The thesis discusses the implications of
quantification in architectural evaluation, parts to whole relationship paradigms, and the role of
images and playing in architecture. Finally, it concludes that the computational aesthetics framework
build is a heuristic for predicting the aesthetic preferences of groups.
Descrição
Palavras-chave
computational aesthetics , artificial neural networks , aesthetic measure