## Details

Originalsprache | Englisch |
---|---|

Titel des Sammelwerks | Proceedings of the 2023 Annual Modeling and Simulation Conference, ANNSIM 2023 |

Herausgeber/-innen | Maria Julia Blas, Gonzalo Alvarez |

Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |

Seiten | 496-507 |

Seitenumfang | 12 |

ISBN (elektronisch) | 9781713873280 |

Publikationsstatus | Veröffentlicht - 2023 |

Veranstaltung | Annual Modeling and Simulation Conference, ANNSIM 2023 - Hamilton, Kanada Dauer: 23 Mai 2023 → 26 Mai 2023 |

## Abstract

A computational framework is proposed to quantify the aesthetic experience of perceiving architecture. This framework is used as the criterion for navigating a design solution space. A parametric model's solution space is represented as a set of computer-generated images. We utilize computer vision to recognize parts and the relations between parts and the whole. These values are inserted into an adaptation of Birkhoff's aesthetic measure formula, calibrated via crowdsourced hedonic response. An artificial neural network (ANN) model is trained to predict the hedonic response of images using (A) parts relations as inputs and (B) the average hedonic responses as target outputs. The prediction of the ANN is used as a fitness function for optimization. The same public evaluates the parametric model's output to compare the ANN model's effectiveness in predicting their response. The method is useful in translating formal qualities into quantities and navigating solution spaces, especially with surrogate models.

## ASJC Scopus Sachgebiete

- Entscheidungswissenschaften (insg.)
**Informationssysteme und -management**- Mathematik (insg.)
**Steuerung und Optimierung**- Mathematik (insg.)
**Modellierung und Simulation**

## Zitieren

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- BibTex
- RIS

**Aesthetics as a Criterion: Navigating Solution Spaces Utilizing Computer Vision, the Aesthetic Measure, and Artificial Neural Networks.**/ Sardenberg, Victor; Becker, Mirco.

Proceedings of the 2023 Annual Modeling and Simulation Conference, ANNSIM 2023. Hrsg. / Maria Julia Blas; Gonzalo Alvarez. Institute of Electrical and Electronics Engineers Inc., 2023. S. 496-507.

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review

*Proceedings of the 2023 Annual Modeling and Simulation Conference, ANNSIM 2023.*Institute of Electrical and Electronics Engineers Inc., S. 496-507, Annual Modeling and Simulation Conference, ANNSIM 2023, Hamilton, Kanada, 23 Mai 2023.

*Proceedings of the 2023 Annual Modeling and Simulation Conference, ANNSIM 2023*(S. 496-507). Institute of Electrical and Electronics Engineers Inc..

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TY - GEN

T1 - Aesthetics as a Criterion

T2 - Annual Modeling and Simulation Conference, ANNSIM 2023

AU - Sardenberg, Victor

AU - Becker, Mirco

PY - 2023

Y1 - 2023

N2 - A computational framework is proposed to quantify the aesthetic experience of perceiving architecture. This framework is used as the criterion for navigating a design solution space. A parametric model's solution space is represented as a set of computer-generated images. We utilize computer vision to recognize parts and the relations between parts and the whole. These values are inserted into an adaptation of Birkhoff's aesthetic measure formula, calibrated via crowdsourced hedonic response. An artificial neural network (ANN) model is trained to predict the hedonic response of images using (A) parts relations as inputs and (B) the average hedonic responses as target outputs. The prediction of the ANN is used as a fitness function for optimization. The same public evaluates the parametric model's output to compare the ANN model's effectiveness in predicting their response. The method is useful in translating formal qualities into quantities and navigating solution spaces, especially with surrogate models.

AB - A computational framework is proposed to quantify the aesthetic experience of perceiving architecture. This framework is used as the criterion for navigating a design solution space. A parametric model's solution space is represented as a set of computer-generated images. We utilize computer vision to recognize parts and the relations between parts and the whole. These values are inserted into an adaptation of Birkhoff's aesthetic measure formula, calibrated via crowdsourced hedonic response. An artificial neural network (ANN) model is trained to predict the hedonic response of images using (A) parts relations as inputs and (B) the average hedonic responses as target outputs. The prediction of the ANN is used as a fitness function for optimization. The same public evaluates the parametric model's output to compare the ANN model's effectiveness in predicting their response. The method is useful in translating formal qualities into quantities and navigating solution spaces, especially with surrogate models.

KW - Artificial Neural Networks

KW - Computational Aesthetics

KW - Computer Vision

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M3 - Conference contribution

AN - SCOPUS:85165425554

SP - 496

EP - 507

BT - Proceedings of the 2023 Annual Modeling and Simulation Conference, ANNSIM 2023

A2 - Blas, Maria Julia

A2 - Alvarez, Gonzalo

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 23 May 2023 through 26 May 2023

ER -