Understanding the relationships between aesthetic properties of shapes and geometric quantities of free-form curves and surfaces using Machine Learning Techniques

Abstract : Today on the market we can find a large variety of different products and differentshapes of the same product and this great choice overwhelms the customers. It is evident that the aesthetic appearance of the product shape and its emotional affection will lead the customers to the decision for buying the product. Therefore, it is very important to understand the aesthetic proper-ties and to adopt them in the early product design phases. The objective of this thesis is to propose a generic framework for mapping aesthetic properties to 3D freeform shapes, so as to be able to extract aesthetic classification rules and associated geometric properties. The key element of the proposed framework is the application of the Data Mining (DM) methodology and Machine Learning Techniques (MLTs) in the mapping of aesthetic properties to the shapes. The application of the framework is to investigate whether there is a common judgment for the flatness perceived from non-professional designers. The aim of the framework is not only to establish a structure for mapping aesthetic properties to free-form shapes, but also to be used as a guided path for identifying a mapping between different semantics and free-form shapes. The long-term objective of this work is to define a methodology to efficiently integrate the concept of Affective Engineering in the Industrial Designing.
Complete list of metadatas

Cited literature [172 references]  Display  Hide  Download

https://pastel.archives-ouvertes.fr/tel-01344873
Contributor : Abes Star <>
Submitted on : Tuesday, July 12, 2016 - 5:20:07 PM
Last modification on : Wednesday, November 21, 2018 - 3:16:38 AM

File

PETROV.pdf
Version validated by the jury (STAR)

Identifiers

  • HAL Id : tel-01344873, version 1

Collections

Citation

Aleksandar Petrov. Understanding the relationships between aesthetic properties of shapes and geometric quantities of free-form curves and surfaces using Machine Learning Techniques. Mechanical engineering [physics.class-ph]. Ecole nationale supérieure d'arts et métiers - ENSAM, 2016. English. ⟨NNT : 2016ENAM0007⟩. ⟨tel-01344873⟩

Share

Metrics

Record views

1089

Files downloads

1135