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Understanding the Relationships Between Aesthetic Properties Of Understanding the relationships between aesthetic properties of shapes and geometric quantities of free-form curves and surfaces using Machine Learning Techniques Aleksandar Petrov To cite this version: Aleksandar Petrov. Understanding the relationships between aesthetic properties of shapes and ge- ometric 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 HAL Id: tel-01344873 https://pastel.archives-ouvertes.fr/tel-01344873 Submitted on 12 Jul 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. N°: 2009 ENAM XXXX 2016-ENAM-0007 PhD THESIS in cotutelle To obtain the degree of Docteur de l’Arts et Métiers ParisTech Spécialité “Mécanique – Conception” École doctorale n° 432: “Science des Métiers de l’ingénieur” and Dottore di Ricerca della Università degli Studi di Genova Specialità “Ingeneria Meccanica” Scuola di Dottorato: “Scienze e tecnologie per l’ingegneria” ciclo XXVI Presented and defended publicly by Aleksandar PETROV January 25th, 2016 Understanding the relationships between aesthetic properties of shapes and geometric quantities of free-form curves and surfaces using Machine Learning Techniques Director of thesis: Philippe VÉRON Co-director of thesis: Franca GIANNINI T Co-supervisors of the thesis: Jean-Philippe PERNOT, Bianca FALCIDIENO H È Jury M. Jean-François OMHOVER, Associate Professor, CPI, Arts et Méties ParisTech President S M. Umberto CUGINI, Professor, KAEMaRT, Polytechnico di Milano Reviewer Mme Géraldine MORIN, Associate Professor, INP, Université de Toulouse Reviewer E Mme Rezia MOLFINO, Professor, PMAR, Università degli Studi di Genova Examiner Arts et Métiers ParisTech – Centre d’Aix-en-Provence, LSIS, France Università degli Studi di Genova, CNR-IMATI, Genova, Italia To my parents They would have been proud EXPLOITATION DE TECHNIQUES D’APPRENTISSAGE ARTIFICIEL POUR LA COMPREHENSION DES LIENS ENTRE LES PROPRIETES ESTHETIQUES DES FORMES ET LES GRANDEURS GEOMETRIQUES DE COURBES ET SURFACE GAUCHES RESUME: Aujourd’hui, sur le marché, on peut trouver une vaste gamme de produits différents ou des formes variées d’un même produit et ce grand assortiment fatigue les clients. Il est clair que la décision des clients d’acheter un produit dépend de l'aspect esthétique de la forme du produit et de l’affection émotionnelle. Par conséquent, il est très important de comprendre les propriétés esthétiques et de les adopter dans la conception du produit, dès le début. L'objectif de cette thèse est de proposer un cadre générique pour la cartographie des propriétés esthétiques des formes gauches en 3D en façon d'être en mesure d’extraire des règles de classification esthétiques et des propriétés géométriques associées. L'élément clé du cadre proposé est l'application des méthodologies de l’Exploration des données (Data Mining) et des Techniques d’apprentissage automatiques (Machine Learning Techniques) dans la cartographie des propriétés esthétiques des formes. L'application du cadre est d'étudier s’il y a une opinion commune pour la planéité perçu de la part des concepteurs non-professionnels. Le but de ce cadre n'est pas seulement d’établir une structure pour repérer des propriétés esthétiques des formes gauches, mais aussi pour être utilisé comme un chemin guidé pour l’identification d’une cartographie entre les sémantiques et les formes gauches différentes. L'objectif à long terme de ce travail est de définir une méthodologie pour intégrer efficacement le concept de l’Ingénierie affective (c.à.d. Affective Engineering) dans le design industriel. Mots clés : Courbes et surfaces gauches, techniques d'apprentissage automatiques, propriétés esthétiques, ingénierie affective, design industriel. 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 different shapes 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 free- form 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. Keywords : Free-form curves and surfaces, machine learning techniques, aesthetic properties, affective engineering, industrial design. 6 Chapter 1 Introduction 1 Introduction Nowadays, it is commonly admitted that the aesthetic appearance of a product has an emphasized role in its commercial success. Today, on the market, we can find a large variety of different products and different shapes of the same product and this abundance of choic- es overwhelms the customer. At the beginning, the decision for buying a product is generally based on three criteria: Functionality, Price and Quality (Figure 1.1). By giving some priority to one of these criteria we can easily decide which product we are going to buy. For in- stance, if we take the price as a main criterion, we can find many products that satisfy the functionality with an acceptable quality for the same price. In such situation, how to make a choice? Actually, the aesthetic appearance of the product plays a key role in its commercial success. Therefore, understanding and manipulating the aesthetic properties of shapes and its visual impression in the early design phases has become a very attractive field of re- search. Figure 1.1: Reasoning about the relationships between the product shape and its properties As a consequence, designing products with attractive shapes requires knowledge about the feelings and impression the products evoke on the customers that are also the 7 end-users. Understanding such affective influence of the product shape in the product de- sign process requires the use of appropriate methods that can extract and transform subjec- tive impressions about a product into concrete design parameters and tools, referred to as Affective Engineering (AE). AE is actually a new aspect integrated in the product design pro- cess that provides a platform where emotional features are incorporated into design appeal- ing products (Nagamachi, 2011). The final and long-term objective of the AE is to define di- rect mapping between surface shape and emotions. Giannini et al. (Giannini & Monti, A survey of tools for understending and exploiting the link between shape and emotion in product design, 2010) provides an overview of the most common AE methodologies applied to investigate the relationships between shape features and emotions from various discipli- nary perspectives, including psychology and computer science. The objectives of this thesis are in the scope of Affective Engineering. Affective Engineering deals with perception of shapes, which refers to very complex emotional-intuitive mechanisms that capture, organize, identify and interpret the sensory information in the nervous system. The perception is sometimes described as a process of constructing mental representations of the sensory information, shaped by knowledge, memory, expectation and attention. Regarding the classification of shapes by perception, it is common that when people classify shapes, with respect to some properties, they intuitive- ly follow certain rules and changes of the surface shapes. Sometimes, these rules can be ex- plicitly explained, but often they are implicit and affected by geometric properties of the shape. Aesthetic properties of shapes play a key role in the perceptual impression of shapes. However, those properties are not yet well defined for surfaces and even less mapped to surface characteristics. Moreover, trying to define the aesthetic properties and map them with surface characteristics while using classical observation techniques is practically impos- sible. Those mechanisms are very complex and involve many factors. Therefore, finding the direct relationships between aesthetic properties
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