How can century-old architectural hierarchies for the design of public libraries be re-interpreted and re-used?

Purpose The purpose of this paper is to describe a novel approach to inform heritage conservation based on the effective integration of documentation-based research with advanced survey methods for the creation of a sharable historic building information modelling (HBIM) objects database, specifically oriented to the study of Carnegie libraries whose designs in the USA and the UK were somewhat systematised by early principles of standardisation. The aim is to generate an exemplar developing new methodologies for the salvage, re-use and re-invigoration of shared inherited public buildings which have many common and standardized features. Design/methodology/approach This project will also involve the collaboration of conservation practice and digital recording together with library history. Digital laser scanning and structure from motion will be used together with archival documents to accurately build an information-rich framework for CAD and building information modelling applications. Findings By providing the base elements for the semi-automatic generation of a wide variety of morphological typologies and construction elements, this work ultimately promotes a shift towards the implementation of HBIM to support the conservation, maintenance and management of a high number of insufficiently protected public buildings from the turn of the last century. Originality/value The intention is that the resulting multidimensional parametric object library will provide suitable support for the faster generation of enriched 3D historic models and ultimately support the preservation of a large proportion of the huge but threatened public library building heritage in the UK and USA

Fine Art Pattern Extraction and Recognition

This is a reprint of articles from the Special Issue published online in the open access journal Journal of Imaging (ISSN 2313-433X) (available at: https://www.mdpi.com/journal/jimaging/special issues/faper2020)

Report on shape analysis and matching and on semantic matching

In GRAVITATE, two disparate specialities will come together in one working platform for the archaeologist: the fields of shape analysis, and of metadata search. These fields are relatively disjoint at the moment, and the research and development challenge of GRAVITATE is precisely to merge them for our chosen tasks. As shown in chapter 7 the small amount of literature that already attempts join 3D geometry and semantics is not related to the cultural heritage domain. Therefore, after the project is done, there should be a clear ‘before-GRAVITATE’ and ‘after-GRAVITATE’ split in how these two aspects of a cultural heritage artefact are treated.This state of the art report (SOTA) is ‘before-GRAVITATE’. Shape analysis and metadata description are described separately, as currently in the literature and we end the report with common recommendations in chapter 8 on possible or plausible cross-connections that suggest themselves. These considerations will be refined for the Roadmap for Research deliverable.Within the project, a jargon is developing in which ‘geometry’ stands for the physical properties of an artefact (not only its shape, but also its colour and material) and ‘metadata’ is used as a general shorthand for the semantic description of the provenance, location, ownership, classification, use etc. of the artefact. As we proceed in the project, we will find a need to refine those broad divisions, and find intermediate classes (such as a semantic description of certain colour patterns), but for now the terminology is convenient – not least because it highlights the interesting area where both aspects meet.On the ‘geometry’ side, the GRAVITATE partners are UVA, Technion, CNR/IMATI; on the metadata side, IT Innovation, British Museum and Cyprus Institute; the latter two of course also playing the role of internal users, and representatives of the Cultural Heritage (CH) data and target user’s group. CNR/IMATI’s experience in shape analysis and similarity will be an important bridge between the two worlds for geometry and metadata. The authorship and styles of this SOTA reflect these specialisms: the first part (chapters 3 and 4) purely by the geometry partners (mostly IMATI and UVA), the second part (chapters 5 and 6) by the metadata partners, especially IT Innovation while the joint overview on 3D geometry and semantics is mainly by IT Innovation and IMATI. The common section on Perspectives was written with the contribution of all

Institutional Analysis of a Natural History Museum: Formation and dissemination of scientific knowledge

When the canonization of scientific knowledge is considered as a point of sociological inquiry the interrelations of popular culture, commerce, and enterprise become crucial elements to understanding how forms of knowledge are produced and reproduced. Scientific knowledge is an integral part of our social world. We use it to better understand the natural processes behind global climates, health issues, ecology and biology. And yet most individuals outside the field of production are unaware of the processes behind obtaining this knowledge. Museums with research and collections are an arena for this topic. Their operations persist in influencing the selection of knowledge forming fields of study in both direct and indirect ways. In this study, The Field Museum of Natural History is used as a focal point to analyze ways in which the operation of an institution coincides and/or conflicts with scientific research. Data was collected through direct personal observation, interviews and published financial and historical records. Results from this analysis show that institutional operations within a museum persist in influencing the selection of knowledge forming fields of study in both direct and indirect ways and add to the literature on scientific knowledge

Data-driven modelling of perceptual properties of 3D shapes

The recent surge in 3D content generation has led to the evolution of difficult to search, organise and re-use massive online 3D visual content libraries. We explore crowdsourcing and machine learning techniques to help alleviate these difficulties by focusing on the visual perceptual properties of 3D shapes. We study “style similarity” and “aesthetics” as two fundamental perceptual properties of 3D shapes and build data-driven models. We rely on crowdsourcing platforms to collect large number of human judgements on style matching and aesthetics of 3D shapes. The judgement data collected directly from humans is used to learn metrics of style matching and aesthetics. Our style similarity measure can be used to compute style distance between a pair of input 3D shapes. In contrast to previous work, we incorporate colour and texture in addition to geometric features to build a colour and texture aware style similarity metric. We also experiment with learning objective and personalised style metrics 3D shapes. The application prototypes we build demonstrate the use of style based search and scene composition. Further, our style distance metric is built iteratively to consume lesser amount of human style judgement data compared to previous methods. We study the problem of building a data-driven model of 3D shape aesthetics in two steps. We first focus on designing a study to crowdsource human aesthetics judgement data. We then formulate a deep learning based strategy to learn a measure of 3D shape aesthetics from collected data. The results of the study in first step helped us choose an appropriate shape representation i.e. voxels as an input to deep neural networks for learning a measure of visual aesthetics. In the same crowdsourcing study, we experiment with the use of polygonal, volumetric, and point based shape representations to create shape stimuli to collect and compare human shape aesthetics judgements. On analysis of the collected data we found that that humans can reliably distinguish more aesthetic shape in a pair even from coarser shape representations such as voxels. This observation implies that detailed shape representations are not needed to compare aesthetics in pairs. The aesthetic value of a 3D shape has traditionally been explored in terms of specific visual features (or handcrafted features) such as curvature and symmetry. For example, more symmetric and curved shapes are considered aesthetic compared to less curved and symmetric shapes. We call such properties as pre-existing notion (or rules) of aesthetics. In order to develop a measure of perceptual aesthetics of 3D shapes which is independent of any pre-existing notion or shape features, we train deep neural networks directly on human aesthetics judgement data. We demonstrate the usefulness of the learned measure by designing applications to rank a collection of shapes based on their aesthetics scores and interactively build scenes using shapes with high aesthetics scores. The overarching goal of this thesis is to demonstrate the use of machine learning and crowdsourcing approaches to build data-driven models of visual perceptual properties of 3D shapes for applications in search, organisation, scene composition, and visualisation of 3D shape data present in ever increasing online 3D shape content libraries. We believe that our exploration of perceptual properties of 3D shapes will motivate further research by looking into other important perceptual properties related to our vision system and will also fuel development of techniques to automatically enhance such properties of a given 3D shape

Driving brand growth in the chinese beauty industry through licensing agreements: L'Oréal and the Armani case study

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An aesthetics of touch: investigating the language of design relating to form

How well can designers communicate qualities of touch? This paper presents evidence that they have some capability to do so, much of which appears to have been learned, but at present make limited use of such language. Interviews with graduate designer-makers suggest that they are aware of and value the importance of touch and materiality in their work, but lack a vocabulary to fully relate to their detailed explanations of other aspects such as their intent or selection of materials. We believe that more attention should be paid to the verbal dialogue that happens in the design process, particularly as other researchers show that even making-based learning also has a strong verbal element to it. However, verbal language alone does not appear to be adequate for a comprehensive language of touch. Graduate designers-makers’ descriptive practices combined non-verbal manipulation within verbal accounts. We thus argue that haptic vocabularies do not simply describe material qualities, but rather are situated competences that physically demonstrate the presence of haptic qualities. Such competencies are more important than groups of verbal vocabularies in isolation. Design support for developing and extending haptic competences must take this wide range of considerations into account to comprehensively improve designers’ capabilities