12 research outputs found

    Statistical relational learning of semantic models and grammar rules for 3D building reconstruction from 3D point clouds

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    Formal grammars are well suited for the estimation of models with an a-priori unknown number of parameters such as buildings and have proven their worth for 3D modeling and reconstruction of cities. However, the generation and design of corresponding grammar rules is a laborious task and relies on expert knowledge. This thesis presents novel approaches for the reduction of this effort using advanced machine learning methods resulting in automatically learned sophisticated grammar rules. Indeed, the learning of a wide range of sophisticated rules, that reflect the variety and complexity, is a challenging task. This is especially the case if a simultaneous machine learning of building structures and the underlying aggregation hierarchies as well as the building parameters and the constraints among them for a semantic interpretation is expected. Thus, in this thesis, an incremental approach is followed. It separates the structure learning from the parameter distribution learning of building parts. Moreover, the so far procedural approaches with formal grammars are mostly rather convenient for the generation of virtual city models than for the reconstruction of existing buildings. To this end, Inductive Logic Programming (ILP) techniques are transferred and applied for the first time in the field of 3D building modeling. This enables the automatic learning of declarative logic programs, which are equivalent to attribute grammars and separate the representation of buildings and their parts from the reconstruction task. A stepwise bottom-up learning, starting from the smallest atomic features of a building part together with the semantic, topological and geometric constraints, is a key to a successful learning of a whole building part. Only few examples are sufficient to learn from precise as well as noisy observations. The learning from uncertain data is realized using probability density functions, decision trees and uncertain projective geometry. This enables the handling and modeling of uncertain topology and geometric reasoning taking noise into consideration. The uncertainty of models itself is also considered. Therefore, a novel method is developed for the learning of Weighted Attribute Context-Free Grammar (WACFG). On the one hand, the structure learning of façades – context-free part of the Grammar – is performed based on annotated derivation trees using specific Support Vector Machines (SVMs). The latter are able to derive probabilistic models from structured data and to predict a most likely tree regarding to given observations. On the other hand, to the best of my knowledge, Statistical Relational Learning (SRL), especially Markov Logic Networks (MLNs), are applied for the first time in order to learn building part (shape and location) parameters as well as the constraints among these parts. The use of SRL enables to take profit from the elegant logical relational description and to benefit from the efficiency of statistical inference methods. In order to model latent prior knowledge and exploit the architectural regularities of buildings, a novel method is developed for the automatic identification of translational as well as axial symmetries. For symmetry identification a supervised machine learning approach is followed based on an SVM classifier. Building upon the classification results, algorithms are designed for the representation of symmetries using context-free grammars from authoritative building footprints. In all steps the machine learning is performed based on real- world data such as 3D point clouds and building footprints. The handling with uncertainty and occlusions is assured. The presented methods have been successfully applied on real data. The belonging classification and reconstruction results are shown.Statistisches relationales Lernen von semantischen Modellen und Grammatikregeln fĂŒr 3D GebĂ€uderekonstruktion aus 3D Punktwolken Formale Grammatiken eignen sich sehr gut zur SchĂ€tzung von Modellen mit a-priori unbekannter Anzahl von Parametern und haben sich daher als guter Ansatz zur Rekonstruktion von StĂ€dten mittels 3D Stadtmodellen bewĂ€hrt. Der Entwurf und die Erstellung der dazugehörigen Grammatikregeln benötigt jedoch Expertenwissen und ist mit großem Aufwand verbunden. Im Rahmen dieser Arbeit wurden Verfahren entwickelt, die diesen Aufwand unter Zuhilfenahme von leistungsfĂ€higen Techniken des maschinellen Lernens reduzieren und automatisches Lernen von Regeln ermöglichen. Das Lernen umfangreicher Grammatiken, die die Vielfalt und KomplexitĂ€t der GebĂ€ude und ihrer Bestandteile widerspiegeln, stellt eine herausfordernde Aufgabe dar. Dies ist insbesondere der Fall, wenn zur semantischen Interpretation sowohl das Lernen der Strukturen und Aggregationshierarchien als auch von Parametern der zu lernenden Objekte gleichzeitig statt finden soll. Aus diesem Grund wird hier ein inkrementeller Ansatz verfolgt, der das Lernen der Strukturen vom Lernen der Parameterverteilungen und Constraints zielfĂŒhrend voneinander trennt. Existierende prozedurale AnsĂ€tze mit formalen Grammatiken sind eher zur Generierung von synthetischen Stadtmodellen geeignet, aber nur bedingt zur Rekonstruktion existierender GebĂ€ude nutzbar. HierfĂŒr werden in dieser Schrift Techniken der Induktiven Logischen Programmierung (ILP) zum ersten Mal auf den Bereich der 3D GebĂ€udemodellierung ĂŒbertragen. Dies fĂŒhrt zum Lernen deklarativer logischer Programme, die hinsichtlich ihrer AusdrucksstĂ€rke mit attributierten Grammatiken gleichzusetzen sind und die ReprĂ€sentation der GebĂ€ude von der Rekonstruktionsaufgabe trennen. Das Lernen von zuerst disaggregierten atomaren Bestandteilen sowie der semantischen, topologischen und geometrischen Beziehungen erwies sich als SchlĂŒssel zum Lernen der Gesamtheit eines GebĂ€udeteils. Das Lernen erfolgte auf Basis einiger weniger sowohl prĂ€ziser als auch verrauschter Beispielmodelle. Um das Letztere zu ermöglichen, wurde auf Wahrscheinlichkeitsdichteverteilungen, EntscheidungsbĂ€umen und unsichere projektive Geometrie zurĂŒckgegriffen. Dies erlaubte den Umgang mit und die Modellierung von unsicheren topologischen Relationen sowie unscharfer Geometrie. Um die Unsicherheit der Modelle selbst abbilden zu können, wurde ein Verfahren zum Lernen Gewichteter Attributierter Kontextfreier Grammatiken (Weighted Attributed Context-Free Grammars, WACFG) entwickelt. Zum einen erfolgte das Lernen der Struktur von Fassaden –kontextfreier Anteil der Grammatik – aus annotierten HerleitungsbĂ€umen mittels spezifischer Support Vektor Maschinen (SVMs), die in der Lage sind, probabilistische Modelle aus strukturierten Daten abzuleiten und zu prĂ€dizieren. Zum anderen wurden nach meinem besten Wissen Methoden des statistischen relationalen Lernens (SRL), insbesondere Markov Logic Networks (MLNs), erstmalig zum Lernen von Parametern von GebĂ€uden sowie von bestehenden Relationen und Constraints zwischen ihren Bestandteilen eingesetzt. Das Nutzen von SRL erlaubt es, die eleganten relationalen Beschreibungen der Logik mit effizienten Methoden der statistischen Inferenz zu verbinden. Um latentes Vorwissen zu modellieren und architekturelle RegelmĂ€ĂŸigkeiten auszunutzen, ist ein Verfahren zur automatischen Erkennung von Translations- und Spiegelsymmetrien und deren ReprĂ€sentation mittels kontextfreier Grammatiken entwickelt worden. HierfĂŒr wurde mittels ĂŒberwachtem Lernen ein SVM-Klassifikator entwickelt und implementiert. Basierend darauf wurden Algorithmen zur Induktion von Grammatikregeln aus Grundrissdaten entworfen

    A generic housing grammar for the generation of different housing languages: a generic housing shape grammar for Palladian villas, Prairie and Malagueira houses

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    Shape grammars have traditionally described a design language and replicated it using a procedure. In the majority of existing studies, one language corresponded to one grammar and vice versa; the generative procedure was univocal and language specific. Generic grammars, which are capable of describing multiple design languages, potentially allow greater flexibility and help describe not only languages but relationships between languages. This study proposes a generic housing process based on a parametric shape grammar, and uses this to investigate relationships between several grammars or families of designs. A study case of three single housing grammars was selected using the Palladian villas, Prairie and Malagueira houses. Specific parameterisation confers the sense of style required to define a language. From the generated corpora two methods were exercised to explore two research questions: 1. A qualitative method tested how the parametric space of a shape grammar corresponded with our intuition of similarities and differences amongst designs. This was performed using a set of questionnaires posed to both laymen and expert observers. 2. A quantitative method was used to test how well the parametric space of a shape grammar coincided with the design space expressed by the different corpora. Principal Components Analysis was used to inform if the set of parameters used to design the solutions would group into clusters. Results indicate that the expected relationships between individual designs are captured by the generic grammar. The design solutions generated by the generic grammar were also naturally perceived by observers and clustering was identified amongst language related design solutions. A tool such as a generic shape grammar captures the principles of design as described by the generative shape rules and its parameterisation, which can be used in academia, practice or analysis to explore design

    University of Windsor Graduate Calendar 2022 Spring

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    https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1024/thumbnail.jp

    Logic and intuition in architectural modelling: philosophy of mathematics for computational design

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    This dissertation investigates the relationship between the shift in the focus of architectural modelling from object to system and philosophical shifts in the history of mathematics that are relevant to that change. Particularly in the wake of the adoption of digital computation, design model spaces are more complex, multidimensional, arguably more logical, less intuitive spaces to navigate, less accessible to perception and visual comprehension. Such spatial issues were encountered much earlier in mathematics than in architectural modelling, with the growth of analytical geometry, a transition from Classical axiomatic proofs in geometry as the basis of mathematics, to analysis as the underpinning of geometry. Can the computational design modeller learn from the changing modern history, philosophy and psychology of mathematics about the construction and navigation of computational geometrical architectural system model space? The research is conducted through a review of recent architectural project examples and reference to three more detailed architectural modelling case studies. The spatial questions these examples and case studies raise are examined in the context of selected historical writing in the history, philosophy and psychology of mathematics and space. This leads to conclusions about changes in the relationship of architecture and mathematics, and reflections on the opportunities and limitations for architectural system models using computation geometry in the light of this historical survey. This line of questioning was motivated as a response to the experience of constructing digital associative geometry models and encountering the apparent limits of their flexibility as the graph of dependencies grew and the messiness of the digital modelling space increased. The questions were inspired particularly by working on the Narthex model for the Sagrada Família church, which extends to many tens of thousands of relationships and constraints, and which was modelled and repeatedly partially remodelled over a very long period. This experience led to the realisation that the limitations of the model were not necessarily the consequence of poor logical schema definition, but could be inevitable limitations of the geometry as defined, regardless of the means of defining it, the ‘shape’ of the multidimensional space being created. This led to more fundamental questions about the nature of Space, its relationship to geometry and the extent to which the latter can be considered simply as an operational and notational system. This dissertation offers a purely inductive journey, offering evidence through very selective examples in architecture, architectural modelling and in the philosophy of mathematics. The journey starts with some questions about the tendency of the model space to break out and exhibit unpredictable and not always desirable behaviour and the opportunities for geometrical construction to solve these questions is not conclusively answered. Many very productive questions about computational architectural modelling are raised in the process of looking for answers

    University of Windsor Graduate Calendar 2022 Fall

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    https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1025/thumbnail.jp

    Automatic semantic and geometric enrichment of CityGML 3D building models of varying architectural styles with HOG-based template matching

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    While the number of 3D geo-spatial digital models of buildings with cultural heritage interest is burgeoning, most lack semantic annotation that could be used to inform users of mobile and desktop applications about the architectural features and origins of the buildings. Additionally, while automated reconstruction of 3D building models is an active research area, the labelling of architectural features (objects) is comparatively less well researched, while distinguishing between different architectural styles is less well researched still. Meanwhile, the successful automatic identification of architectural objects, typified by a comparatively less symmetrical or less regular distribution of objects on façades, particularly on older buildings, has so far eluded researchers. This research has addressed these issues by automating the semantic and geometric enrichment of existing 3D building models by using Histogram of Oriented Gradients (HOG)-based template matching. The methods are applied to the texture maps of 3D building models of 20th century styles, of Georgian-Regency (1715-1830) style and of the Norman (1066 to late 12th century) style, where the amalgam of styles present on buildings of the latter style necessitates detection of styles of the Gothic tradition (late 12th century to present day). The most successful results were obtained when applying a set of heuristics including the use of real world dimensions, while a Support Vector Machine (SVM)-based machine learning approach was found effective in obviating the need for thresholds on matchscores when making detection decisions

    University of Windsor Graduate Calendar 2023 Winter

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    https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1026/thumbnail.jp

    University of Windsor Graduate Calendar 2023 Spring

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    https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1027/thumbnail.jp
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