Camera Marker Networks for Pose Estimation and Scene Understanding in Construction Automation and Robotics.

The construction industry faces challenges that include high workplace injuries and fatalities, stagnant productivity, and skill shortage. Automation and Robotics in Construction (ARC) has been proposed in the literature as a potential solution that makes machinery easier to collaborate with, facilitates better decision-making, or enables autonomous behavior. However, there are two primary technical challenges in ARC: 1) unstructured and featureless environments; and 2) differences between the as-designed and the as-built. It is therefore impossible to directly replicate conventional automation methods adopted in industries such as manufacturing on construction sites. In particular, two fundamental problems, pose estimation and scene understanding, must be addressed to realize the full potential of ARC. This dissertation proposes a pose estimation and scene understanding framework that addresses the identified research gaps by exploiting cameras, markers, and planar structures to mitigate the identified technical challenges. A fast plane extraction algorithm is developed for efficient modeling and understanding of built environments. A marker registration algorithm is designed for robust, accurate, cost-efficient, and rapidly reconfigurable pose estimation in unstructured and featureless environments. Camera marker networks are then established for unified and systematic design, estimation, and uncertainty analysis in larger scale applications. The proposed algorithms' efficiency has been validated through comprehensive experiments. Specifically, the speed, accuracy and robustness of the fast plane extraction and the marker registration have been demonstrated to be superior to existing state-of-the-art algorithms. These algorithms have also been implemented in two groups of ARC applications to demonstrate the proposed framework's effectiveness, wherein the applications themselves have significant social and economic value. The first group is related to in-situ robotic machinery, including an autonomous manipulator for assembling digital architecture designs on construction sites to help improve productivity and quality; and an intelligent guidance and monitoring system for articulated machinery such as excavators to help improve safety. The second group emphasizes human-machine interaction to make ARC more effective, including a mobile Building Information Modeling and way-finding platform with discrete location recognition to increase indoor facility management efficiency; and a 3D scanning and modeling solution for rapid and cost-efficient dimension checking and concise as-built modeling.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113481/1/cforrest_1.pd

Augmented reality over maps

Dissertação de mestrado integrado em Engenharia InformáticaMaps and Geographic Information System (GIS) play a major role in modern society, particularly on tourism, navigation and personal guidance. However, providing geographical information of interest related to individual queries remains a strenuous task. The main constraints are (1) the several information scales available, (2) the large amount of information available on each scale, and (3) difficulty in directly infer a meaningful geographical context from text, pictures, or diagrams that are used by most user-aiding systems. To that extent, and to overcome the aforementioned difficulties, we develop a solution which allows the overlap of visual information over the maps being queried — a method commonly referred to as Augmented Reality (AR). With that in mind, the object of this dissertation is the research and implementation of a method for the delivery of visual cartographic information over physical (analogue) and digital two-dimensional (2D) maps utilizing AR. We review existing state-of-art solutions and outline their limitations across different use cases. Afterwards, we provide a generic modular solution for a multitude of real-life applications, to name a few: museums, fairs, expositions, and public street maps. During the development phase, we take into consideration the trade-off between speed and accuracy in order to develop an accurate and real-time solution. Finally, we demonstrate the feasibility of our methods with an application on a real use case based on a map of the city of Oporto, in Portugal.Mapas e Sistema de Informação Geográfica (GIS) desempenham um papel importante na sociedade, particularmente no turismo, navegação e orientação pessoal. No entanto, fornecer informações geográficas de interesse a consultas dos utilizadores é uma tarefa árdua. Os principais dificuldades são (1) as várias escalas de informações disponíveis, (2) a grande quantidade de informação disponível em cada escala e (3) dificuldade em inferir diretamente um contexto geográfico significativo a partir dos textos, figuras ou diagramas usados. Assim, e para superar as dificuldades mencionadas, desenvolvemos uma solução que permite a sobreposição de informações visuais sobre os mapas que estão a ser consultados - um método geralmente conhecido como Realidade Aumentada (AR). Neste sentido, o objetivo desta dissertação é a pesquisa e implementação de um método para a visualização de informações cartográficas sobre mapas 2D físicos (analógicos) e digitais utilizando AR. Em primeiro lugar, analisamos o estado da arte juntamente com as soluções existentes e também as suas limitações nas diversas utilizações possíveis. Posteriormente, fornecemos uma solução modular genérica para uma várias aplicações reais tais como: museus, feiras, exposições e mapas públicos de ruas. Durante a fase de desenvolvimento, tivemos em consideração o compromisso entre velocidade e precisão, a fim de desenvolver uma solução precisa que funciona em tempo real. Por fim, demonstramos a viabilidade de nossos métodos com uma aplicação num caso de uso real baseado num mapa da cidade do Porto (Portugal)

Unsupervised Detection of Emergent Patterns in Large Image Collections

With the advent of modern image acquisition and sharing technologies, billions of images are added to the Internet every day. This huge repository contains useful information, but it is very hard to analyze. If labeled information is available for this data, then supervised learning techniques can be used to extract useful information. Visual pattern mining approaches provide a way to discover visual structures and patterns in an image collection without the need of any supervision. The Internet contains images of various objects, scenes, patterns, and shapes. The majority of approaches for visual pattern discovery, on the other hand, find patterns that are related to object or scene categories.Emergent pattern mining techniques provide a way to extract generic, complex and hidden structures in images. This thesis describes research, experiments, and analysis conducted to explore various approaches to mine emergent patterns from image collections in an unsupervised way. These approaches are based on itemset mining and graph theoretic strategies. The itemset mining strategy uses frequent itemset mining and rare itemset mining techniques to discover patterns.The mining is performed on a transactional dataset which is obtained from the BoW representation of images. The graph-based approach represents visual word co-occurrences obtained from images in a co-occurrence graph.Emergent patterns form dense clusters in this graph that are extracted using normalized cuts. The patterns that are discovered using itemset mining approaches are:stripes and parallel lines;dots and checks;bright dots;single lines;intersections; and frames. The graph based approach revealed various interesting patterns, including some patterns that are related to object categories

Augmented reality for non-rigid surfaces

Augmented Reality (AR) is the process of integrating virtual elements in reality, often by mixing computer graphics into a live video stream of a real scene. It requires registration of the target object with respect to the cameras. To this end, some approaches rely on dedicated hardware, such as magnetic trackers or infra-red cameras, but they are too expensive and cumbersome to reach a large public. Others are based on specifically designed markers which usually look like bar-codes. However, they alter the look of objects to be augmented, thereby hindering their use in application for which visual design matters. Recent advances in Computer Vision have made it possible to track and detect objects by relying on natural features. However, no such method is commonly used in the AR community, because the maturity of available packages is not sufficient yet. As far as deformable surfaces are concerned, the choice is even more limited, mainly because initialization is so difficult. Our main contribution is therefore a new AR framework that can properly augment deforming surfaces in real-time. Its target platform is a standard PC and a single webcam. It does not require any complex calibration procedure, making it perfectly suitable for novice end-users. To satisfy to the most demanding application designers, our framework does not require any scene engineering, renders virtual objects illuminated by real light, and let real elements occlude virtual ones. To meet this challenge, we developed several innovative techniques. Our approach to real-time registration of a deforming surface is based on wide-baseline feature matching. However, traditional outlier elimination techniques such as RANSAC are unable to handle the non-rigid surface's large number of degrees of freedom. We therefore proposed a new robust estimation scheme that allows both 2–D and 3–D non-rigid surface registration. Another issue of critical importance in AR to achieve realism is illumination handling, for which existing techniques often require setup procedures or devices such as reflective spheres. By contrast, our framework includes methods to estimate illumination for rendering purposes without sacrificing ease of use. Finally, several existing approaches to handling occlusions in AR rely on multiple cameras or can only deal with occluding objects modeled beforehand. Our requires only one camera and models occluding objects at runtime. We incorporated these components in a consistent and flexible framework. We used it to augment many different objects such as a deforming T-shirt or a sheet of paper, under challenging conditions, in real-time, and with correct handling of illumination and occlusions. We also used our non-rigid surface registration technique to measure the shape of deformed sails. We validated the ease of deployment of our framework by distributing a software package and letting an artist use it to create two AR applications

Script Effects as the Hidden Drive of the Mind, Cognition, and Culture

This open access volume reveals the hidden power of the script we read in and how it shapes and drives our minds, ways of thinking, and cultures. Expanding on the Linguistic Relativity Hypothesis (i.e., the idea that language affects the way we think), this volume proposes the “Script Relativity Hypothesis” (i.e., the idea that the script in which we read affects the way we think) by offering a unique perspective on the effect of script (alphabets, morphosyllabaries, or multi-scripts) on our attention, perception, and problem-solving. Once we become literate, fundamental changes occur in our brain circuitry to accommodate the new demand for resources. The powerful effects of literacy have been demonstrated by research on literate versus illiterate individuals, as well as cross-scriptal transfer, indicating that literate brain networks function differently, depending on the script being read. This book identifies the locus of differences between the Chinese, Japanese, and Koreans, and between the East and the West, as the neural underpinnings of literacy. To support the “Script Relativity Hypothesis”, it reviews a vast corpus of empirical studies, including anthropological accounts of human civilization, social psychology, cognitive psychology, neuropsychology, applied linguistics, second language studies, and cross-cultural communication. It also discusses the impact of reading from screens in the digital age, as well as the impact of bi-script or multi-script use, which is a growing trend around the globe. As a result, our minds, ways of thinking, and cultures are now growing closer together, not farther apart. ; Examines the origin, emergence, and co-evolution of written language, the human mind, and culture within the purview of script effects Investigates how the scripts we read over time shape our cognition, mind, and thought patterns Provides a new outlook on the four representative writing systems of the world Discusses the consequences of literacy for the functioning of the min

Dubbing Wordplay in Children’s Programmes from English into Thai

This doctoral research aims to investigate the most prevalent translation techniques adopted by Thai dubbing translators when transferring English-language idioms found in animated films into a lesser-known language such as Thai. To achieve this purpose, the methodological approach combines a quantitative phase, which has the benefit of revealing certain tendencies, with a qualitative phase that investigates the data in greater depth. Wordplay instances can be grouped into two main categories according to their presentation nature: media-based and rhetoric-based. In the case of the media-based category, the types of wordplay instances uncovered in the analysis are audio-verbal, audio-visual-verbal and visual-verbal, while, based in the rhetoric-based category, they are homonymy, homophony, paraphony, hahaphony and allusion types. In an attempt to render ST puns into the TT, the following seven dubbing techniques have been activated by Thai translators: loan, literal translation, explicitation, substitution, recreation, combination and non-translation. Close examination of the data reveals that, despite the translators’ best effort to transfer the semantic ambiguity and humorous effect embedded in the English wordplay into the Thai dialogue, PUN>NON-PUN is the translation outcome with the highest occurrence. This results in the inevitable loss of semantic ambiguity and humour in the TT wordplay, as well as other pedagogical objectives intended by the film’s producers such as a language learning facilitator for young viewers

Crossing Experiences in Digital Epigraphy: From Practice to Discipline

Although a relevant number of projects digitizing inscriptions are under development or have been recently accomplished, Digital Epigraphy is not yet considered to be a proper discipline and there are still no regular occasions to meet and discuss. By collecting contributions on nineteen projects – very diversified for geographic and chronological context, for script and language, and for typology of digital output – this volume intends to point out the methodological issues which are specific to the application of information technologies to epigraphy. The first part of the volume is focused on data modelling and encoding, which are conditioned by the specific features of different scripts and languages, and deeply influence the possibility to perform searches on texts and the approach to the lexicographic study of such under-resourced languages. The second part of the volume is dedicated to the initiatives aimed at fostering aggregation, dissemination and the reuse of epigraphic materials, and to discuss issues of interoperability. The common theme of the volume is the relationship between the compliance with the theoretic tools and the methodologies developed by each different tradition of studies, and, on the other side, the necessity of adopting a common framework in order to produce commensurable and shareable results. The final question is whether the computational approach is changing the way epigraphy is studied, to the extent of renovating the discipline on the basis of new, unexplored questions