Visual navigation is a ubiquitous yet complex task which is performed by many species for the purpose of survival. Although visual navigation is actively being studied within the robotics community, the determination of elemental constituents of a robust visual navigation system remains a challenge. Motion estimation is mistakenly considered as the sole ingredient to make a robust autonomous visual navigation system and therefore efforts are made to improve the accuracy of motion estimations. On the contrary, there are other factors which are as important as motion and whose absence could result in inability to perform seamless visual navigation such as the one exhibited by humans. Therefore, it is needed that a general model for a visual navigation system be devised which would describe it in terms of a set of elemental units. In this regard, a set of visual navigation elements (i.e. spatial memory, motion memory, scene geometry, context and scene semantics) are suggested as building blocks of a visual navigation system in this thesis. A set of methods are proposed which investigate the existence and role of visual navigation elements in a visual navigation system. A quantitative research methodology in the form of a series of systematic experiments is conducted on these methods. The thesis formulates, implements and analyzes the proposed methods in the context of visual navigation elements which are arranged into three major groupings; a) Spatial memory b) Motion Memory c) Manhattan, context and scene semantics. The investigations are carried out on multiple image datasets obtained by robot mounted cameras (2D/3D) moving in different environments. Spatial memory is investigated by evaluation of proposed place recognition methods. The recognized places and inter-place associations are then used to represent a visited set of places in the form of a topological map. Such a representation of places and their spatial associations models the concept of spatial memory. It resembles the humans’ ability of place representation and mapping for large environments (e.g. cities). Motion memory in a visual navigation system is analyzed by a thorough investigation of various motion estimation methods. This leads to proposals of direct motion estimation methods which compute accurate motion estimates by basing the estimation process on dominant surfaces. In everyday world, planar surfaces, especially the ground planes, are ubiquitous. Therefore, motion models are built upon this constraint. Manhattan structure provides geometrical cues which are helpful in solving navigation problems. There are some unique geometric primitives (e.g. planes) which make up an indoor environment. Therefore, a plane detection method is proposed as a result of investigations performed on scene structure. The method uses supervised learning to successfully classify the segmented clusters in 3D point-cloud datasets. In addition to geometry, the context of a scene also plays an important role in robustness of a visual navigation system. The context in which navigation is being performed imposes a set of constraints on objects and sections of the scene. The enforcement of such constraints enables the observer to robustly segment the scene and to classify various objects in the scene. A contextually aware scene segmentation method is proposed which classifies the image of a scene into a set of geometric classes. The geometric classes are sufficient for most of the navigation tasks. However, in order to facilitate the cognitive visual decision making process, the scene ought to be semantically segmented. The semantic of indoor scenes as well as semantic of the outdoor scenes are dealt with separately and separate methods are proposed for visual mapping of environments belonging to each type. An indoor scene consists of a corridor structure which is modeled as a cubic space in order to build a map of the environment. A “flash-n-extend” strategy is proposed which is responsible for controlling the map update frequency. The semantics of the outdoor scenes is also investigated and a scene classification method is proposed. The method employs a Markov Random Field (MRF) based classification framework which generates a set of semantic maps.

Visual navigation is a ubiquitous yet complex task which is performed by many species for the purpose of survival. Although visual navigation is actively being studied within the robotics community, the determination of elemental constituents of a robust visual navigation system remains a challenge. Motion estimation is mistakenly considered as the sole ingredient to make a robust autonomous visual navigation system and therefore efforts are made to improve the accuracy of motion estimations. On the contrary, there are other factors which are as important as motion and whose absence could result in inability to perform seamless visual navigation such as the one exhibited by humans. Therefore, it is needed that a general model for a visual navigation system be devised which would describe it in terms of a set of elemental units. In this regard, a set of visual navigation elements (i.e. spatial memory, motion memory, scene geometry, context and scene semantics) are suggested as building blocks of a visual navigation system in this thesis. A set of methods are proposed which investigate the existence and role of visual navigation elements in a visual navigation system. A quantitative research methodology in the form of a series of systematic experiments is conducted on these methods. The thesis formulates, implements and analyzes the proposed methods in the context of visual navigation elements which are arranged into three major groupings; a) Spatial memory b) Motion Memory c) Manhattan, context and scene semantics. The investigations are carried out on multiple image datasets obtained by robot mounted cameras (2D/3D) moving in different environments. Spatial memory is investigated by evaluation of proposed place recognition methods. The recognized places and inter-place associations are then used to represent a visited set of places in the form of a topological map. Such a representation of places and their spatial associations models the concept of spatial memory. It resembles the humans’ ability of place representation and mapping for large environments (e.g. cities). Motion memory in a visual navigation system is analyzed by a thorough investigation of various motion estimation methods. This leads to proposals of direct motion estimation methods which compute accurate motion estimates by basing the estimation process on dominant surfaces. In everyday world, planar surfaces, especially the ground planes, are ubiquitous. Therefore, motion models are built upon this constraint. Manhattan structure provides geometrical cues which are helpful in solving navigation problems. There are some unique geometric primitives (e.g. planes) which make up an indoor environment. Therefore, a plane detection method is proposed as a result of investigations performed on scene structure. The method uses supervised learning to successfully classify the segmented clusters in 3D point-cloud datasets. In addition to geometry, the context of a scene also plays an important role in robustness of a visual navigation system. The context in which navigation is being performed imposes a set of constraints on objects and sections of the scene. The enforcement of such constraints enables the observer to robustly segment the scene and to classify various objects in the scene. A contextually aware scene segmentation method is proposed which classifies the image of a scene into a set of geometric classes. The geometric classes are sufficient for most of the navigation tasks. However, in order to facilitate the cognitive visual decision making process, the scene ought to be semantically segmented. The semantic of indoor scenes as well as semantic of the outdoor scenes are dealt with separately and separate methods are proposed for visual mapping of environments belonging to each type. An indoor scene consists of a corridor structure which is modeled as a cubic space in order to build a map of the environment. A “flash-n-extend” strategy is proposed which is responsible for controlling the map update frequency. The semantics of the outdoor scenes is also investigated and a scene classification method is proposed. The method employs a Markov Random Field (MRF) based classification framework which generates a set of semantic maps.

Les séries temporelles sont l'un des types de données les plus courants dans divers domaines scientifiques, industriels et financiers. Selon le contexte, l'analyse des séries temporelles est effectuée à diverses fins: prévision, estimation, classification et détection des tendances et des événements. Grâce aux capacités exceptionnelles de la perception visuelle humaine, la visualisation reste l'un des outils les plus puissants pour l'analyse de données, en particulier pour les données temporelles. Avec la croissance de volume et de la complexité des jeux de données, de nouvelles techniques de visualisation sont clairement nécessaires pour améliorer l'analyse des données. Elles visent à faciliter l'analyse visuelle dans le cas où des situations ou des tâches sont bien spécifiées, ou à favoriser l'analyse exploratoire non guidée.La visualisation est basée sur le "mapping visuel", un processus qui consiste à associer les valeurs de données aux canaux visuels comme la position, la taille et la couleur des éléments graphiques. A cet égard, la forme la plus connue de visualisation des séries temporelles, c'est-à-dire les graphiques linéaires ("line charts" en anglais), consiste en une mise en correspondance des valeurs de données avec la position verticale de la ligne. Cependant, un seul mapping visuel ne convient pas à toutes les situations et objectifs analytiques.Notre but est d'introduire des alternatives au mapping visuel conventionnel et de trouver des situations dans lesquelles, la nouvelle approche compense la simplicité et la familiarité des techniques existantes. Nous présentons une revue de l'état de l'art sur la visualisation des séries chronologiques, puis nous nous concentrons sur les approches existantes du mapping visuel.Ensuite, nous présentons nos contributions. Notre première contribution est une étude systématique d'un «mapping visuelle composite» qui consiste à utiliser des combinaisons de canaux visuels pour communiquer différentes facettes d'une série temporelle. Au moyen de plusieurs expériences avec des utilisateurs, nous comparons les nouveaux mappings visuels à une technique de référence existante et nous mesurons la vitesse et la précision des utilisateurs dans différentes tâches analytiques. Nos résultats montrent que les nouvelles conceptions visuelles conduisent à des performances analytiques proches de celles des techniques existantes sans être inutilement complexes ou nécessiter un entraînement. De plus, certains mappings proposés surpassent les techniques existantes dans les situations de contraintes spatiales. L'efficacité spatiale est d'une grande importance pour la visualisation simultanée de grands volumes de données ou de visualisation sur de petits écrans. Les deux scénarios font partie des défis actuels de la visualisation de l'information. / Time series are one of the most common types of recorded data in various scientific, industrial, and financial domains. Depending on the context, time series analysis are used for a variety of purposes: forecasting, estimation, classification, and trend and event detection. Thanks to the outstanding capabilities of human visual perception, visualization remains one of the most powerful tools for data analysis, particularly for time series. With the increase in data sets' volume and complexity, new visualization techniques are clearly needed to improve data analysis. They aim to facilitate visual analysis in specified situations, tasks, or for unguided exploratory analysis.Visualization is based upon visual mapping, which consists in association of data values to visual channels, e.g. position, size, and color of the graphical elements. In this regard, the most familiar form of time series visualization, i.e. line charts, consists in a mapping of data values to the vertical position of the line. However, a single visual mapping is not suitable for all situations and analytical objectives.Our goal is to introduce alternatives to the conventional visual mapping and find situations in which, the new approach compensate for the simplicity and familiarity of the existing techniques. We present a review of the existing literature on time series visualization and then, we focus on the existing approaches to visual mapping.Next, we present our contributions. Our first contribution is a systematic study of a "composite" visual mapping which consists in using combinations of visual channels to communicate different facets of a time series. By means of several user studies, we compare our new visual mappings with an existing reference technique and we measure users' speed and accuracy in different analytical tasks. Our results show that the new visual designs lead to analytical performances close to those of the existing techniques without being unnecessarily complex or requiring training. Also, some of the proposed mappings outperform the existing techniques in space constraint situations. Space efficiency is of great importance to simultaneous visualization of large volumes of data or visualization on small screens. Both scenarios are among the current challenges in information visualization.

Project file archives are becoming increasingly large. The number of files, information and data that need to be created, accessed and modified throughout a project can be overwhelming. It is critical for project participants or contributors to find relevant information in project archives quickly. In this thesis, I present VisArchive, an interactive visualization tool that provides users with better awareness of search results within project archives. VisArchive visualizes the relevance-ranked search results with a color-coded stacked bar chart and interactive timelines and provides supporting visual cues to help differentiate search results based on searched keywords. It aims to allow users to interactively search, browse, and explore information in project archives, including access history, effectively and efficiently. I will present two case studies to illustrate how VisArchive can be used to support searching, browsing, and exploring information in building construction and open source software projects. In addition, I discuss how VisArchive can be improved to address information retrieval problems and work across different domains. VisArchive demonstrates the combination and application of several visualization techniques to the problem of searching and navigating project archives. / Graduate / 0984

Project file archives are becoming increasingly large. The number of files, information and data that need to be created, accessed and modified throughout a project can be overwhelming. It is critical for project participants or contributors to find relevant information in project archives quickly. In this thesis, I present VisArchive, an interactive visualization tool that provides users with better awareness of search results within project archives. VisArchive visualizes the relevance-ranked search results with a color-coded stacked bar chart and interactive timelines and provides supporting visual cues to help differentiate search results based on searched keywords. It aims to allow users to interactively search, browse, and explore information in project archives, including access history, effectively and efficiently. I will present two case studies to illustrate how VisArchive can be used to support searching, browsing, and exploring information in building construction and open source software projects. In addition, I discuss how VisArchive can be improved to address information retrieval problems and work across different domains. VisArchive demonstrates the combination and application of several visualization techniques to the problem of searching and navigating project archives. / Graduate / 0984

We live fully embedded in Petroculture - in a society shaped by oil and its outcomes, meanwhile the substance itself stays practically invisible to us. This invisibility impedes our ability to rapidly address environmental issues, while narrating us into large networks of unequal power structures. In this study, I emphasize the necessity of making oil part of our visual vocabulary and attempting to pierce its veiling cloak of invisibility through radical subjectivity, I investigate how might we contextualise the human-fossil fuel industry relationship in a way which will challenge oppressive binary structures and instigate action, in relation to climate crisis.  By asking What do I need to see? I advocate for empowering body-centric practice of self-enquiry through visual thinking, using filmmaking as an intervention tool to point fingers back at oil and facilitate reflection upon the structures that sustain its persistence.  The result of this inquiry is “The 5th Element”, a film installation made with a process of visual questioning and  mapping the experience of living in Petroculture.

Visual Analytics is a multi-disciplinary field that uses interactive visualisations to promote and assist the analytic reasoning and generate insights. Understanding the perceptual and cognitive factors is key to the progress in this field. This research focuses on understanding the benefits of interaction in terms of insight generation Moreover, this investigation explores the compounding effects individual differences have with interaction when analysing data to generate insights. This study investigated the individual differences in two sets; psychometric set measures, and a sensorial preferences multimodal learning style. Interaction was analysed from an information visualisation perspective, exploring the Visual Mapping and View Transformation interaction, by isolating interaction as an independent variable. Moreover, the View Transformation experiment used two different visual representations 2D and 3D. Additionally, the individual differences were analysed using the aptitude-by-treatment interaction (ATI) methodology. The ATI approach enabled the assessment of the performance gains in terms of insight generation according to pre-defined set levels of individual differences measures. This thesis confirms the benefits of interaction in generating more insights and increasing their accuracy, whilst facilitating the generation of insights requiring lower mental effort. Further, the results show significant conjoint effects between interaction and individual differences. Furthermore this research revealed a performance difference between 2D and 3D visual representation in the serious game problem solving context. Overall, this thesis provides tangible proof that both visual mapping and view transformation interaction are beneficial to visual analytics in generating insights. Strengthening the view that interaction with the problem-set improves understanding, and the number of insights gleaned into the problem and that more research into the use of individual differences, as a performance predictor in Visual Analytics is beneficial.

I am exploring the way music alters or enhances the perception of our environment. This creative project allows me to explore and visualize several issues that intrigue me: music (sound), emotion, and visual imagery (film). My goal in developing this topic is to allow others to have an experience related to sound and image, where image is altered and enhanced by the use of music.

The world wide web has gone through several distinct eras since its launch in 1989, going through the eras Web 1.0, Web 2.0, and Web 3.0. There is an upcoming era, Web 4.0, where the web will become seamlessly integrated with people's everyday life. However, creating consumer engagement across platforms has already been recognized as challenging in Web 3.0 (Dolan et al., 2016). A type of storytelling, known for creating engagement is transmedia storytelling (TS). TS is a technique of telling one single story across multiple platforms, and when enjoyed together becomes a full experience. There is limited research conducted on the relationship between TS and the web, which became the problem of this study. The purpose of this study is to explorethe research gap and it was fulfilled by asking two research questions. The first oneestablished an understanding about the relationship between TS and Web 2.0 and 3.0, tounderstand how TS evolves and adapts to the web. Based on the built foundation, the second question aimed to explore the possibilities of TS and Web 4.0, and how they could benefit from each other and grow together. To answer our research questions this paper utilised the method of a multiple-case study that examines two cases, one in Web 2.0, and one in Web 3.0. The data was manually collected, traced and broken down into instances using reverse engineering. To analyse the data, it was rebuilt with visual mapping to understand the connections, and lastly compared to the web characteristics and TS principles. Concluding the findings and analysis, it can be established that TS evolves alongside the web, utilising and adapting to the new characteristics. In Web 2.0, TS relied heavily on offline sources such as DVD’s and TV, while in Web 3.0, it existed solely online. The participatory activity also grew from Web 2.0 to 3.0, where the users were an active part of the campaign evolving in Web 3.0. TS manages to create engagement across platforms, and benefits from new technical innovations. After the individual case reports, and cross-case analysis, it could be concluded that TS has good chances of continuing to adapt in the rise of Web 4.0. This study explored the research gap further, offering valuable insight onthe topic and opening for various further research possibilities, where TS and web cancontinue being investigated.

Durant les 20 dernières années, la recherche en visualisation d’informations (InfoVis) a permis l’émergence de nouvelles techniques et méthodes qui permettent d’assister l’analyse de données intensives pour la science, l’industrie, et les gouvernements. Cependant, la plupart de ces travaux de recherches furent orientés sur des données statiques pour des utilisateurs experts.Dernièrement, des évolutions technologique et sociétales ont eu pour effet de rendre les données de plus en plus dynamiques et accessibles pour une population plus diverse. Par exemple des flux de données tels que les emails, les mises à jours de statuts sur les réseaux sociaux, les flux RSS, les systèmes de
gestion de versions, et bien d’autres. Ces nouveaux types de données sont utilisés par une population qui n’est pas forcément entraînée ou éduquée à utiliser des visualisations de données. La plupart de ces personnes sont des utilisateurs occasionnels, d’autres utilisent très souvent ces données dans leurs travaux. Dans les deux cas, il est probable que ces personnes n’aient pas reçu de formation formelle en visualisation de données.Ces changements technologiques et sociétaux ont généré une multitude de nouveaux défis, car la plupart des techniques de visualisations sont conçues pour des experts et des bases de données statiques. Peu d’études ont été conduites pour explorer ces défis. Dans ce rapport de thèse, j’adresse la question suivante : « Peut-­on permettre à des utilisateurs non­-experts de créer leur propre visualisation et de contribuer à l’analyse de flux de données ? »La première étape pour répondre à cette question est d’évaluer si des personnes non formées à la visualisation d’informations ou aux « data sciences » peuvent effectuer des tâches d’analyse de données dynamiques utiles, en utilisant un système de visualisation adapté pour supporter cette tâche. Dans la première partie de cette dissertation, je présente différents scénarios et systèmes, qui permettent à des utilisateurs non­-experts (de 20 à 300 ou 2000 à 700 000 personnes) d’utiliser la visualisation d’informations pour analyser des données dynamiques.Un autre problème important est le manque de principes génériques de design pour l’encodage visuel de visualisations d’informations dynamiques. Dans cette dissertation, je conçois, définis, et explore un espace de design pour représenter des donnés dynamiques pour des utilisateurs non­-experts. Cette espace de design est structuré par des jetons graphiques représentant des éléments de données qui permettent de construire dans le temps différentes visualisations, tant classiques que nouvelles.Dans cette thèse, je propose un nouveau paradigme de conception (design) pour faciliter la réalisation de visualisation d’informations par les utilisateurs non­-experts. Ce paradigme est inspiré par des théories établies en psychologie du développement, tout autant que par des pratiques passées et présentes de création de visualisation à partir d’objets tangibles. Je décris tout d’abord les composants et processus de bases qui structurent ce paradigme. Ensuite, j’utiliserai cette description pour étudier *si et comment* des utilisateur non­-experts sont capables de créer, discuter, et mettre à jour leurs propres visualisations. Cette étude nous permettra de réviser notre modèle précédent et de fournir une première exploration des phénomènes relatifs à la création d’encodages visuels par des utilisateurs non­-experts sans logiciel. En résumé, cette thèse contribue à la compréhension des visualisations dynamiques pour des utilisateurs non­-experts. / During the past two decades, information visualisation (InfoVis) research has created new techniques and methods to support data- intensive analyses in science, industry and government. These have enabled a wide range of analyses tasks to be executed, with tasks varying in terms of the type and volume of data involved. However, the majority of this research has focused on static datasets, and the analysis and visualisation tasks tend to be carried out by trained expert users. In more recent years, social changes and technological advances have meant that data have become more and more dynamic, and are consumed by a wider audience. Examples of such dynamic data streams include e-mails, status updates, RSS 1 feeds, versioning systems, social networks and others. These new types of data are used by populations that are not specifically trained in information visualization. Some of these people might consist of casual users, while others might consist of people deeply involved with the data, but in both cases, they would not have received formal training in information visualization. For simplicity, throughout this dissertation, I refer to the people (casual users, novices, data experts) who have not been trained in information visualisation as non-experts.These social and technological changes have given rise to multiple challenges because most existing visualisation models and techniques are intended for experts, and assume static datasets. Few studies have been conducted that explore these challenges. In this dissertation, with my collaborators, I address the question: Can we empower non-experts in their use of visualisation by enabling them to contribute to data stream analysis as well as to create their own visualizations?The first step to answering this question is to determine whether people who are not trained in information visualisation and the data sciences can conduct useful dynamic analysis tasks using a visualisation system that is adapted to support their tasks. In the first part of this dissertation I focus on several scenarios and systems where different sized crowds of InfoVis non-experts users (20 to 300 and 2 000 to 700 000 people) use dynamic information visualisation to analyse dynamic data.Another important issue is the lack of generic design principles for the visual encoding of dynamic visualization. In this dissertation I design, define and explore a design space to represent dynamic data for non-experts. This design space is structured by visual tokens representing data items that provide the constructive material for the assembly over time of different visualizations, from classic represen- tations to new ones. To date, research on visual encoding has been focused on static datasets for specific tasks, leaving generic dynamic approaches unexplored and unexploited.In this thesis, I propose construction as a design paradigm for non-experts to author simple and dynamic visualizations. This paradigm is inspired by well-established developmental psychological theory as well as past and existing practices of visualisation authoring with tangible elements. I describe the simple conceptual components and processes underlying this paradigm, making it easier for the human computer interaction community to study and support this process for a wide range of visualizations. Finally, I use this paradigm and tangible tokens to study if and how non-experts are able to create, discuss and update their own visualizations. This study allows us to refine our previous model and provide a first exploration into how non-experts perform a visual mapping without software. In summary, this thesis contributes to the understanding of dynamic visualisation for non-expert users.