The Future of visibility: imagining possibilities for networked civic discontent

Expressions of public discontent are traditionally considered one of the key elements of performing citizenship. This article explores the potential futures of technologically augmented discontent and the implications these future scenarios might have for civil society as a source of alternative voices on key social issues and civic rights. Though there are many issues at stake for civil society actors participating in social protest, I focus on the issue of visibility of discontent and the role of future technologies, such as drone imaging, AR, VR, holographic technology and AI, in making social protest more or less visible. I conceptualise the future potentialities of technologically augmented protest visibility through the prism of technological affordances theory. Affordances refer to the potential opportunities or limitations of action that emerge at the nexus of actor intentions, technological capabilities and the environment in which they interact. Such a context-dependent approach is useful in horizon scanning as it allows to account for a number of potential scenarios and to speculate how each may shape the value and impact of certain technological interventions for particular civic publics

Recurrent processing during object reocognition

Although the rich bidirectional architecture of the ventral visual stream has been documented for some time, relatively little work has gone into understanding its function in object recognition. Recently, computational modeling work has suggested that the computations performed within a bidirectional architecture (recurrent processing) could be beneficial to object recognition by clean- ing up ambiguity in input signals, thus providing robustness to degradations like occlusion that underspecify the visual stimulus. The research described here tests this claim by using visual masking to disrupt this recurrent processing. In one experiment and a series of accompanying modeling simulations, it is shown that there is a significant interaction between a mask and oc- clusion such that performance on an object categorization task is differentially impaired when a moderately occluded stimulus is masked compared to a relatively unoccluded one. Furthermore, the modeling simulations provide a mechanistic explanation of how recurrent processing resolves ambiguity as well as how masking interrupts this process. Together, the results of the experiment and modeling simulations suggest that object recognition is a dynamic process characterized by interactions between adjacent areas along the ventral visual stream

Cyclist Detection, Tracking, and Trajectory Analysis in Urban Traffic Video Data

The major objective of this thesis work is examining computer vision and machine learning detection methods, tracking algorithms and trajectory analysis for cyclists in traffic video data and developing an efficient system for cyclist counting. Due to the growing number of cyclist accidents on urban roads, methods for collecting information on cyclists are of significant importance to the Department of Transportation. The collected information provides insights into solving critical problems related to transportation planning, implementing safety countermeasures, and managing traffic flow efficiently. Intelligent Transportation System (ITS) employs automated tools to collect traffic information from traffic video data. In comparison to other road users, such as cars and pedestrians, the automated cyclist data collection is relatively a new research area. In this work, a vision-based method for gathering cyclist count data at intersections and road segments is developed. First, we develop methodology for an efficient detection and tracking of cyclists. The combination of classification features along with motion based properties are evaluated to detect cyclists in the test video data. A Convolutional Neural Network (CNN) based detector called You Only Look Once (YOLO) is implemented to increase the detection accuracy. In the next step, the detection results are fed into a tracker which is implemented based on the Kernelized Correlation Filters (KCF) which in cooperation with the bipartite graph matching algorithm allows to track multiple cyclists, concurrently. Then, a trajectory rebuilding method and a trajectory comparison model are applied to refine the accuracy of tracking and counting. The trajectory comparison is performed based on semantic similarity approach. The proposed counting method is the first cyclist counting method that has the ability to count cyclists under different movement patterns. The trajectory data obtained can be further utilized for cyclist behavioral modeling and safety analysis

Real-time people tracking in a camera network

Visual tracking is a fundamental key to the recognition and analysis of human behaviour. In this thesis we present an approach to track several subjects using multiple cameras in real time. The tracking framework employs a numerical Bayesian estimator, also known as a particle lter, which has been developed for parallel implementation on a Graphics Processing Unit (GPU). In order to integrate multiple cameras into a single tracking unit we represent the human body by a parametric ellipsoid in a 3D world. The elliptical boundary can be projected rapidly, several hundred times per subject per frame, onto any image for comparison with the image data within a likelihood model. Adding variables to encode visibility and persistence into the state vector, we tackle the problems of distraction and short-period occlusion. However, subjects may also disappear for longer periods due to blind spots between cameras elds of view. To recognise a desired subject after such a long-period, we add coloured texture to the ellipsoid surface, which is learnt and retained during the tracking process. This texture signature improves the recall rate from 60% to 70-80% when compared to state only data association. Compared to a standard Central Processing Unit (CPU) implementation, there is a signi cant speed-up ratio

Egocentric Reconstruction of Human Bodies for Real-time Mobile Telepresence

A mobile 3D acquisition system has the potential to make telepresence significantly more convenient, available to users anywhere, anytime, without relying on any instrumented environments. Such a system can be implemented using egocentric reconstruction methods, which rely only on wearable sensors, such as head-worn cameras and body-worn inertial measurement units. Prior egocentric reconstruction methods suffer from incomplete body visibility as well as insufficient sensor data. This dissertation investigates an egocentric 3D capture system relying only on sensors embedded in commonly worn items such as eyeglasses, wristwatches, and shoes. It introduces three advances in egocentric reconstruction of human bodies. (1) A parametric-model-based reconstruction method that overcomes incomplete body surface visibility by estimating the user's body pose and facial expression, and using the results to re-target a high-fidelity pre-scanned model of the user. (2) A learning-based visual-inertial body motion reconstruction system that relies only on eyeglasses-mounted cameras and a few body-worn inertial sensors. This approach overcomes the challenges of self-occlusion and outside-of-camera motions, and allows for unobtrusive real-time 3D capture of the user. (3) A physically plausible reconstruction method based on rigid body dynamics, which reduces motion jitter and prevents interpenetrations between the reconstructed user's model and the objects in the environment such as the ground, walls, and furniture. This dissertation includes experimental results demonstrating the real-time, mobile reconstruction of human bodies in indoor and outdoor scenes, relying only on wearable sensors embedded in commonly-worn objects and overcoming the sparse observation challenges of egocentric reconstruction. The potential usefulness of this approach is demonstrated in a telepresence scenario featuring physical therapy training.Doctor of Philosoph

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Medical image analysis methods in MR/CT-imaged acute-subacute ischemic stroke lesion:Segmentation, prediction and insights into dynamic evolution simulation models. A critical appraisal

AbstractOver the last 15years, basic thresholding techniques in combination with standard statistical correlation-based data analysis tools have been widely used to investigate different aspects of evolution of acute or subacute to late stage ischemic stroke in both human and animal data. Yet, a wave of biology-dependent and imaging-dependent issues is still untackled pointing towards the key question: “how does an ischemic stroke evolve?” Paving the way for potential answers to this question, both magnetic resonance (MRI) and CT (computed tomography) images have been used to visualize the lesion extent, either with or without spatial distinction between dead and salvageable tissue. Combining diffusion and perfusion imaging modalities may provide the possibility of predicting further tissue recovery or eventual necrosis. Going beyond these basic thresholding techniques, in this critical appraisal, we explore different semi-automatic or fully automatic 2D/3D medical image analysis methods and mathematical models applied to human, animal (rats/rodents) and/or synthetic ischemic stroke to tackle one of the following three problems: (1) segmentation of infarcted and/or salvageable (also called penumbral) tissue, (2) prediction of final ischemic tissue fate (death or recovery) and (3) dynamic simulation of the lesion core and/or penumbra evolution. To highlight the key features in the reviewed segmentation and prediction methods, we propose a common categorization pattern. We also emphasize some key aspects of the methods such as the imaging modalities required to build and test the presented approach, the number of patients/animals or synthetic samples, the use of external user interaction and the methods of assessment (clinical or imaging-based). Furthermore, we investigate how any key difficulties, posed by the evolution of stroke such as swelling or reperfusion, were detected (or not) by each method. In the absence of any imaging-based macroscopic dynamic model applied to ischemic stroke, we have insights into relevant microscopic dynamic models simulating the evolution of brain ischemia in the hope to further promising and challenging 4D imaging-based dynamic models. By depicting the major pitfalls and the advanced aspects of the different reviewed methods, we present an overall critique of their performances and concluded our discussion by suggesting some recommendations for future research work focusing on one or more of the three addressed problems

Bidirektionale Interaktion von Mensch und Roboter beim Bewegungslernen - Visuelle Wahrnehmung von Roboterbewegungen

In den vergangenen Jahrzehnten haben sich die Arbeitsbereiche von Menschen und Robotern zunehmend gegenseitig durchdrungen. Interaktionen zwischen Mensch und Roboter sind in vielen Lebensbereichen, z. B. Industrie, Medizin, Rehabilitation und Sport gegenwärtig. Während Roboter bisher vorwiegend starr programmiert wurden, hat sich in den letzten Jahren ein Paradigmenwechsel hin zu einer anpassungsfähigen, lernenden Programmierung vollzogen. Basierend auf diesem neuen Ansatz der Programmierung tritt eine direkte, teils physische Interaktion zwischen Mensch und Roboter zunehmend in den Fokus der Entwicklung und eröffnet ein bisher ungeahntes Potential zur Weiterentwicklung der Mensch-Roboter-Interaktion. Die Beziehung von Mensch und Roboter ist von vielen, teils extremen Unterschieden zwischen den beiden Systemen gekennzeichnet (Verfügbare Sensorik, Anzahl der Freiheitsgrade, Anzahl der Muskeln/Aktuatoren sowie Integrationsgrad von Sensorik und Aktuatorik). Diese Unterschiede erweisen sich für die beiden Systeme in einem isolierten Bewegungslernprozess teils als Vor- und teils als Nachteil. Der Frage, wie sich die Vorteile der beiden Systeme in einem gemeinsamen bidirektionalen Bewegungslernprozess optimal kombinieren lassen, geht das Projekt Bidirectional Interaction between Human and Robot when learning movements nach. Im Rahmen dieses interdisziplinären Forschungsprojektes sollen die Erkenntnisse aus den Bereichen der Sportwissenschaft und der Informatik kombiniert und die wissenschaftliche Basis für ein verbessertes Mensch-Roboter-Training gelegt werden. Das Projekt unterteilt sich dabei in vier Teilbereiche: die bidirektionale Interaktion zweier Menschen, die unidirektionale Interaktion von Mensch und Roboter (zwei Richtungen) sowie die bidirektionale Interaktion von Mensch und Roboter. In dieser Dissertation werden drei Artikel zu der beschriebenen Thematik vorgestellt. Der erste Artikel beschreibt Ziele und Struktur des Forschungsprojekts sowie drei exemplarische Studien zu den ersten drei Teilbereichen des Projekts. Aufbauend auf den Erkenntnissen einer der vorgestellten Studien zur Bedeutung der Beobachtungsperspektive beim Bewegungslernen, fokussieren die beiden darauf folgenden Artikel die visuelle Wahrnehmung von Roboterbewegungen durch den Menschen. Der Beschreibung des Projekts in Zielen und Struktur schließt sich im Artikel I die Vorstellung von drei exemplarischen Untersuchungen an. Die erste Studie betrachtet die bidirektionale Interaktion in Mensch-Mensch-Dyaden. Sie verifiziert einen prototypischen, dyadischen Bewegungslernprozess und identifiziert relevante Themen, die auf Mensch-Roboter-Dyaden übertragen werden können. Zur unidirektionalen Interaktion zwischen Mensch und Roboter werden zwei Studien vorgestellt. Im Bereich des Lernens eines Roboters von einem Menschen wird eine iterative Feedbackstrategie eines Roboters beschrieben. Eine Untersuchung zur Bedeutung der Beobachtungsperspektive beim Bewegungslernen von Mensch und Roboter bearbeitet den Bereich des unidirektionalen Lernens eines Mensches von einem Roboter. Basierend auf dieser Untersuchung ergeben sich die Fragestellungen, die in den folgenden beiden Artikeln untersucht werden. Während viele Studien die Wahrnehmung von biologischen Bewegungen untersucht haben, befassen sich nur wenige Ansätze mit der Wahrnehmung von nichtbiologischen Roboterbewegungen. Um diese Lücke zu schließen, werden im Artikel II zwei aufeinander aufbauende Studien zur Wahrnehmung von Roboterputtbewegungen durch den Menschen vorgestellt. Es konnte gezeigt werden, dass eine Leistungsvorhersage der gezeigten Roboterputtbewegungen nur bei Sichtbarkeit der vollständigen Bewegung möglich sind. Insbesondere die Ausschwungphase scheint eine Vielzahl an räumlich-zeitlichen Informationen bereit zu stellen, die einen großen Einfluss auf die Leistungsvorhersage besitzen. Aufbauend auf den bisher gewonnenen Erkenntnissen wird im Artikel III eine Studie vorgestellt, die versucht, die für die Ableitung von räumlich-zeitlichen Informationen wichtigen Bewegungselemente zu identifizieren. Im Rahmen der vorgestellten Untersuchung wurden die gezeigten Roboterputtbewegungen teilweise manipuliert. Wichtige Bewegungselemente, z. B. Roboter, Schläger oder Ball, wurden ausgeblendet. Zusammenfassend betrachtet diese Dissertation die visuelle Wahrnehmung von Roboterbewegungen durch den Menschen am Beispiel der Puttbewegung im Golf. Der Hauptbeitrag dieser Arbeit sind Erkenntnisse, die in einen bidirektionalen Bewegungslernprozess von Mensch-Roboter-Dyaden überführt werden können. Aus der Arbeit ergeben sich weiterführende Forschungsansätze und Fragestellungen, die eine hohe Relevanz für die Weiterentwicklung der Interaktion von Mensch und Roboter besitzen

Seuratun kappaleen poikkeuttaminen silmänräpäysten aikana: käyttäytymis- ja neuromagneettisia havaintoja

The visual world is perceived as continuous despite frequent interruptions of sensory data due to eyeblinks and rapid eye movements. To create the perception of constancy, the brain makes use of fill-in mechanisms. This study presents an experiment in which the location of an object during smooth pursuit tracking is altered during eyeblinks. The experiment investigates the effects of blink suppression and fill-in mechanisms to cloud the discrimination of these changes. We employed a motion-tracking task, which promotes the accurate evaluation of the object’s trajectory and thus can counteract the fill-in mechanisms. Six subjects took part in the experiment, during which they were asked to report any perceived anomalies in the trajectory. Eye movements were monitored with a video-based tracking and brain responses with simultaneous MEG recordings. Discrimination success was found to depend on the direction of the displacement, and was significantly modulated by prior knowledge of the triggered effect. Eye-movement data were congruent with previous findings and revealed a smooth transition from blink recovery to object locating. MEG recordings were analysed for condition-dependent evoked and induced responses; however, intersubject variability was too large for drawing clear conclusions regarding the brain basis of the fill-in mechanisms.Visuaalinen maailma koetaan jatkuvana, vaikka silmänräpäykset ja nopeat silmänliikkeet aiheuttavat keskeytyksiä sensoriseen tiedonkeruuseen. Luodakseen käsityksen pysyvyydestä, aivot käyttävät täyttömekanismeja. Tämä tutkimus esittelee kokeen, jossa kappaleen seurantaa hitailla seurantaliikkeillä häiritään muuttamalla sen sijaintia silmänräpäysten aikana. Tämä koe tutkii, kuinka silmänräpäysten aiheuttama suppressio ja täyttömekanismit sumentavat kykyä erotella näitä muutoksia. Käytimme liikeseurantatehtävää, joka vastaavasti edistää kappaleen liikeradan tarkkaa arviointia. Kuusi koehenkilöä osallistui kokeeseen, jonka aikana heitä pyydettiin ilmoittamaan kaikki havaitut poikkeamat kappaleen liikeradassa. Silmänliikkeitä tallennettiin videopohjaisella seurannalla, ja aivovasteita yhtäaikaisella MEG:llä. Erottelykyvyn todettiin riippuvan poikkeutuksen suunnasta, sekä merkittävästi a priori tiedosta poikkeutusten esiintymistavasta. Silmänliikedata oli yhtenevää aiempien tutkimusten kanssa, ja paljasti sujuvan siirtymisen silmänräpäyksistä palautumisesta kappaleen paikallistamiseen. MEG-tallenteet analysoitiin ehdollisten heräte- ja indusoitujen vasteiden löytämiseksi, mutta yksilölliset vaste-erot koehenkilöiden välillä olivat liian suuria selkeiden johtopäätösten tekemiseksi täyttömekanismien aivoperustasta