Machine learned boundary definitions for an expert's tracing assistant in image processing

Department Head: Anton Willem Bohm.Includes bibliographical references (pages 178-184).Most image processing work addressing boundary definition tasks embeds the assumption that an edge in an image corresponds to the boundary of interest in the world. In straightforward imagery this is true, however it is not always the case. There are images in which edges are indistinct or obscure, and these images can only be segmented by a human expert. The work in this dissertation addresses the range of imagery between the two extremes of those straightforward images and those requiring human guidance to appropriately segment. By freeing systems of a priori edge definitions and building in a mechanism to learn the boundary definitions needed, systems can do better and be more broadly applicable. This dissertation presents the construction of such a boundary-learning system and demonstrates the validity of this premise on real data. A framework was created for the task in which expert-provided boundary exemplars are used to create training data, which in turn are used by a neural network to learn the task and replicate the expert's boundary tracing behavior. This is the framework for the Expert's Tracing Assistant (ETA) system. For a representative set of nine structures in the Visible Human imagery, ETA was compared and contrasted to two state-of-the-art, user guided methods--Intelligent Scissors (IS) and Active Contour Models (ACM). Each method was used to define a boundary, and the distances between these boundaries and an expert's ground truth were compared. Across independent trials, there will be a natural variation in an expert's boundary tracing, and this degree of variation served as a benchmark against which these three methods were compared. For simple structural boundaries, all the methods were equivalent. However, in more difficult cases, ETA was shown to significantly better replicate the expert's boundary than either IS or ACM. In these cases, where the expert's judgement was most called into play to bound the structure, ACM and IS could not adapt to the boundary character used by the expert while ETA could

Third CLIPS Conference Proceedings, volume 1

Expert systems are computed programs which emulate human expertise in well defined problem domains. The potential payoff from expert systems is high: valuable expertise can be captured and preserved, repetitive and/or mundane tasks requiring human expertise can be automated, and uniformity can be applied in decision making processes. The C Language Integrated Production Systems (CLIPS) is an expert system building tool, developed at the Johnson Space Center, which provides a complete environment for the development and delivery of rule and/or object based expert systems. CLIPS was specifically designed to provide a low cost option for developing and deploying expert system applications across a wide range of hardware platforms. The development of CLIPS has helped to improve the ability to deliver expert systems technology throughout the public and private sectors for a wide range of applications and diverse computing environments

Intelligent Traffic Management: From Practical Stochastic Path Planning to Reinforcement Learning Based City-Wide Traffic Optimization

This research focuses on intelligent traffic management including stochastic path planning and city scale traffic optimization. Stochastic path planning focuses on finding paths when edge weights are not fixed and change depending on the time of day/week. Then we focus on minimizing the running time of the overall procedure at query time utilizing precomputation and approximation. The city graph is partitioned into smaller groups of nodes and represented by its exemplar. In query time, source and destination pairs are connected to their respective exemplars and the path between those exemplars is found. After this, we move toward minimizing the city wide traffic congestion by making structural changes include changing the number of lanes, using ramp metering, varying speed limit, and modifying signal timing is possible. We propose a multi agent reinforcement learning (RL) framework for improving traffic flow in city networks. Our framework utilizes two level learning: a) each single agent learns the initial policy and b) multiple agents (changing the environment at the same time) update their policy based on the interaction with the dynamic environment and in agreement with other agents. The goal of RL agents is to interact with the environment to learn the optimal modification for each road segment through maximizing the cumulative reward over the set of possible actions in state space

Detection and prediction of urban archetypes at the pedestrian scale: computational toolsets, morphological metrics, and machine learning methods

Granular, dense, and mixed-use urban morphologies are hallmarks of walkable and vibrant streets. However, urban systems are notoriously complex and planned urban development, which grapples with varied interdependent and oft conflicting criteria, may — despite best intentions — yield aberrant morphologies fundamentally at odds with the needs of pedestrians and the resiliency of neighbourhoods. This work addresses the measurement, detection, and prediction of pedestrian-friendly urban archetypes by developing techniques for high-resolution urban analytics at the pedestrian scale. A spatial-analytic computational toolset, the cityseer-api Python package, is created to assess localised centrality, land-use, and statistical metrics using contextually sensitive workflows applied directly over the street network. cityseer-api subsequently facilitates a review of mixed-use and street network centrality methods to improve their utility concerning granular urban analysis. Unsupervised machine learning methods are applied to recover ‘signatures’ — urban archetypes — using Principal Component Analysis, Variational Autoencoders, and clustering methods from a high-resolution multi-variable and multi-scalar dataset consisting of centralities, land-uses, and population densities for Greater London. Supervised deep-learning methods applied to a similar dataset developed for 931 towns and cities in Great Britain demonstrate how, with the aid of domain knowledge, machine-learning classifiers can learn to discriminate between ‘artificial’ and ‘historical’ urban archetypes. These methods use complex systems thinking as a departure point and illustrate how high-resolution spatial-analytic quantitative methods can be combined with machine learning to extrapolate benchmarks in keeping with more qualitatively framed urban morphological conceptions. Such tools may aid urban design professionals in better anticipating the outcomes of varied design scenarios as part of iterative and scalable workflows. These techniques may likewise provide robust and demonstrable feedback as part of planning review and approvals processes

Visually Guided Control of Movement

The papers given at an intensive, three-week workshop on visually guided control of movement are presented. The participants were researchers from academia, industry, and government, with backgrounds in visual perception, control theory, and rotorcraft operations. The papers included invited lectures and preliminary reports of research initiated during the workshop. Three major topics are addressed: extraction of environmental structure from motion; perception and control of self motion; and spatial orientation. Each topic is considered from both theoretical and applied perspectives. Implications for control and display are suggested

Classification by Neural Network and Statistical Models in Tandem: Does Integration Enhance Performance?

The major purposes of the current research are twofold. The first purpose is to present a composite approach to the general classification problem by using outputs from various parametric statistical procedures and neural networks. The second purpose is to compare several parametric and neural network models on a transportation planning related classification problem and five simulated classification problems

Processes of hybrid knowledge creation in pastoralist development

This thesis addresses an under-researched disjunction surrounding knowledge creation between, and within, development and pastoralist groups. Many academics increasingly recognise pastoralist populations as creative and adaptable, yet these populations often lack the resources to develop innovations beyond the local context. Despite often being better resourced than pastoralist communities, development interventions in the Horn of Africa have achieved limited successes; an observation often linked in academic literature with a failure to rethink inappropriate established practices drawn from settled agriculture. The need to explore new ways of understanding hybrid knowledge creation in pastoralist settings emerged from the international community’s limited understanding of informal innovation processes and unique contexts of pastoralist regions, due in part to the unsuitability of current frameworks and research tools for conceptualising informal innovation in marginal settings. This study makes an original research contribution by exploring the factors that shape processes of knowledge creation between development and pastoralist groups to answer the question what factors influence innovation in pastoralist areas? An interconnected, mixed-methods research strategy was developed and applied to study the role of knowledge networks and framings in processes of knowledge creation amongst pastoralist and development actors innovating in North Horr, Kenya. The empirical data gathered throughout the research informed the development of an internally-valid analytical framework with which to explore innovation in this setting. The key findings of this study highlight the importance of the contextual and often asymmetric nature of relationships in processes of emergent knowledge creation within pastoralist development. The observations collected throughout the research process provide an empirical basis from which to discuss networks, framings, and knowledge creation in pastoralist settings; contributing to wider debates surrounding informal innovation processes and narratives of pastoralist development

A geometry without angles: the case for a functional geometry of spatial prepositions

This thesis develops the view that the semantics of spatial prepositions are more fully realised within a framework of functionality, incorporating knowledge of the world, than within the spatial, geometrical framework more often used to analyse prepositions. It is argued that previous approaches which support full specification of lexical entries through the use of polysemy and prototype notions are not satisfactory or psychologically valid. It will also be shown that the minimal specification Classical approaches fail to account for all uses of the locatives described. It is suggested that minimal specification of lexical entries can be achieved by means of functional controls that can provide a more psychologically valid account of the semantics of spatial prepositions. Functional geometric control relations of fContainment, fSupport and fSuperiority are proposed for IN, ON and OVER respectively. These focus on the importance of location control in prepositional choice. It is argued that such controls underlie the use of spatial prepositions. The controls are suggested to be inherently dynamic and state that the relatum object is some way able to control the location of the referent object. For example, the use of the preposition IN is guided by the principle of fContainment which operates on the basic premise that the relatum (y) controls the location of the referent (x) such that when y moves there will be a correlated movement in x (or uncorrelated movement within the convex hull of y) by virtue of some degree of enclosure. The control relation that guides the use of OVER is fSuperiority and it operates on the basic premise that x threatens to come into contact with y as a consequence of gravitational force. Finally, the use of the preposition ON is suggested to be guided by notions of fSupport which operates on the premise that the relatum protects the referent from the force of gravity

Urban Mobility Transitions: Governing through Experimentation in Bristol and New York City

Transitions away from car-dominance is one of the key debates in urban research, policy and practice today. Car-free public space, cycling and convenient public transport services are widely seen as desirable, yet the reconfiguration of our streets and transport networks has been incremental. This doctoral research examines how mobility in cities is governed through experiments, commonly understood as pilot projects, and whether experiments hold potential for transformative change in urban mobility systems, including transitions away from automobility. The research draws on a synthesis of sustainability transitions, transport studies and urban studies literature, and traces the outcomes of 108 experiments undertaken over two decades in two cities: Bristol (UK) and New York City (USA) between 1996/7 and 2016. The findings demonstrate that experiments can contribute to transforming the physical shape of urban mobility systems and the institutions involved in governing them, and can even contribute to transitions, if assessed as change in commuting patterns away from car use. The research compares the capacity of respective municipal governments, Bristol City Council and NYC city government for ‘transformative experimentation’, and presents an institutionalist analysis of why the transformation of Bristol’s mobility system was more limited than NYC’s. To unpack the problematisation of piecemeal, ‘project-based’ experimentation driven by competitive funding landscapes, the research compares Bristol City Council and NYC city government as two municipalities with a different degree of reliance on external funding. The stronger capacity of NYC city government can be explained by its higher degree of fiscal autonomy and mobility policy discretion, whereas Bristol City Council’s capacity was limited by the centralisation of the UK state. Yet the thesis also shows that both municipalities pursued successful endogenous strategies in response to multi-scalar structure, and points to organisational and governance practices that can create ‘political space’ for urban actors to further transitions

Mobility Design

Climate change and the scarcity of resources, but also the steadily increasing amount of traffic, make it indispensable to develop new solutions for environmentally friendly and people-friendly mobility. With the expansion of digital information systems, we will in future be able to easily combine different modes of transport according to our needs. These developments are a great challenge for the design of different mobility spaces. While the focus in Volume 1 was on practice, Volume 2 now brings together research from the fields of design, architecture, urban planning, geography, social science, transport planning, psychology and communication technology. The current discussion about the traffic turnaround is expanded to include the perspective of user-centred mobility design