Shadows of militarism: an ethnography of trauma and resistance among soldiers and veterans in post-9/11 USA.

This thesis critically examines the military disciplining of trauma through a detailed ethnographic study of post-9/11 lower-enlisted soldiers and veterans in the U.S. who have links to a national movement of resistance to, and healing from, militarism. Drawing on 12 months of ethnographic fieldwork at two G.I. coffeehouses and with the post-9/11 veteran anti-militarism movement in U.S., it analyses the journey of joining the military, becoming a soldier, leaving the military and veteran identities. It explores militarism and military power as a cultural process which reproduces and conceals itself within normative conceptions of the everyday, and military trauma as a site of contested power and resistance. In doing so, this research addresses an urgent need to critically engage with military trauma as a means to challenge normalised discourses of militarism. This research reveals a disjuncture between the imagined and lived reality of military identities in the post-9/11 era. It explores the politics of recognition of veterans’ public and private lives, their contested identities, and their constrained relationship to the state. It argues that veterans are silenced and their identities reduced to symbolic tools in a public military imaginary which constructs military trauma into politically manageable categories, while disciplining and silencing the nation from critically examining war and militarism. In this way, this thesis argues that veterans serve a vital function in U.S. society by absorbing and containing the violence of the state, which then becomes unspeakable, unhearable, and inescapable. This thesis shows how a small number of soldiers and veterans are pushing back against this narrative. In sum, this thesis seeks to challenge the disciplinary effects of militarism upon trauma and support veteran voices to speak their own truths.

Improved detection and tracking of objects in surveillance video

Surveillance networks are typically monitored by a few people, viewing several monitors displaying the camera feeds. It is then very difficult for a human operator to effectively detect events as they happen. Recently, computer vision research has begun to address ways to automatically process some of this data, to assist human operators. Object tracking, event recognition, crowd analysis and human identification at a distance are being pursued as a means to aid human operators and improve the security of areas such as transport hubs. The task of object tracking is key to the effective use of more advanced technologies. To recognize an event people and objects must be tracked. Tracking also enhances the performance of tasks such as crowd analysis or human identification. Before an object can be tracked, it must be detected. Motion segmentation techniques, widely employed in tracking systems, produce a binary image in which objects can be located. However, these techniques are prone to errors caused by shadows and lighting changes. Detection routines often fail, either due to erroneous motion caused by noise and lighting effects, or due to the detection routines being unable to split occluded regions into their component objects. Particle filters can be used as a self contained tracking system, and make it unnecessary for the task of detection to be carried out separately except for an initial (often manual) detection to initialise the filter. Particle filters use one or more extracted features to evaluate the likelihood of an object existing at a given point each frame. Such systems however do not easily allow for multiple objects to be tracked robustly, and do not explicitly maintain the identity of tracked objects. This dissertation investigates improvements to the performance of object tracking algorithms through improved motion segmentation and the use of a particle filter. A novel hybrid motion segmentation / optical flow algorithm, capable of simultaneously extracting multiple layers of foreground and optical flow in surveillance video frames is proposed. The algorithm is shown to perform well in the presence of adverse lighting conditions, and the optical flow is capable of extracting a moving object. The proposed algorithm is integrated within a tracking system and evaluated using the ETISEO (Evaluation du Traitement et de lInterpretation de Sequences vidEO - Evaluation for video understanding) database, and significant improvement in detection and tracking performance is demonstrated when compared to a baseline system. A Scalable Condensation Filter (SCF), a particle filter designed to work within an existing tracking system, is also developed. The creation and deletion of modes and maintenance of identity is handled by the underlying tracking system; and the tracking system is able to benefit from the improved performance in uncertain conditions arising from occlusion and noise provided by a particle filter. The system is evaluated using the ETISEO database. The dissertation then investigates fusion schemes for multi-spectral tracking systems. Four fusion schemes for combining a thermal and visual colour modality are evaluated using the OTCBVS (Object Tracking and Classification in and Beyond the Visible Spectrum) database. It is shown that a middle fusion scheme yields the best results and demonstrates a significant improvement in performance when compared to a system using either mode individually. Findings from the thesis contribute to improve the performance of semi-automated video processing and therefore improve security in areas under surveillance.

Slice of Brisbane in family shadows

Review of 'The True Story of Butterfish', Brisbane Festival / Brisbane Powerhouse, published in The Australian, 6 October 2009.

Inducing shades of meaning by matrix methods : a first step towards thematic analysis of opinion

This article explores two matrix methods to induce the ``shades of meaning" (SoM) of a word. A matrix representation of a word is computed from a corpus of traces based on the given word. Non-negative Matrix Factorisation (NMF) and Singular Value Decomposition (SVD) compute a set of vectors corresponding to a potential shade of meaning. The two methods were evaluated based on loss of conditional entropy with respect to two sets of manually tagged data. One set reflects concepts generally appearing in text, and the second set comprises words used for investigations into word sense disambiguation. Results show that for NMF consistently outperforms SVD for inducing both SoM of general concepts as well as word senses. The problem of inducing the shades of meaning of a word is more subtle than that of word sense induction and hence relevant to thematic analysis of opinion where nuances of opinion can arise.

The Dystopian Mirror and the Female Body

Women’s experience of the change room mirror is not a particularly affirmative one. The pleasure in looking at the self is dissipated by the ideal feminine ‘I’ that hovers in the shadows of their image of self and others constructing dystopian surveillance and entrapment. This article considers the responses of a number of women bloggers who describe their negative experiences in front of change room mirrors. It also argues that the mirror has been used in positive and creative ways by women artists to assert a self that is not subject to a critical gaze.

Towards an automatic road lane marks extraction based on ISODATA segmentation and shadow detection from large-scale aerial images

The automatic extraction of road features from remote sensed images has been a topic of great interest within the photogrammetric and remote sensing communities for over 3 decades. Although various techniques have been reported in the literature, it is still challenging to efficiently extract the road details with the increasing of image resolution as well as the requirement for accurate and up-to-date road data. In this paper, we will focus on the automatic detection of road lane markings, which are crucial for many applications, including lane level navigation and lane departure warning. The approach consists of four steps: i) data preprocessing, ii) image segmentation and road surface detection, iii) road lane marking extraction based on the generated road surface, and iv) testing and system evaluation. The proposed approach utilized the unsupervised ISODATA image segmentation algorithm, which segments the image into vegetation regions, and road surface based only on the Cb component of YCbCr color space. A shadow detection method based on YCbCr color space is also employed to detect and recover the shadows from the road surface casted by the vehicles and trees. Finally, the lane marking features are detected from the road surface using the histogram clustering. The experiments of applying the proposed method to the aerial imagery dataset of Gympie, Queensland demonstrate the efficiency of the approach.

Cities in the airports 'shadow' : underlying interests and discretionary power in airport-region development

The previously distinct boundary between airports and their cities has become increasingly blurred as new interests and actors are identified as important stakeholders in the decision making process. As a consequence airport entities are more than ever seeking an integrated existence with their surrounding regions. While current planning strategies provide insights on how to improve and leverage land use planning in and around airports, emerging challenges for implementing and protecting these planning ideals stem from the governance shadows of development decisions. The thesis of this paper is that improving the identification, articulation and consideration of city and airport interests in the development approval process (between planning and implementation) can help avoid outcomes that hinder the ability of cities and their airports to meet their separate/mutual long-term objectives. By applying a network governance perspective to the pilot case study of Brisbane, analysis of overlapping and competing actor interests show how different governance arrangements facilitate (or impede) decision making that protects sustainable ‘airport region’ development. ---------- Contributions are made to airport and city development decision makers through the identification and analysis of effective and ineffective decision making pathways, and to governance literature by way of forwarding empirically derived frameworks for showing how actors protect their interests in the ‘crowded decision making domain’ of airport region development. This work was carried out through the Airport Metropolis Research Project under the Australian Research Council’s Linkage Projects funding scheme (LP0775225).

The candidate : a novella and examination of Australian Gothic crime fiction

Australia is a land without haunted castles or subterranean corridors, without ancient graveyards or decaying monasteries, a land whose climate is rarely gloomy. Yet, the literary landscape is splattered with shades of the Gothic genre. This Gothic heritage is especially evident within elements of nineteenth century Australian sensation fiction. Australian crime fiction in the twentieth century, in keeping with this lineage, repeatedly employs elements of the Gothic, adapting and appropriating these conventions for literary effect. I believe that a ‘mélange’ of historical Gothic crime traditions could produce an exciting new mode of Gothic crime writing in the Australian context. As such, I have written a contemporary literary experiment in a Gothic crime ‘hybrid’ style: this novella forms my creative practice. The accompanying exegesis is a critical study of a selection of Australian literary works that exhibit the characteristics of both Gothic and crime genres. Through an analysis of these creative works, this study argues that the interlacing of Gothic traditions with crime writing conventions has been a noteworthy practice in Australian fiction during both the nineteenth and twentieth centuries and these literary tropes are interwoven in the writing of ‘The Candidate’, a Gothic crime novella.

Calculating shades of meaning in semantic spaces

This thesis introduces the problem of conceptual ambiguity, or Shades of Meaning (SoM) that can exist around a term or entity. As an example consider President Ronald Reagan the ex-president of the USA, there are many aspects to him that are captured in text; the Russian missile deal, the Iran-contra deal and others. Simply finding documents with the word “Reagan” in them is going to return results that cover many different shades of meaning related to "Reagan". Instead it may be desirable to retrieve results around a specific shade of meaning of "Reagan", e.g., all documents relating to the Iran-contra scandal. This thesis investigates computational methods for identifying shades of meaning around a word, or concept. This problem is related to word sense ambiguity, but is more subtle and based less on the particular syntactic structures associated with or around an instance of the term and more with the semantic contexts around it. A particularly noteworthy difference from typical word sense disambiguation is that shades of a concept are not known in advance. It is up to the algorithm itself to ascertain these subtleties. It is the key hypothesis of this thesis that reducing the number of dimensions in the representation of concepts is a key part of reducing sparseness and thus also crucial in discovering their SoMwithin a given corpus.

Graphcut-based interactive segmentation using colour and depth cues

Segmentation of novel or dynamic objects in a scene, often referred to as background sub- traction or foreground segmentation, is critical for robust high level computer vision applica- tions such as object tracking, object classifca- tion and recognition. However, automatic real- time segmentation for robotics still poses chal- lenges including global illumination changes, shadows, inter-re ections, colour similarity of foreground to background, and cluttered back- grounds. This paper introduces depth cues provided by structure from motion (SFM) for interactive segmentation to alleviate some of these challenges. In this paper, two prevailing interactive segmentation algorithms are com- pared; Lazysnapping [Li et al., 2004] and Grab- cut [Rother et al., 2004], both based on graph- cut optimisation [Boykov and Jolly, 2001]. The algorithms are extended to include depth cues rather than colour only as in the original pa- pers. Results show interactive segmentation based on colour and depth cues enhances the performance of segmentation with a lower er- ror with respect to ground truth.

Stop whining and make the best of it: a cross-national comparison of response to regional disadvantage in the wine industry

Our cross-national field study of wine entrepreneurship in the “wrong” places provides some redress to the focus of the “regional advantage” literature on places that have already won and on the firms that benefit from “clusters” and other centers of industry advantage. Regional “disadvantage” is at best a shadowy afterthought to this literature. By poking around in these shadows, we help to synthesize and extend the incipient yet burgeoning literature on entrepreneurial “resourcefulness” and we contribute to the developing body of insights and theory pertinent to the numerous but often ignored firms and startups that mostly need to worry about how they will compete at all now if they are ever to have of chance of “winning” in the future. The core of our findings suggests that understandable – though contested – processes of ingenuity underlie entrepreneurial responses to regional disadvantage. Because we study entrepreneurship that from many angles simply does not make sense, we are also able to proffer a novel perspective on entrepreneurial sensemaking.

The motivation and structure of corrupt police networks : theorising the dark side of the ‘thin blue line’

In recent times considerable research attention has been directed to understanding dark networks, especially criminal and terrorist networks. Dark networks are those in which member motivations are self rather than public interested, achievements come at the cost of other individuals, groups or societies and, in addition, their activities are both ‘covert and illegal’ (Raab & Milward, 2003: 415). This ‘darkness’ has implications for the way in which these networks are structured, the strategies adopted and their recruitment methods. Such entities exhibit distinctive operating characteristics including most notably the tension between creating an efficient network structure while retaining the ability to hide from public view while avoiding catastrophic collapse should one member cooperate with authorities (Bouchard 2007). While theoretical emphasis has been on criminal and terrorist networks, recent work has demonstrated that corrupt police networks exhibit some distinctive characteristics. In particular, these entities operate within the shadows of a host organisation - the Police Force and distort the functioning of the ‘Thin Blue Line’ as the interface between the law abiding citizenry and the criminal society. Drawing on data derived from the Queensland Fitzgerald Commission of Enquiry into Police Misconduct and related documents, this paper examines the motivations, structural properties and operational practices of corrupt police networks and compares and contrasts these with other dark networks with ‘bright’ public service networks. The paper confirms the structural differences between dark corrupt police networks and bright networks and suggests. However, structural embeddedness alone is found to be an insufficient theoretical explanation for member involvement in networks and that a set of elements combine to impact decision-making. Although offering important insights into network participation, the paper’s findings are especially pertinent in identifying additional points of intervention for police corruption networks.

Stop whining and make the best of it : a cross-national comparison of responses to regional disadvantage in the wine industry

Our cross-national field study of wine entrepreneurship in the “wrong” places provides some redress to the focus of the “regional advantage” literature on places that have already won and on the firms that benefit from “clusters” and other centers of industry advantage. Regional “disadvantage” is at best a shadowy afterthought to this literature. By poking around in these shadows, we help to synthesize and extend the incipient yet burgeoning literature on entrepreneurial “resourcefulness” and we contribute to the developing body of insights and theory pertinent to the numerous but often ignored firms and startups that mostly need to worry about how they will compete at all now if they are ever to have of chance of “winning” in the future. The core of our findings suggests that understandable – though contested – processes of ingenuity underlie entrepreneurial responses to regional disadvantage. Because we study entrepreneurship that from many angles simply does not make sense, we are also able to proffer a novel perspective on entrepreneurial sensemaking.

An integrated approach for precise road reconstruction from aerial imagery and LiDAR data

Accurate and detailed road models play an important role in a number of geospatial applications, such as infrastructure planning, traffic monitoring, and driver assistance systems. In this thesis, an integrated approach for the automatic extraction of precise road features from high resolution aerial images and LiDAR point clouds is presented. A framework of road information modeling has been proposed, for rural and urban scenarios respectively, and an integrated system has been developed to deal with road feature extraction using image and LiDAR analysis. For road extraction in rural regions, a hierarchical image analysis is first performed to maximize the exploitation of road characteristics in different resolutions. The rough locations and directions of roads are provided by the road centerlines detected in low resolution images, both of which can be further employed to facilitate the road information generation in high resolution images. The histogram thresholding method is then chosen to classify road details in high resolution images, where color space transformation is used for data preparation. After the road surface detection, anisotropic Gaussian and Gabor filters are employed to enhance road pavement markings while constraining other ground objects, such as vegetation and houses. Afterwards, pavement markings are obtained from the filtered image using the Otsu's clustering method. The final road model is generated by superimposing the lane markings on the road surfaces, where the digital terrain model (DTM) produced by LiDAR data can also be combined to obtain the 3D road model. As the extraction of roads in urban areas is greatly affected by buildings, shadows, vehicles, and parking lots, we combine high resolution aerial images and dense LiDAR data to fully exploit the precise spectral and horizontal spatial resolution of aerial images and the accurate vertical information provided by airborne LiDAR. Objectoriented image analysis methods are employed to process the feature classiffcation and road detection in aerial images. In this process, we first utilize an adaptive mean shift (MS) segmentation algorithm to segment the original images into meaningful object-oriented clusters. Then the support vector machine (SVM) algorithm is further applied on the MS segmented image to extract road objects. Road surface detected in LiDAR intensity images is taken as a mask to remove the effects of shadows and trees. In addition, normalized DSM (nDSM) obtained from LiDAR is employed to filter out other above-ground objects, such as buildings and vehicles. The proposed road extraction approaches are tested using rural and urban datasets respectively. The rural road extraction method is performed using pan-sharpened aerial images of the Bruce Highway, Gympie, Queensland. The road extraction algorithm for urban regions is tested using the datasets of Bundaberg, which combine aerial imagery and LiDAR data. Quantitative evaluation of the extracted road information for both datasets has been carried out. The experiments and the evaluation results using Gympie datasets show that more than 96% of the road surfaces and over 90% of the lane markings are accurately reconstructed, and the false alarm rates for road surfaces and lane markings are below 3% and 2% respectively. For the urban test sites of Bundaberg, more than 93% of the road surface is correctly reconstructed, and the mis-detection rate is below 10%.

Shades of grey

Poem published in Islet