Dramaturgie de la scène primitive et passion postcourtoise : le couple Judith et Holopherne dans L’âge d’homme et L’Afrique fantôme de Michel Leiris

Ce mémoire propose une réflexion psychanalytique à partir de L'Âge d'homme et de L'Afrique fantôme de Michel Leiris. Il y est montré que la notion freudienne de scène primitive sert de paradigme à l'écriture autobiographique de cet auteur. Cette étude commence par approfondir les récits de rêves dans ces textes. Il s'agit d'emprunter la voie royale vers l'inconscient. Le moi du narrateur adopte une position masochiste devant des femmes phalliques dont il s'éprend. C'est ce qui le fait basculer dans la passion postcourtoise, notion développée par Paul-Laurent Assoun. La Dame se substitue à l'objet perdu et se présente comme une Judith meurtrière. Les nombreuses identifications du narrateur avec des personnages légendaires nous permettent de transposer son existence à l'intérieur d'une mythologie personnelle. L'analyse de ces fantaisies d'identification avec des modèles masculins révèle le sadisme de son surmoi. À Gondar, l'auteur tombe amoureux d'Emawayish, une sorcière éthiopienne, qui devient son double idéal de Lucrèce et Judith. Cette relation amoureuse répète inconsciemment la structure du complexe d'Œdipe. L'Âge d'homme apparaît comme une recherche du corps maternel et l'écriture autobiographique devient le moyen de remédier au sentiment mélancolique de perte d'objet d'amour.

Gesturing elsewhere: the identity politics of the Balinese death/thrash metal scene

This essay explores the political significance of Balinese death/thrash fandom. In the early 1990s, the emergence of a death/thrash scene in Bali paralleled growing criticism of accelerated tourism development on the island. Specifically, locals protested the increasing ubiquity of Jakarta, 'the centre', cast as threatening to an authentically 'low', peripheral Balinese culture. Similarly, death/thrash enthusiasts also gravitated toward certain fringes, although they rejected dominant notions of Balinese-ness by gesturing elsewhere, toward a global scene. The essay explores the ways in which death/thrash enthusiasts engaged with local discourses by coveting their marginality, and aims to demonstrate how their articulations of 'alien-ness' contributed in important ways to a broader regionalism.

Creating a scene: Balinese punk's beginnings

The promotion of alternative music by deregulated television and recording industries, together with the increasingly felt presence of the metropolis, converged on Balinese cultural and physical landscapes in the 1990s. Mirroring developments in broader society, a regionalist discourse, which polarized notions of ‘centre’ and ‘periphery’, emerged among Balinese youth in the context of the local band scene. For certain musicians, musical authenticity was firmly rooted in a cultural and geographical locale, and was articulated by their abhorrence for socializing at shopping malls. In contrast, these Balinese alternative (including punk) musicians sought authenticity in a metropolitan elsewhere. This article is a case study of the indigenization of a ‘global’ code in a non-western periphery. It contests arguments for the ‘post-imperial’ nature of globalization, and demonstrates the continued salience of centre–periphery dialectics in local discourses. At the same time, the study attests to the progressive role a metropolitan superculture can play in cultural renewal in the periphery.

High Culture as Subculture : Brisbane's Contemporary Chamber Music Scene

The aim of the dissertation is to discover the extent to which methodologies and conceptual frameworks used to understand popular culture may also be useful in the attempt to understand contemporary high culture. The dissertation addresses this question through the application of subculture theory to Brisbane’s contemporary chamber music scene, drawing on a detailed case study of the contemporary chamber ensemble Topology and its audiences. The dissertation begins by establishing the logic and necessity of applying cultural studies methodologies to contemporary high culture. This argument is supported by a discussion of the conceptual relationships between cultural studies, high culture, and popular culture, and the methodological consequences of these relationships. In Chapter 2, a brief overview of interdisciplinary approaches to music reveals the central importance of subculture theory, and a detailed survey of the history of cultural studies research into music subcultures follows. Five investigative themes are identified as being crucial to all forms of contemporary subculture theory: the symbolic; the spatial; the social; the temporal; the ideological and political. Chapters 3 and 4 present the findings of the case study as they relate to these five investigative themes of contemporary subculture theory. Chapter 5 synthesises the findings of the previous two chapters, and argues that while participation in contemporary chamber music is not as intense or pervasive as is the case with the most researched street-based youth subcultures, it is nevertheless possible to describe Brisbane’s contemporary chamber music scene as a subculture. The dissertation closes by reflecting on the ways in which the subcultural analysis of contemporary chamber music has yielded some insight into the lived practices of high culture in contemporary urban contexts.

Scene invariant crowd counting for real-time surveillance

Automated crowd counting allows excessive crowding to be detected immediately, without the need for constant human surveillance. Current crowd counting systems are location specific, and for these systems to function properly they must be trained on a large amount of data specific to the target location. As such, configuring multiple systems to use is a tedious and time consuming exercise. We propose a scene invariant crowd counting system which can easily be deployed at a different location to where it was trained. This is achieved using a global scaling factor to relate crowd sizes from one scene to another. We demonstrate that a crowd counting system trained at one viewpoint can achieve a correct classification rate of 90% at a different viewpoint.

Wide-baseline keypoint detection and matching with wide-angle images for vision based localisation

This thesis addresses the problem of detecting and describing the same scene points in different wide-angle images taken by the same camera at different viewpoints. This is a core competency of many vision-based localisation tasks including visual odometry and visual place recognition. Wide-angle cameras have a large field of view that can exceed a full hemisphere, and the images they produce contain severe radial distortion. When compared to traditional narrow field of view perspective cameras, more accurate estimates of camera egomotion can be found using the images obtained with wide-angle cameras. The ability to accurately estimate camera egomotion is a fundamental primitive of visual odometry, and this is one of the reasons for the increased popularity in the use of wide-angle cameras for this task. Their large field of view also enables them to capture images of the same regions in a scene taken at very different viewpoints, and this makes them suited for visual place recognition. However, the ability to estimate the camera egomotion and recognise the same scene in two different images is dependent on the ability to reliably detect and describe the same scene points, or ‘keypoints’, in the images. Most algorithms used for this purpose are designed almost exclusively for perspective images. Applying algorithms designed for perspective images directly to wide-angle images is problematic as no account is made for the image distortion. The primary contribution of this thesis is the development of two novel keypoint detectors, and a method of keypoint description, designed for wide-angle images. Both reformulate the Scale- Invariant Feature Transform (SIFT) as an image processing operation on the sphere. As the image captured by any central projection wide-angle camera can be mapped to the sphere, applying these variants to an image on the sphere enables keypoints to be detected in a manner that is invariant to image distortion. Each of the variants is required to find the scale-space representation of an image on the sphere, and they differ in the approaches they used to do this. Extensive experiments using real and synthetically generated wide-angle images are used to validate the two new keypoint detectors and the method of keypoint description. The best of these two new keypoint detectors is applied to vision based localisation tasks including visual odometry and visual place recognition using outdoor wide-angle image sequences. As part of this work, the effect of keypoint coordinate selection on the accuracy of egomotion estimates using the Direct Linear Transform (DLT) is investigated, and a simple weighting scheme is proposed which attempts to account for the uncertainty of keypoint positions during detection. A word reliability metric is also developed for use within a visual ‘bag of words’ approach to place recognition.

Vision only pose estimation and scene reconstruction on airborne platforms

We aim to demonstrate unaided visual 3D pose estimation and map reconstruction using both monocular and stereo vision techniques. To date, our work has focused on collecting data from Unmanned Aerial Vehicles, which generates a number of significant issues specific to the application. Such issues include scene reconstruction degeneracy from planar data, poor structure initialisation for monocular schemes and difficult 3D reconstruction due to high feature covariance. Most modern Visual Odometry (VO) and related SLAM systems make use of a number of sensors to inform pose and map generation, including laser range-finders, radar, inertial units and vision [1]. By fusing sensor inputs, the advantages and deficiencies of each sensor type can be handled in an efficient manner. However, many of these sensors are costly and each adds to the complexity of such robotic systems. With continual advances in the abilities, small size, passivity and low cost of visual sensors along with the dense, information rich data that they provide our research focuses on the use of unaided vision to generate pose estimates and maps from robotic platforms. We propose that highly accurate (�5cm) dense 3D reconstructions of large scale environments can be obtained in addition to the localisation of the platform described in other work [2]. Using images taken from cameras, our algorithm simultaneously generates an initial visual odometry estimate and scene reconstruction from visible features, then passes this estimate to a bundle-adjustment routine to optimise the solution. From this optimised scene structure and the original images, we aim to create a detailed, textured reconstruction of the scene. By applying such techniques to a unique airborne scenario, we hope to expose new robotic applications of SLAM techniques. The ability to obtain highly accurate 3D measurements of an environment at a low cost is critical in a number of agricultural and urban monitoring situations. We focus on cameras as such sensors are small, cheap and light-weight and can therefore be deployed in smaller aerial vehicles. This, coupled with the ability of small aerial vehicles to fly near to the ground in a controlled fashion, will assist in increasing the effective resolution of the reconstructed maps.

Scene invariant crowd counting

This paper describes a scene invariant crowd counting algorithm that uses local features to monitor crowd size. Unlike previous algorithms that require each camera to be trained separately, the proposed method uses camera calibration to scale between viewpoints, allowing a system to be trained and tested on different scenes. A pre-trained system could therefore be used as a turn-key solution for crowd counting across a wide range of environments. The use of local features allows the proposed algorithm to calculate local occupancy statistics, and Gaussian process regression is used to scale to conditions which are unseen in the training data, also providing confidence intervals for the crowd size estimate. A new crowd counting database is introduced to the computer vision community to enable a wider evaluation over multiple scenes, and the proposed algorithm is tested on seven datasets to demonstrate scene invariance and high accuracy. To the authors' knowledge this is the first system of its kind due to its ability to scale between different scenes and viewpoints.

Scene invariant crowd counting and crowd occupancy analysis

In public places, crowd size may be an indicator of congestion, delay, instability, or of abnormal events, such as a fight, riot or emergency. Crowd related information can also provide important business intelligence such as the distribution of people throughout spaces, throughput rates, and local densities. A major drawback of many crowd counting approaches is their reliance on large numbers of holistic features, training data requirements of hundreds or thousands of frames per camera, and that each camera must be trained separately. This makes deployment in large multi-camera environments such as shopping centres very costly and difficult. In this chapter, we present a novel scene-invariant crowd counting algorithm that uses local features to monitor crowd size. The use of local features allows the proposed algorithm to calculate local occupancy statistics, scale to conditions which are unseen in the training data, and be trained on significantly less data. Scene invariance is achieved through the use of camera calibration, allowing the system to be trained on one or more viewpoints and then deployed on any number of new cameras for testing without further training. A pre-trained system could then be used as a ‘turn-key’ solution for crowd counting across a wide range of environments, eliminating many of the costly barriers to deployment which currently exist.

Unusual scene detection using distributed behaviour model and sparse representation

The ability to detect unusual events in surviellance footage as they happen is a highly desireable feature for a surveillance system. However, this problem remains challenging in crowded scenes due to occlusions and the clustering of people. In this paper, we propose using the Distributed Behavior Model (DBM), which has been widely used in computer graphics, for video event detection. Our approach does not rely on object tracking, and is robust to camera movements. We use sparse coding for classification, and test our approach on various datasets. Our proposed approach outperforms a state-of-the-art work which uses the social force model and Latent Dirichlet Allocation.

Analytical electron microscopy of fine-grained phases in primitive interplanetary dust particles and carbonaceous chondrites

In order to describe the total mineralogical diversity within primitive extraterrestrial materials, individual interplanetary dust particles (IDPs) collected from the stratosphere as part of the JSC Cosmic Dust Curatorial Program were analyzed using a var ...

Poorly graphitized carbon as a new cosmothermometer for primitive extraterrestrial materials

The presence of carbon in primitive extraterrestrial materials has long been considered a useful indicator of prevailing geochemical conditions early in the formation of the Solar System. A recent addition to the suite of primitive materials available for study by cosmochemists includes particles collected from the stratosphere called chondritic porous (CP) aggregates1. Carbon-rich CP aggregates are less abundant in stratospheric collections and contain many low-temperature phases (such as layer silicates) as minor components2,3. We describe here the nature of the most abundant carbon phase in a carbon-rich CP aggregate (sample no. W7029* A) collected from the stratosphere as part of the Johnson Space Center (JSC) Cosmic Dust Program4. By comparison with experimental and terrestrial studies of poorly graphitized carbon (PGC), we show that the graphitization temperature, or the degree of ordering in the PGC, may provide a useful cosmothermometer for primitive extraterrestrial materials.

Scene invariant multi camera crowd counting

Automated crowd counting has become an active field of computer vision research in recent years. Existing approaches are scene-specific, as they are designed to operate in the single camera viewpoint that was used to train the system. Real world camera networks often span multiple viewpoints within a facility, including many regions of overlap. This paper proposes a novel scene invariant crowd counting algorithm that is designed to operate across multiple cameras. The approach uses camera calibration to normalise features between viewpoints and to compensate for regions of overlap. This compensation is performed by constructing an 'overlap map' which provides a measure of how much an object at one location is visible within other viewpoints. An investigation into the suitability of various feature types and regression models for scene invariant crowd counting is also conducted. The features investigated include object size, shape, edges and keypoints. The regression models evaluated include neural networks, K-nearest neighbours, linear and Gaussian process regresion. Our experiments demonstrate that accurate crowd counting was achieved across seven benchmark datasets, with optimal performance observed when all features were used and when Gaussian process regression was used. The combination of scene invariance and multi camera crowd counting is evaluated by training the system on footage obtained from the QUT camera network and testing it on three cameras from the PETS 2009 database. Highly accurate crowd counting was observed with a mean relative error of less than 10%. Our approach enables a pre-trained system to be deployed on a new environment without any additional training, bringing the field one step closer toward a 'plug and play' system.