Festival Images: brand image and stakeholders' brand relationship types at the Edinburgh Festival Fringe.

The Edinburgh Festival Fringe (The Fringe) is the largest arts festival in the world and it has inspired the creation of similar festivals world-wide. Since its conception in 1947, the Fringe has demonstrated significant growth in visitor numbers; ticket sales; and its economic contribution. Despite this, the sustainable future of Edinburgh’s festivals is debated as Edinburgh, ‘the Festival City’, faces threats from other festival destinations. Festivals position Edinburgh creatively in contrast to the city’s traditionally perceived image as a cultural-historic centre. Despite this, little research has been undertaken into the creative and cultural significance of Edinburgh’s festivals, including the Fringe. This interdisciplinary research grounded in marketing, tourism, and festival and event management; and underpinned by constructivism, presents an understanding of types of brand relationships that exist between the Fringe and its primary stakeholders. This is achieved through defining both the Fringe brand image and its primary stakeholders; and applying these definitions to the development of a typology of Fringe-stakeholders’ brand relationships. The significance of this study is evident within its topic of inquiry and the research methods applied. In the little-considered arena of arts festivals and their stakeholders, this is the first in-depth study into the Fringe as a festival and festival brand. Within this, the definition of a Fringe brand image contributes to understanding the cultural and creative significance of the Fringe. Furthermore, this research contributes a unique understanding of the types of stakeholders that are engaged with the Fringe. The types of brand relationships that exist between these stakeholders and the Fringe are another significant contribution to knowledge and understanding. While specific to the present context, these findings may prove transferable to further festivals or events, and related areas and industries. The contribution made by this research to the methodological developments in festival and event studies is of additional significance. The application of visual research methods, including semiotic analysis and photo-elicitation within phenomenological interviews, has previously been applied in marketing, consumer, and tourism research, but not to the understanding of festival brands and stakeholders’ brand relationship types. Findings of this research illustrate that existing marketing and consumer brand frameworks and stakeholder theories are applicable to festivals. Further, it is possible to define ‘a’ Fringe brand image which is subjective and contradictory. The unique open-access and organic, operational model of the Fringe facilitates its many contributors, and consumers. Fringe stakeholders may be categorised according to their level of engagement with the Fringe (as primary or secondary) and their particular stakeholder role(s), which are varied and multiple. Fringe-stakeholder brand relationship types are overwhelmingly positive; and are based upon interpersonal relationship dimensions (including friendships, marriages, kinships and partnerships). Fringe-stakeholder brand relationship types can be classified therefore as having similar dimensions to those brand relationship types previously described for consumer products and brands.

The Depiction of Coronal Structure in White-Light Images

Morgan, Huw, Habbal, S.R., Woo, R., (2006) 'The Depiction of Coronal Structure in White-Light Images', Solar Physics 236(2) pp.263-272 RAE2008

Accurate Range Image Registration: Eliminating or Modelling Outliers

Liu, Yonghuai, Liu, Honghai, Li, Longzhuang, Wei, Baogang. Accurate Range Image Registration: Eliminating or Modelling Outliers. Proceedings of 12th IEEE Conference on Emerging Technologies and Factory Automation, 2007, pp. 1316-1323. Sponsorship: IEEE

Eliminating False Matches for the Projective Registration of Free-Form Surfaces with Small Translational Motions

Liu, Yonghuai. Eliminating False Matches for the Projective Registration of Free-Form Surfaces with Small Translational Motions. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, vol. 35, no. 3, pp. 607-624, 2005.

Automatic 3d free form shape matching using the graduated assignment algorithm.

Liu, Yonghuai. Automatic 3d free form shape matching using the graduated assignment algorithm. Pattern Recognition, vol. 38, no. 10, pp. 1615-1631, 2005.

Synthetic biology: a utilitarian perspective

I examine the positive and negative features of synthetic biology (‘SynBio’) from a utilitarian ethical perspective. The potential beneficial outcomes from SynBio in the context of medicine are substantial; however it is not presently possible to predict precise outcomes due to the nascent state of the field. Potential negative outcomes from SynBio also exist, including iatrogenesis and bioterrorism; however it is not yet possible to quantify these risks. I argue that the application of a ‘precautionary’ approach to SynBio is ethically fraught, as is the notion that SynBio-associated knowledge ought to be restricted. I conclude that utilitarians ought to support a broadly laissez-faire stance in respect of SynBio.

Words, Images, Enemies : Securitization and International Politics

Williams, Mike, 'Words, Images, Enemies: Securitization and International Politics', International Studies Quarterly (2003) 47(4) pp.511-531 RAE2008

Religion and the life attitudes and self-images of American adolescents

The purpose of the project is to research the shape and influence of religion and spirituality in the lives of U.S. adolescents; to identify effective practices in the religious, moral, and social formation of the lives of youth; to describe the extent to which youth participate in and benefit from the programs and opportunities that religious communities are offering to their youth; and to foster an informed national discussion about the influence of religion in youth's lives, in order to encourage sustained reflection about and rethinking of our cultural and institutional practices with regard to youth and religion.

Estimating 3D Hand Pose from a Cluttered Image

A method is proposed that can generate a ranked list of plausible three-dimensional hand configurations that best match an input image. Hand pose estimation is formulated as an image database indexing problem, where the closest matches for an input hand image are retrieved from a large database of synthetic hand images. In contrast to previous approaches, the system can function in the presence of clutter, thanks to two novel clutter-tolerant indexing methods. First, a computationally efficient approximation of the image-to-model chamfer distance is obtained by embedding binary edge images into a high-dimensional Euclide an space. Second, a general-purpose, probabilistic line matching method identifies those line segment correspondences between model and input images that are the least likely to have occurred by chance. The performance of this clutter-tolerant approach is demonstrated in quantitative experiments with hundreds of real hand images.

Head Tracking via Robust Registration in Texture Map Images

A novel method for 3D head tracking in the presence of large head rotations and facial expression changes is described. Tracking is formulated in terms of color image registration in the texture map of a 3D surface model. Model appearance is recursively updated via image mosaicking in the texture map as the head orientation varies. The resulting dynamic texture map provides a stabilized view of the face that can be used as input to many existing 2D techniques for face recognition, facial expressions analysis, lip reading, and eye tracking. Parameters are estimated via a robust minimization procedure; this provides robustness to occlusions, wrinkles, shadows, and specular highlights. The system was tested on a variety of sequences taken with low quality, uncalibrated video cameras. Experimental results are reported.

3D Hand Pose Estimation by Finding Appearance-Based Matches in a Large Database of Training Views

Ongoing work towards appearance-based 3D hand pose estimation from a single image is presented. A large database of synthetic hand views is generated using a 3D hand model and computer graphics. The views display different hand shapes as seen from arbitrary viewpoints. Each synthetic view is automatically labeled with parameters describing its hand shape and viewing parameters. Given an input image, the system retrieves the most similar database views, and uses the shape and viewing parameters of those views as candidate estimates for the parameters of the input image. Preliminary results are presented, in which appearance-based similarity is defined in terms of the chamfer distance between edge images.

An Appearance-Based Framework for 3D Hand Shape Classification and Camera Viewpoint Estimation

An appearance-based framework for 3D hand shape classification and simultaneous camera viewpoint estimation is presented. Given an input image of a segmented hand, the most similar matches from a large database of synthetic hand images are retrieved. The ground truth labels of those matches, containing hand shape and camera viewpoint information, are returned by the system as estimates for the input image. Database retrieval is done hierarchically, by first quickly rejecting the vast majority of all database views, and then ranking the remaining candidates in order of similarity to the input. Four different similarity measures are employed, based on edge location, edge orientation, finger location and geometric moments.

Specialized Mappings Architecture with Applications to Vision-based Estimation of Articulated Body Pose

A fundamental task of vision systems is to infer the state of the world given some form of visual observations. From a computational perspective, this often involves facing an ill-posed problem; e.g., information is lost via projection of the 3D world into a 2D image. Solution of an ill-posed problem requires additional information, usually provided as a model of the underlying process. It is important that the model be both computationally feasible as well as theoretically well-founded. In this thesis, a probabilistic, nonlinear supervised computational learning model is proposed: the Specialized Mappings Architecture (SMA). The SMA framework is demonstrated in a computer vision system that can estimate the articulated pose parameters of a human body or human hands, given images obtained via one or more uncalibrated cameras. The SMA consists of several specialized forward mapping functions that are estimated automatically from training data, and a possibly known feedback function. Each specialized function maps certain domains of the input space (e.g., image features) onto the output space (e.g., articulated body parameters). A probabilistic model for the architecture is first formalized. Solutions to key algorithmic problems are then derived: simultaneous learning of the specialized domains along with the mapping functions, as well as performing inference given inputs and a feedback function. The SMA employs a variant of the Expectation-Maximization algorithm and approximate inference. The approach allows the use of alternative conditional independence assumptions for learning and inference, which are derived from a forward model and a feedback model. Experimental validation of the proposed approach is conducted in the task of estimating articulated body pose from image silhouettes. Accuracy and stability of the SMA framework is tested using artificial data sets, as well as synthetic and real video sequences of human bodies and hands.

On Clustering Images Using Compression

The need for the ability to cluster unknown data to better understand its relationship to know data is prevalent throughout science. Besides a better understanding of the data itself or learning about a new unknown object, cluster analysis can help with processing data, data standardization, and outlier detection. Most clustering algorithms are based on known features or expectations, such as the popular partition based, hierarchical, density-based, grid based, and model based algorithms. The choice of algorithm depends on many factors, including the type of data and the reason for clustering, nearly all rely on some known properties of the data being analyzed. Recently, Li et al. proposed a new universal similarity metric, this metric needs no prior knowledge about the object. Their similarity metric is based on the Kolmogorov Complexity of objects, the objects minimal description. While the Kolmogorov Complexity of an object is not computable, in "Clustering by Compression," Cilibrasi and Vitanyi use common compression algorithms to approximate the universal similarity metric and cluster objects with high success. Unfortunately, clustering using compression does not trivially extend to higher dimensions. Here we outline a method to adapt their procedure to images. We test these techniques on images of letters of the alphabet.

3D Trajectory Recovery for Tracking Multiple Objects and Trajectory Guided Recognition of Actions

A mechanism is proposed that integrates low-level (image processing), mid-level (recursive 3D trajectory estimation), and high-level (action recognition) processes. It is assumed that the system observes multiple moving objects via a single, uncalibrated video camera. A novel extended Kalman filter formulation is used in estimating the relative 3D motion trajectories up to a scale factor. The recursive estimation process provides a prediction and error measure that is exploited in higher-level stages of action recognition. Conversely, higher-level mechanisms provide feedback that allows the system to reliably segment and maintain the tracking of moving objects before, during, and after occlusion. The 3D trajectory, occlusion, and segmentation information are utilized in extracting stabilized views of the moving object. Trajectory-guided recognition (TGR) is proposed as a new and efficient method for adaptive classification of action. The TGR approach is demonstrated using "motion history images" that are then recognized via a mixture of Gaussian classifier. The system was tested in recognizing various dynamic human outdoor activities; e.g., running, walking, roller blading, and cycling. Experiments with synthetic data sets are used to evaluate stability of the trajectory estimator with respect to noise.