Looking beyond image and tourism : the role of flagship cultural projects in local arts development

State and local governments frequently look to flagship cultural projects to improve the city image and catalyze tourism but, in the process, often overlook their potential to foster local arts development. To better understand this role, the article examines if and how cultural institutions in Los Angeles and San Francisco attract and support arts-related activity. The analysis reveals that cultural flagships have mixed success in generating arts-based development and that their ability may be improved through attention to the local context, facility and institutional characteristics, and the approach of the sponsoring agencies. Such knowledge is useful for planners to enhance their revitalization efforts, particularly as the economic development potential of arts organizations and artists has become more apparent.

A digital watermarking framework with application to medical image security

Dealing with digital medical images is raising many new security problems with legal and ethical complexities for local archiving and distant medical services. These include image retention and fraud, distrust and invasion of privacy. This project was a significant step forward in developing a complete framework for systematically designing, analyzing, and applying digital watermarking, with a particular focus on medical image security. A formal generic watermarking model, three new attack models, and an efficient watermarking technique for medical images were developed. These outcomes contribute to standardizing future research in formal modeling and complete security and computational analysis of watermarking schemes.

Digital image watermarking: its formal model, fundamental properties and possible attacks

While formal definitions and security proofs are well established in some fields like cryptography and steganography, they are not as evident in digital watermarking research. A systematic development of watermarking schemes is desirable, but at present their development is usually informal, ad hoc, and omits the complete realization of application scenarios. This practice not only hinders the choice and use of a suitable scheme for a watermarking application, but also leads to debate about the state-of-the-art for different watermarking applications. With a view to the systematic development of watermarking schemes, we present a formal generic model for digital image watermarking. Considering possible inputs, outputs, and component functions, the initial construction of a basic watermarking model is developed further to incorporate the use of keys. On the basis of our proposed model, fundamental watermarking properties are defined and their importance exemplified for different image applications. We also define a set of possible attacks using our model showing different winning scenarios depending on the adversary capabilities. It is envisaged that with a proper consideration of watermarking properties and adversary actions in different image applications, use of the proposed model would allow a unified treatment of all practically meaningful variants of watermarking schemes.

Contrast transfer function correction applied to cryo-electron tomography and sub-tomogram averaging

Cryo-electron tomography together with averaging of sub-tomograms containing identical particles can reveal the structure of proteins or protein complexes in their native environment. The resolution of this technique is limited by the contrast transfer function (CTF) of the microscope. The CTF is not routinely corrected in cryo-electron tomography because of difficulties including CTF detection, due to the low signal to noise ratio, and CTF correction, since images are characterised by a spatially variant CTF. Here we simulate the effects of the CTF on the resolution of the final reconstruction, before and after CTF correction, and consider the effect of errors and approximations in defocus determination. We show that errors in defocus determination are well tolerated when correcting a series of tomograms collected at a range of defocus values. We apply methods for determining the CTF parameters in low signal to noise images of tilted specimens, for monitoring defocus changes using observed magnification changes, and for correcting the CTF prior to reconstruction. Using bacteriophage PRDI as a test sample, we demonstrate that this approach gives an improvement in the structure obtained by sub-tomogram averaging from cryo-electron tomograms.

Attracting and Retaining Staff : The Role of Branding and Industry Image

In increasingly competitive labour markets, attracting and retaining talent has become a prime concern of organisations. Employers need to understand the range of factors that influence career decision making and the role of employer branding in attracting human capital that best fits and contributes to the strategic aims of an organisation. This chapter identifies the changing factors that attract people to certain employment and industries and discusses the importance of aligning employer branding with employee branding to create a strong, genuine and lasting employer brand. Whilst organisations have long used marketing and branding practices to engender loyalty in customers, they are increasingly expanding this activity to differentiate organisations and make them attractive from an employee perspective. This chapter discusses employer branding and industry image as two important components of attraction strategies and describes ways companies can maximise their brand awareness in the employment market to both current and future employees.

A comparison of techniques for the reconstruction of leaf surfaces from scanned data

The foliage of a plant performs vital functions. As such, leaf models are required to be developed for modelling the plant architecture from a set of scattered data captured using a scanning device. The leaf model can be used for purely visual purposes or as part of a further model, such as a fluid movement model or biological process. For these reasons, an accurate mathematical representation of the surface and boundary is required. This paper compares three approaches for fitting a continuously differentiable surface through a set of scanned data points from a leaf surface, with a technique already used for reconstructing leaf surfaces. The techniques which will be considered are discrete smoothing D2-splines [R. Arcangeli, M. C. Lopez de Silanes, and J. J. Torrens, Multidimensional Minimising Splines, Springer, 2004.], the thin plate spline finite element smoother [S. Roberts, M. Hegland, and I. Altas, Approximation of a Thin Plate Spline Smoother using Continuous Piecewise Polynomial Functions, SIAM, 1 (2003), pp. 208--234] and the radial basis function Clough-Tocher method [M. Oqielat, I. Turner, and J. Belward, A hybrid Clough-Tocher method for surface fitting with application to leaf data., Appl. Math. Modelling, 33 (2009), pp. 2582-2595]. Numerical results show that discrete smoothing D2-splines produce reconstructed leaf surfaces which better represent the original physical leaf.

Human identification from video using advanced gait recognition techniques

The solutions proposed in this thesis contribute to improve gait recognition performance in practical scenarios that further enable the adoption of gait recognition into real world security and forensic applications that require identifying humans at a distance. Pioneering work has been conducted on frontal gait recognition using depth images to allow gait to be integrated with biometric walkthrough portals. The effects of gait challenging conditions including clothing, carrying goods, and viewpoint have been explored. Enhanced approaches are proposed on segmentation, feature extraction, feature optimisation and classification elements, and state-of-the-art recognition performance has been achieved. A frontal depth gait database has been developed and made available to the research community for further investigation. Solutions are explored in 2D and 3D domains using multiple images sources, and both domain-specific and independent modality gait features are proposed.

Responsible reconstruction : The architect's role

This paper investigates the role of the architect in post-disaster reconstruction and questions their ability to facilitate per- manent building solutions. There is an ever-increasing population of refugees and internally displaced persons due to disasters and conflicts who have a basic need for shelter. To date, housing solutions for such people has tended to focus on short-term, temporary shelter solutions that have been largely unsuccessful. This increasing demand for shelter has led to an emerging group of architects skilled in post-disaster reconstruction. These architects acknowledge that shelter is critical to survival, but believe architects should focus on rebuilding in a manner that is quick, durable but permanent. They believe that an architect skilled in post-disaster reconstruction can produce solutions that meet the requirement of the emergency phase, through to semi-permanent and even permanent homes, without wasting time and money on interim shelters. Case Study Research was used to examine and evaluate the assistance provided by Emergency Architects Australia (EAA) to the Kei Gold community in the Solomon Islands after the 2007 earthquake and tsunami. The results indicate that an architect’s response to a disaster must go beyond providing temporary shelter; they must create permanent building solutions that respond to the site and the culture while servicing the needs of the communi- ty. The vernacular reconstruction methods implemented by EAA in Kei Gold Village have been successful in develop- ing permanent housing solutions. Further research and development is required to gain a broader understanding of the role of the architect in disasters of varying scales and typologies.

When the five giants of post war reconstruction meet the four pillars of welfare reform-young people lose out, again

This paper discusses proposed changes to the Australian welfare state in the Welfare Review chaired by Patrick McClure and launched by Kevin Andrews, Minister for Social Services in the Abbott government, in a recent address to the Sydney Institute. Andrews cited the Beveridge Report of 1942, referring to Lord William Beveridge as the “godfather of the British post-war welfare state”, commending him for putting forward a plan for a welfare state providing a minimal level of support, constituting a bare safety net, rather than “stifling civil society and personal responsibility” through generous provision. In line with a key TASA conference theme of challenging institutions and identifying social and political change at local and global levels, this paper examines both the Beveridge Report and the McClure Report, identifying key issues and themes of relevance to current times in Australia.

Pre-processing for approximate Bayesian computation in image analysis

Most of the existing algorithms for approximate Bayesian computation (ABC) assume that it is feasible to simulate pseudo-data from the model at each iteration. However, the computational cost of these simulations can be prohibitive for high dimensional data. An important example is the Potts model, which is commonly used in image analysis. Images encountered in real world applications can have millions of pixels, therefore scalability is a major concern. We apply ABC with a synthetic likelihood to the hidden Potts model with additive Gaussian noise. Using a pre-processing step, we fit a binding function to model the relationship between the model parameters and the synthetic likelihood parameters. Our numerical experiments demonstrate that the precomputed binding function dramatically improves the scalability of ABC, reducing the average runtime required for model fitting from 71 hours to only 7 minutes. We also illustrate the method by estimating the smoothing parameter for remotely sensed satellite imagery. Without precomputation, Bayesian inference is impractical for datasets of that scale.

The use of a Riesz fractional differential-based approach for texture enhancement in image processing

Texture enhancement is an important component of image processing that finds extensive application in science and engineering. The quality of medical images, quantified using the imaging texture, plays a significant role in the routine diagnosis performed by medical practitioners. Most image texture enhancement is performed using classical integral order differential mask operators. Recently, first order fractional differential operators were used to enhance images. Experimentation with these methods led to the conclusion that fractional differential operators not only maintain the low frequency contour features in the smooth areas of the image, but they also nonlinearly enhance edges and textures corresponding to high frequency image components. However, whilst these methods perform well in particular cases, they are not routinely useful across all applications. To this end, we apply the second order Riesz fractional differential operator to improve upon existing approaches of texture enhancement. Compared with the classical integral order differential mask operators and other first order fractional differential operators, we find that our new algorithms provide higher signal to noise values and superior image quality.

Pollen image classification using the classifynder system: Algorithm comparison and a case study on New Zealand honey

We describe an investigation into how Massey University’s Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University’s pollen reference collection (2,890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set.We additionally work through a real world case study where we assess the ability of the system to determine the pollen make-up of samples of New Zealand honey. In addition to the Classifynder’s native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples.

Identifying weak linear features with the "coalescing shortest path image transform"

The detection of line-like features in images finds many applications in microanalysis. Actin fibers, microtubules, neurites, pilis, DNA, and other biological structures all come up as tenuous curved lines in microscopy images. A reliable tracing method that preserves the integrity and details of these structures is particularly important for quantitative analyses. We have developed a new image transform called the "Coalescing Shortest Path Image Transform" with very encouraging properties. Our scheme efficiently combines information from an extensive collection of shortest paths in the image to delineate even very weak linear features. © Copyright Microscopy Society of America 2011.

DroplIT, an improved image analysis method for droplet identification in high-throughput crystallization trials

The application of robotics to protein crystallization trials has resulted in the production of millions of images. Manual inspection of these images to find crystals and other interesting outcomes is a major rate-limiting step. As a result there has been intense activity in developing automated algorithms to analyse these images. The very first step for most systems that have been described in the literature is to delineate each droplet. Here, a novel approach that reaches over 97% success rate and subsecond processing times is presented. This will form the seed of a new high-throughput system to scrutinize massive crystallization campaigns automatically. © 2010 International Union of Crystallography Printed in Singapore-all rights reserved.