Frequency decomposition techniques for increased discriminative 3D facial information capture

Eigen-based techniques and other monolithic approaches to face recognition have long been a cornerstone in the face recognition community due to the high dimensionality of face images. Eigen-face techniques provide minimal reconstruction error and limit high-frequency content while linear discriminant-based techniques (fisher-faces) allow the construction of subspaces which preserve discriminatory information. This paper presents a frequency decomposition approach for improved face recognition performance utilising three well-known techniques: Wavelets; Gabor / Log-Gabor; and the Discrete Cosine Transform. Experimentation illustrates that frequency domain partitioning prior to dimensionality reduction increases the information available for classification and greatly increases face recognition performance for both eigen-face and fisher-face approaches.

Relationship Based Procurement Strategies for the 21st Century

This book Relationship-based Procurement Strategies for the 21st Century, is an important foundation document to better understand social and industry drivers from traditional adversarial contracting techniques to a more relationship-based approach building on the strengths of individual partners. This publication has evolved from the Commonwealth Government’s sponsorship of the case study of The National Museum of Australia Project—the first building construction project (as distinct from a resource development or engineering project) undertaken by a project alliance anywhere in the world.

Project alliancing at National Museum of Australia : the collaborative process

Project alliancing is a new alternative to traditional project delivery systems, especially in the commercial building sector. The Collaborative Process is a theoretical model of people and systems characteristics that are required to reduce the adversarial nature of most construction projects. Although developed separately, both are responses to the same pressures. Project alliancing was just used successfully to complete the National Museum of Australia. This project was analyzed as a case study to determine the extent to which it could be classified as a “collaborative project”. Five key elements of The Collaborative Process were reviewed and numerous examples from the management of this project were cited that support the theoretical recommendations of this model. In the case of this project, significant added value was delivered to the client and many innovations resulted from the collective work of the parties to the contract. It was concluded that project alliances for commercial buildings offer many advantages over traditional project delivery systems, which are related to increasing the levels of collaboration among a project management team.

Project alliancing Vs project partnering : a case study of the Australian National Museum project

Significant differences between project partnering and project alliancing occur in the selection process, management structure of the organisations undertaking the project and nature of risk and reward incentives. This paper helps clarify the nature of project alliancing and how alliance member organisations were selected for this case study. A core issue that differentiates between the two approaches is that in partnering, partners may reap rewards at the expense of other partners. In alliancing each alliance member places their profit margin and reward structure ÁÁat riskÂÂ. Thus in alliancing, the entire alliance entity either benefits together or not all. This fundamentally changes the motivation and dynamics of the relationship between alliance members.

Addressing sustainable living using a collaborative approach and multi-disciplinary techniques

In order to achieve meaningful reductions in individual ecological footprints, individuals must dramatically alter their day to day behaviours. Effective interventions will need to be evidence based and there is a necessity for the rapid transfer or communication of information from the point of research, into policy and practice. A number of health disciplines, including psychology and public health, share a common mission to promote health and well-being and it is becoming clear that the most practical pathway to achieving this mission is through interdisciplinary collaboration. This paper argues that an interdisciplinary collaborative approach will facilitate research that results in the rapid transfer of findings into policy and practice. The application of this approach is described in relation to the Green Living project which explored the psycho-social predictors of environmentally friendly behaviour. Following a qualitative pilot study, and in consultation with an expert panel comprising academics, industry professionals and government representatives, a self-administered mail survey was distributed to a random sample of 3000 residents of Brisbane and Moreton Bay (Queensland, Australia). The Green Living survey explored specific beliefs which included attitudes, norms, perceived control, intention and behaviour, as well as a number of other constructs such as environmental concern and altruism. This research has two beneficial outcomes. First, it will inform a practical model for predicting sustainable living behaviours and a number of local councils have already expressed an interest in making use of the results as part of their ongoing community engagement programs. Second, it provides an example of how a collaborative interdisciplinary project can provide a more comprehensive approach to research than can be accomplished by a single disciplinary project.

A methodology for object identification and tracking in construction based on spatial modeling and image matching techniques

Understanding the motion characteristics of on-site objects is desirable for the analysis of construction work zones, especially in problems related to safety and productivity studies. This article presents a methodology for rapid object identification and tracking. The proposed methodology contains algorithms for spatial modeling and image matching. A high-frame-rate range sensor was utilized for spatial data acquisition. The experimental results indicated that an occupancy grid spatial modeling algorithm could quickly build a suitable work zone model from the acquired data. The results also showed that an image matching algorithm is able to find the most similar object from a model database and from spatial models obtained from previous scans. It is then possible to use the matched information to successfully identify and track objects.

An example of developing a business model for information and communication technologies (ICT) adoption on construction projects – the National Museum of Australia project

Effective use of information and communication technologies (ICT) is necessary for delivering efficiency and improved project delivery in the construction industry. Convincing clients or contracting organisations to embrace ICT is a difficult task, there are few templates of an ICT business model for the industry to use. ICT application in the construction industry is relatively low compared to automotive and aerospace industries. The National Museum of Australia project provides a unique opportunity for investigating and reporting on this deficiency in publicly available knowledge. Concentrates on the business model content and objectives, briefly indicates the evaluation framework that was used to evaluate ICT effectiveness.

Case study of the National Museum (Action Peninsula) project

The Acton Peninsula project alliance is the first project alliance in building construction in the world. The project alliance is set out to achieve the best possible outcome for the project with all participants in the alliance sharing both risks and rewards. The construction of the National Museum of Australia and the Australian Institute of Aboriginal and Torres Strait Islander Studies, on Acton Peninsula in Canberra, will be a significant Australian architectural and construction achievement. The design and construction project team is committed to achieve outstanding results in all aspects of the design, construction and delivery of this significant national project. Innovation and creativity are valued, and outstanding performance will be rewarded.

Machine learning techniques to improve software quality

A significant proportion of the cost of software development is due to software testing and maintenance. This is in part the result of the inevitable imperfections due to human error, lack of quality during the design and coding of software, and the increasing need to reduce faults to improve customer satisfaction in a competitive marketplace. Given the cost and importance of removing errors improvements in fault detection and removal can be of significant benefit. The earlier in the development process faults can be found, the less it costs to correct them and the less likely other faults are to develop. This research aims to make the testing process more efficient and effective by identifying those software modules most likely to contain faults, allowing testing efforts to be carefully targeted. This is done with the use of machine learning algorithms which use examples of fault prone and not fault prone modules to develop predictive models of quality. In order to learn the numerical mapping between module and classification, a module is represented in terms of software metrics. A difficulty in this sort of problem is sourcing software engineering data of adequate quality. In this work, data is obtained from two sources, the NASA Metrics Data Program, and the open source Eclipse project. Feature selection before learning is applied, and in this area a number of different feature selection methods are applied to find which work best. Two machine learning algorithms are applied to the data - Naive Bayes and the Support Vector Machine - and predictive results are compared to those of previous efforts and found to be superior on selected data sets and comparable on others. In addition, a new classification method is proposed, Rank Sum, in which a ranking abstraction is laid over bin densities for each class, and a classification is determined based on the sum of ranks over features. A novel extension of this method is also described based on an observed polarising of points by class when rank sum is applied to training data to convert it into 2D rank sum space. SVM is applied to this transformed data to produce models the parameters of which can be set according to trade-off curves to obtain a particular performance trade-off.