2D-3D Face Recognition Based on PCA and Feature Modelling

Hybrid face recognition, using image (2D) and structural (3D) information, has explored the fusion of Nearest Neighbour classifiers. This paper examines the effectiveness of feature modelling for each individual modality, 2D and 3D. Furthermore, it is demonstrated that the fusion of feature modelling techniques for the 2D and 3D modalities yields performance improvements over the individual classifiers. By fusing the feature modelling classifiers for each modality with equal weights the average Equal Error Rate improves from 12.60% for the 2D classifier and 12.10% for the 3D classifier to 7.38% for the Hybrid 2D+3D clasiffier.

Improving behaviour classification consistency: a technique from biological taxonomy

Quantitative behaviour analysis requires the classification of behaviour to produce the basic data. In practice, much of this work will be performed by multiple observers, and maximising inter-observer consistency is of particular importance. Another discipline where consistency in classification is vital is biological taxonomy. A classification tool of great utility, the binary key, is designed to simplify the classification decision process and ensure consistent identification of proper categories. We show how this same decision-making tool - the binary key - can be used to promote consistency in the classification of behaviour. The construction of a binary key also ensures that the categories in which behaviour is classified are complete and non-overlapping. We discuss the general principles of design of binary keys, and illustrate their construction and use with a practical example from education research.

Assessing the structure of learning outcomes from counselling using the SOLO taxonomy: an exploratory study

Thirty-five clients who had received counselling completed a letter to a friend describing in as much detail as possible what they had learned from counselling. The participants' written responses were analysed and classified using the Structure of Learning Outcomes (SOLO) taxonomy. The results suggested that an expanded SOLO offers a promising and exciting way to view the outcomes of counselling within a learning framework. If the SOLO taxonomy is found to be stable in subsequent research, and clients are easily able to be classified using the taxonomy, then this approach may have implications for the process of counselling. To maximise the learning outcomes, counsellors could use strategies and techniques to enhance their clients' learning.

'We just make the pictures...?' How work is portrayed in children's feature length films

This article explores how adult paid work is portrayed in 'family' feature length films. The study extends previous critical media literature which has overwhelmingly focused on depictions of gender and violence, exploring the visual content of films that is relevant to adult employment. Forty-two G/PG films were analyzed for relevant themes. Consistent with the exploratory nature of the research, themes emerged inductively from the films' content. Results reveal six major themes: males are more visible in adult work roles than women; the division of labour remains gendered; work and home are not mutually exclusive domains; organizational authority and power is wielded in punitive ways; there are avenues to better employment prospects; and status/money is paramount. The findings of the study reflect a range of subject matters related to occupational characteristics and work-related communication and interactions which are typically viewed by children in contemporary society.

A study of entrepreneurship : Taiwanese digital content companies in China

China’s accession to the World Trade Organisation (WTO) has greatly enhanced global interest in investment in the Chinese media market, where demand for digital content is growing rapidly. The East Asian region is positioned as a growth area in many forms of digital content and digital service industries. China is attempting to catch up and take its place as a production centre to offset challenges from neighbouring countries. Meanwhile, Taiwan is seeking to use China both as an export market and as a production site for its digital content. This research investigates entry strategies of Taiwanese digital content firms into the Chinese market. By examining the strategies of a sample of Taiwan-based companies, this study also explores the evolution of their market strategies. However, the focus is on how distinctive business practices such as guanxi are important to Taiwanese business and to relations with Mainland China. This research examines how entrepreneurs manage the characteristics of digital content products and in turn how digital content entrepreneurs adapt to changing market circumstances. This project selected five Taiwan-based digital content companies that have business operations in China: Wang Film, Artkey, CnYES, Somode and iPartment. The study involved a field trip, undertaken between November 2006 and March 2007 to Shanghai and Taiwan to conduct interviews and to gather documentation and archival reports. Six senior managers and nine experts were interviewed. Data were analysed according to Miller’s firm-level entrepreneurship theory, foreign direct investment theory, Life Cycle Model and guanxi philosophy. Most studies of SMEs have focused on free market (capitalist) environments. In contrast, this thesis examines how Taiwanese digital content firms’ strategies apply in the Chinese market. I identified three main types of business strategy: cost-reduction, innovation and quality-enhancement; and four categories of functional strategies: product, marketing, resource acquisition and organizational restructuring. In this study, I introduce the concept of ‘entrepreneurial guanxi’, special relationships that imply mutual obligation, assurance and understanding to secure and exchange favors in entrepreneurial activities. While guanxi is a feature of many studies of business in Pan-Chinese society, it plays an important mediating role in digital content industries. In this thesis, I integrate the ‘Life Cycle Model’ with the dynamic concept of strategy. I outline the significant differences in the evolution of strategy between two types of digital content companies: off-line firms (Wang Film and Artkey) and web-based firms (CnYES, Somode and iPartment). Off-line digital content firms tended to adopt ‘resource acquisition strategies’ in their initial stages and ‘marketing strategies’ in second and subsequent stages. In contrast, web-based digital content companies mainly adopted product and marketing strategies in the early stages, and would adopt innovative approaches towards product and marketing strategies in the whole process of their business development. Some web-based digital content companies also adopted organizational restructuring strategies in the final stage. Finally, I propose the ‘Taxonomy Matrix of Entrepreneurial Strategies’ to emphasise the two dimensions of this matrix: innovation, and the firm’s resource acquisition for entrepreneurial strategy. This matrix is divided into four cells: Effective, Bounded, Conservative, and Impoverished.

Towards automatic tree crown detection and delineation in spectral feature space using PCNN and morphological reconstruction

The application of object-based approaches to the problem of extracting vegetation information from images requires accurate delineation of individual tree crowns. This paper presents an automated method for individual tree crown detection and delineation by applying a simplified PCNN model in spectral feature space followed by post-processing using morphological reconstruction. The algorithm was tested on high resolution multi-spectral aerial images and the results are compared with two existing image segmentation algorithms. The results demonstrate that our algorithm outperforms the other two solutions with the average accuracy of 81.8%.

Information enrichment for quality recommender systems

The explosive growth of the World-Wide-Web and the emergence of ecommerce are the major two factors that have led to the development of recommender systems (Resnick and Varian, 1997). The main task of recommender systems is to learn from users and recommend items (e.g. information, products or books) that match the users’ personal preferences. Recommender systems have been an active research area for more than a decade. Many different techniques and systems with distinct strengths have been developed to generate better quality recommendations. One of the main factors that affect recommenders’ recommendation quality is the amount of information resources that are available to the recommenders. The main feature of the recommender systems is their ability to make personalised recommendations for different individuals. However, for many ecommerce sites, it is difficult for them to obtain sufficient knowledge about their users. Hence, the recommendations they provided to their users are often poor and not personalised. This information insufficiency problem is commonly referred to as the cold-start problem. Most existing research on recommender systems focus on developing techniques to better utilise the available information resources to achieve better recommendation quality. However, while the amount of available data and information remains insufficient, these techniques can only provide limited improvements to the overall recommendation quality. In this thesis, a novel and intuitive approach towards improving recommendation quality and alleviating the cold-start problem is attempted. This approach is enriching the information resources. It can be easily observed that when there is sufficient information and knowledge base to support recommendation making, even the simplest recommender systems can outperform the sophisticated ones with limited information resources. Two possible strategies are suggested in this thesis to achieve the proposed information enrichment for recommenders: • The first strategy suggests that information resources can be enriched by considering other information or data facets. Specifically, a taxonomy-based recommender, Hybrid Taxonomy Recommender (HTR), is presented in this thesis. HTR exploits the relationship between users’ taxonomic preferences and item preferences from the combination of the widely available product taxonomic information and the existing user rating data, and it then utilises this taxonomic preference to item preference relation to generate high quality recommendations. • The second strategy suggests that information resources can be enriched simply by obtaining information resources from other parties. In this thesis, a distributed recommender framework, Ecommerce-oriented Distributed Recommender System (EDRS), is proposed. The proposed EDRS allows multiple recommenders from different parties (i.e. organisations or ecommerce sites) to share recommendations and information resources with each other in order to improve their recommendation quality. Based on the results obtained from the experiments conducted in this thesis, the proposed systems and techniques have achieved great improvement in both making quality recommendations and alleviating the cold-start problem.

Robust video stabilisation algorithm using feature point selection and delta optical flow

In this study, the authors propose a novel video stabilisation algorithm for mobile platforms with moving objects in the scene. The quality of videos obtained from mobile platforms, such as unmanned airborne vehicles, suffers from jitter caused by several factors. In order to remove this undesired jitter, the accurate estimation of global motion is essential. However it is difficult to estimate global motions accurately from mobile platforms due to increased estimation errors and noises. Additionally, large moving objects in the video scenes contribute to the estimation errors. Currently, only very few motion estimation algorithms have been developed for video scenes collected from mobile platforms, and this paper shows that these algorithms fail when there are large moving objects in the scene. In this study, a theoretical proof is provided which demonstrates that the use of delta optical flow can improve the robustness of video stabilisation in the presence of large moving objects in the scene. The authors also propose to use sorted arrays of local motions and the selection of feature points to separate outliers from inliers. The proposed algorithm is tested over six video sequences, collected from one fixed platform, four mobile platforms and one synthetic video, of which three contain large moving objects. Experiments show our proposed algorithm performs well to all these video sequences.