Gradient edge map features for frontal face recognition under extreme illumination changes

Our aim in this paper is to robustly match frontal faces in the presence of extreme illumination changes, using only a single training image per person and a single probe image. In the illumination conditions we consider, which include those with the dominant light source placed behind and to the side of the user, directly above and pointing downwards or indeed below and pointing upwards, this is a most challenging problem. The presence of sharp cast shadows, large poorly illuminated regions of the face, quantum and quantization noise and other nuisance effects, makes it difficult to extract a sufficiently discriminative yet robust representation. We introduce a representation which is based on image gradient directions near robust edges which correspond to characteristic facial features. Robust edges are extracted using a cascade of processing steps, each of which seeks to harness further discriminative information or normalize for a particular source of extra-personal appearance variability. The proposed representation was evaluated on the extremely difficult YaleB data set. Unlike most of the previous work we include all available illuminations, perform training using a single image per person and match these also to a single probe image. In this challenging evaluation setup, the proposed gradient edge map achieved 0.8% error rate, demonstrating a nearly perfect receiver-operator characteristic curve behaviour. This is by far the best performance achieved in this setup reported in the literature, the best performing methods previously proposed attaining error rates of approximately 6–7%.

Growing Self Organizing Map with an Imposed Binary Search Tree for Discovering Temporal Input Patterns

In this paper the Binary Search Tree Imposed Growing Self Organizing Map (BSTGSOM) is presented as an extended version of the Growing Self Organizing Map (GSOM), which has proven advantages in knowledge discovery applications. A Binary Search Tree imposed on the GSOM is mainly used to investigate the dynamic perspectives of the GSOM based on the inputs and these generated temporal patterns are stored to further analyze the behavior of the GSOM based on the input sequence. Also, the performance advantages are discussed and compared with that of the original GSOM.

A social-cultural reading: the Melbourne Savage Club through its collections

This thesis looks at how the collection(s) of a private club located in the Melbourne Central Business District have been shaped by the bohemian attitude of its founding members and examines the contributing factors to the collection which make it distinctive and significant to Australian cultural heritage.

The road map to building new dreams : raising a child with developmental delay or disability

Research on early childhood education emphasises the importance of quality in early childhood intervention. This study examines the quality of Early Childhood Intervention Services based on parents’ experiences raising a child with developmental delay or disability. The study builds on the philosophy of Family-Centred Practice and professionals’ experiences with family-centred interventions. A qualitative case study approach was adopted to gain insight about families who are raising a child with additional needs. Nine in-depth parent-interviews and three focus groups with professionals were conducted in the first two terms of 2010. The case explicates the experiences of parents and professionals who were associated with Specialist Children’s Services in a metropolitan region of Victoria. The research concentrated on the first point of entry to early intervention, the referrals process and the waiting list. It also addressed parents' experiences, priorities and expectations. As a small-scale study, it examined parents’ and children’s needs as well as children’s access to therapy in early intervention. It also investigated community support and parent-professional relationships in the context of early childhood intervention services. The study found that family-centred intervention is beneficial to both parents and children with developmental delay or disability. However, to implement an effective family-centred approach, practitioner support in the form of professional development, supervision and peer mentorship is required to develop professionals’ reflexivity and self-efficacy in family-centred interventions. The study also identified strategies to promote effective practice, gaps in universal and specialised services, and implications for policy.

Growing self-organizing map for online continuous clustering

The internet age has fuelled an enormous explosion in the amount of information generated by humanity. Much of this information is transient in nature, created to be immediately consumed and built upon (or discarded). The field of data mining is surprisingly scant with algorithms that are geared towards the unsupervised knowledge extraction of such dynamic data streams. This chapter describes a new neural network algorithm inspired by self-organising maps. The new algorithm is a hybrid algorithm from the growing self-organising map (GSOM) and the cellular probabilistic self-organising map (CPSOM). The result is an algorithm which generates a dynamically growing feature map for the purpose of clustering dynamic data streams and tracking clusters as they evolve in the data stream.

Cluster identification and separation in the growing self-organizing map: application in protein sequence classification

Growing self-organizing map (GSOM) has been introduced as an improvement to the self-organizing map (SOM) algorithm in clustering and knowledge discovery. Unlike the traditional SOM, GSOM has a dynamic structure which allows nodes to grow reflecting the knowledge discovered from the input data as learning progresses. The spread factor parameter (SF) in GSOM can be utilized to control the spread of the map, thus giving an analyst a flexibility to examine the clusters at different granularities. Although GSOM has been applied in various areas and has been proven effective in knowledge discovery tasks, no comprehensive study has been done on the effect of the spread factor parameter value to the cluster formation and separation. Therefore, the aim of this paper is to investigate the effect of the spread factor value towards cluster separation in the GSOM. We used simple k-means algorithm as a method to identify clusters in the GSOM. By using Davies–Bouldin index, clusters formed by different values of spread factor are obtained and the resulting clusters are analyzed. In this work, we show that clusters can be more separated when the spread factor value is increased. Hierarchical clusters can then be constructed by mapping the GSOM clusters at different spread factor values.

Combining ganglion cell topology and data of patients with glaucoma to determine a structure-function map

PURPOSE

A map of community-based obesity prevention initiatives in Australia following obesity funding 2009-2013.

Obesity is the single biggest public health threat to developed and developing economies. In concert with healthy public policy, multi-strategy, multi-level community-based initiatives appear promising in preventing obesity, with several countries trialling this approach. In Australia, multiple levels of government have funded and facilitated a range of community-based obesity prevention initiatives (CBI), heterogeneous in their funding, timing, target audience and structure. This paper aims to present a central repository of CBI operating in Australia during 2013, to facilitate knowledge exchange and shared opportunities for learning, and to guide professional development towards best practice for CBI practitioners.