Spectral–texture feature extraction using statistical moments with application to object-based vegetation species classification

The use of appropriate features to characterise an output class or object is critical for all classification problems. In order to find optimal feature descriptors for vegetation species classification in a power line corridor monitoring application, this article evaluates the capability of several spectral and texture features. A new idea of spectral–texture feature descriptor is proposed by incorporating spectral vegetation indices in statistical moment features. The proposed method is evaluated against several classic texture feature descriptors. Object-based classification method is used and a support vector machine is employed as the benchmark classifier. Individual tree crowns are first detected and segmented from aerial images and different feature vectors are extracted to represent each tree crown. The experimental results showed that the proposed spectral moment features outperform or can at least compare with the state-of-the-art texture descriptors in terms of classification accuracy. A comprehensive quantitative evaluation using receiver operating characteristic space analysis further demonstrates the strength of the proposed feature descriptors.

Heroes & harmony

The first practice-led research project 'Heroes of the Club', through collaboration with business and community, involved portraying the stories on canvas of heroes of the Australian Surf Life Saving movement. The second project 'Crossing the intersection… art and life' researched a post-modernist approach to a fusion of the aesthetics of Abstract Expressionism and Pop Art. This study resulted in a body of work 'Long Playing' which sought to harmoniously reconcile two apparent polarities of style and context between high and low art characteristics, through personal narrative and with reference to artistic tradition.

Unusual Event Detection in Crowded Scenes Using Bag of LBPs in spatio-temporal patches

Modelling events in densely crowded environments remains challenging, due to the diversity of events and the noise in the scene. We propose a novel approach for anomalous event detection in crowded scenes using dynamic textures described by the Local Binary Patterns from Three Orthogonal Planes (LBP-TOP) descriptor. The scene is divided into spatio-temporal patches where LBP-TOP based dynamic textures are extracted. We apply hierarchical Bayesian models to detect the patches containing unusual events. Our method is an unsupervised approach, and it does not rely on object tracking or background subtraction. We show that our approach outperforms existing state of the art algorithms for anomalous event detection in UCSD dataset.

Challenges facing sustainable housing in Saudi Arabia : a current study showing the level of public awareness

Sustainable housing implementation requires strong support from the public, government and the housing industry. Lack of public awareness and understanding of the language and the meaning of sustainable housing may cause lack of public support. Salama stated that "sustainability or sustainable design is simply a rephrasing of some of the forgotten values of traditional architecture and urbanism"(Salama 2007). This exploratory paper examines public awareness of sustainable housing in Saudi Arabia. In developing countries, like Saudi Arabia, which have been experiencing a rapid rate of urbanisation, sustainable concept intervention is essential due to the scarcity of resources (Reffat 2004a). Sustainable building methods include the full use of the site design, passive solar design, natural light and ventilation. This paper reports on an exploratory survey on understanding the potential of the implementation of sustainable housing in Saudi Arabia. The main problem is that more than half of respondents were not aware of sustainable housing. Thus, one of the recommendations from the survey is to educate the public by using local media to inform people of the benefits of sustainable implementation to both new and existing housing stock.

Negative determinant of Hessian features

Local image feature extractors that select local maxima of the determinant of Hessian function have been shown to perform well and are widely used. This paper introduces the negative local minima of the determinant of Hessian function for local feature extraction. The properties and scale-space behaviour of these features are examined and found to be desirable for feature extraction. It is shown how this new feature type can be implemented along with the existing local maxima approach at negligible extra processing cost. Applications to affine covariant feature extraction and sub-pixel precise corner extraction are demonstrated. Experimental results indicate that the new corner detector is more robust to image blur and noise than existing methods. It is also accurate for a broader range of corner geometries. An affine covariant feature extractor is implemented by combining the minima of the determinant of Hessian with existing scale and shape adaptation methods. This extractor can be implemented along side the existing Hessian maxima extractor simply by finding both minima and maxima during the initial extraction stage. The minima features increase the number of correspondences by two to four fold. The additional minima features are very distinct from the maxima features in descriptor space and do not make the matching process more ambiguous.

Class contestations and Australia’s resource boom : the emergence of the ‘cashed-up bogan’

This article examines the figure of the ‘Cashed-up Bogan’ or ‘Cub’ in Australian media from 2006 to 2009. It explains that ‘Bogan’, like that of ‘Chav’ in Britain, is a widely engaged negative descriptor for the white working-class poor. In contrast, ‘Cubs’ have economic capital. This capital, and the Cub’s emergence, is linked to Australia’s resource boom of recent decades when the need for skilled labour allowed for a highly demarcated segment of the working class to earn relatively high incomes in the mining sector and to participate in consumption. We argue that access to economic capital has provided the Cub with mobility to enter the everyday spaces of the middle class, but this has caused disruption and anxiety to middle-class hegemony. As a result, the middle class has redrawn and reinforced class-infused symbolic and cultural boundaries, whereby, despite their wealth, pernicious media representations mark Cubs as ‘other’ to the middle-class deservingness, taste and morality.

In the pursuit of effective affective computing : the relationship between features and registration

For facial expression recognition systems to be applicable in the real world, they need to be able to detect and track a previously unseen person's face and its facial movements accurately in realistic environments. A highly plausible solution involves performing a "dense" form of alignment, where 60-70 fiducial facial points are tracked with high accuracy. The problem is that, in practice, this type of dense alignment had so far been impossible to achieve in a generic sense, mainly due to poor reliability and robustness. Instead, many expression detection methods have opted for a "coarse" form of face alignment, followed by an application of a biologically inspired appearance descriptor such as the histogram of oriented gradients or Gabor magnitudes. Encouragingly, recent advances to a number of dense alignment algorithms have demonstrated both high reliability and accuracy for unseen subjects [e.g., constrained local models (CLMs)]. This begs the question: Aside from countering against illumination variation, what do these appearance descriptors do that standard pixel representations do not? In this paper, we show that, when close to perfect alignment is obtained, there is no real benefit in employing these different appearance-based representations (under consistent illumination conditions). In fact, when misalignment does occur, we show that these appearance descriptors do work well by encoding robustness to alignment error. For this work, we compared two popular methods for dense alignment-subject-dependent active appearance models versus subject-independent CLMs-on the task of action-unit detection. These comparisons were conducted through a battery of experiments across various publicly available data sets (i.e., CK+, Pain, M3, and GEMEP-FERA). We also report our performance in the recent 2011 Facial Expression Recognition and Analysis Challenge for the subject-independent task.

Robust facial feature extraction and matching

Automated feature extraction and correspondence determination is an extremely important problem in the face recognition community as it often forms the foundation of the normalisation and database construction phases of many recognition and verification systems. This paper presents a completely automatic feature extraction system based upon a modified volume descriptor. These features form a stable descriptor for faces and are utilised in a reversible jump Markov chain Monte Carlo correspondence algorithm to automatically determine correspondences which exist between faces. The developed system is invariant to changes in pose and occlusion and results indicate that it is also robust to minor face deformations which may be present with variations in expression.

Towards spontaneous facial expression recognition in real-world video

Facial expression is an important channel of human social communication. Facial expression recognition (FER) aims to perceive and understand emotional states of humans based on information in the face. Building robust and high performance FER systems that can work in real-world video is still a challenging task, due to the various unpredictable facial variations and complicated exterior environmental conditions, as well as the difficulty of choosing a suitable type of feature descriptor for extracting discriminative facial information. Facial variations caused by factors such as pose, age, gender, race and occlusion, can exert profound influence on the robustness, while a suitable feature descriptor largely determines the performance. Most present attention on FER has been paid to addressing variations in pose and illumination. No approach has been reported on handling face localization errors and relatively few on overcoming facial occlusions, although the significant impact of these two variations on the performance has been proved and highlighted in many previous studies. Many texture and geometric features have been previously proposed for FER. However, few comparison studies have been conducted to explore the performance differences between different features and examine the performance improvement arisen from fusion of texture and geometry, especially on data with spontaneous emotions. The majority of existing approaches are evaluated on databases with posed or induced facial expressions collected in laboratory environments, whereas little attention has been paid on recognizing naturalistic facial expressions on real-world data. This thesis investigates techniques for building robust and high performance FER systems based on a number of established feature sets. It comprises of contributions towards three main objectives: (1) Robustness to face localization errors and facial occlusions. An approach is proposed to handle face localization errors and facial occlusions using Gabor based templates. Template extraction algorithms are designed to collect a pool of local template features and template matching is then performed to covert these templates into distances, which are robust to localization errors and occlusions. (2) Improvement of performance through feature comparison, selection and fusion. A comparative framework is presented to compare the performance between different features and different feature selection algorithms, and examine the performance improvement arising from fusion of texture and geometry. The framework is evaluated for both discrete and dimensional expression recognition on spontaneous data. (3) Evaluation of performance in the context of real-world applications. A system is selected and applied into discriminating posed versus spontaneous expressions and recognizing naturalistic facial expressions. A database is collected from real-world recordings and is used to explore feature differences between standard database images and real-world images, as well as between real-world images and real-world video frames. The performance evaluations are based on the JAFFE, CK, Feedtum, NVIE, Semaine and self-collected QUT databases. The results demonstrate high robustness of the proposed approach to the simulated localization errors and occlusions. Texture and geometry have different contributions to the performance of discrete and dimensional expression recognition, as well as posed versus spontaneous emotion discrimination. These investigations provide useful insights into enhancing robustness and achieving high performance of FER systems, and putting them into real-world applications.

Analyzing the literature for the link between the conservative Islamic culture of Saudi Arabia and the design of sustainable housing

Saudi Arabia experiences housing shortage for mid and low-income families, which is caused by rapid population growth. This condition is worsened by the fact that the current housing supply has problems in meeting both sustainable requirements and cultural needs of those families. This paper aims to investigate the link between the unique conservative Saudi culture and the design of sustainable housing, while keeping the housing cost affordable for mid and low-income families. The paper is based on a review of literatures on the issues of the Islamic culture and how can they be integrated into the design process of a Saudi house. Findings from literature reveiw suggest several design requirements for accommodating the conservative Saudi Culture in low cost sustainable houses. Such requirements include the implementation of proper usage of windows, and house orientation with a courtyard inside rather than facing the main street will provide natural ventilation while maintaining privacy. The main contribution to the body of knowledge is that this is a new approach to sustainable housing in Saudi Arabia considering not only energy use and architectural design issues but also socio-cultural issues as an essential part of sustainability.

Spinning a sustainable yarn: assessing the claims of Australian companies’ eco-initiatives

In Australia, few fashion brands have intervened in the design of their products or the systems around their product to tackle environmental pollution and waste. Instead, support of charities (whether social or environmental) has become conflated with sustainability in the eyes of the public.However, three established Australian brands recently put forward initiatives which explicitly tackle the pre-consumer or post-consumer waste associated with their products. In 2011, Billabong, one of the largest surfwear companies in the world, developed a collection of board shorts made from recycled bottles that are also recyclable at end of life. The initiative has been promoted in partnership with Bob Marley’s son Rohan Marley, and the graphics of the board shorts reference the Rastafarian colours and make use of Marley’s song lyrics. In this way, the company has tapped into an aspect of surf culture linked to environmental activism, in which the natural world is venerated. Two mid-market initiatives, by Metalicus and Country Road, each have a social outcome that arguably aligns to the values of their middle-class consumer base. Metalicus is spear-heading a campaign for Australian garment manufacturers to donate their pre consumer waste – fabric off-cuts – to charity Open Family Australia to be manufactured into quilts for the homeless. Country Road has partnered with the Australian Red Cross to implement a recycling scheme in which consumers donate their old Country Road garments in exchange for a Country Road gift voucher. Both strategies, while tackling waste, tell an altruistic story in which the disadvantaged can benefit from the consumption habits of the middle-class. To varying degrees, the initiative chosen by each company feeds into the stories they tell about themselves and about the consumers who purchase their clothing. However, how can we assess the impact of these schemes on waste management in real terms, or indeed the worth of each scheme in the wider context of the fashion system? This paper will assess the claims made by the companies and analyse their efficacy, suggesting that a more nuanced assessment of green claims is required, in which ‘green’ comes in many tonal variations.

The prevalence and perception of pain amongst hospital in-patients

The aim of this paper is to describe the prevalence and perceptions of pain and pain management amongst hospital in-patients. A cross-sectional descriptive survey of 205 patients was conducted. Presence and severity of pain was assessed using verbal descriptor and visual analogue scales, and perceptions of pain were assessed using multi-item scales. Although the severity of pain reported was consistent across age groups and clinical areas, women in the study sample were significantly more likely to report high levels of pain than men. Differences in how men and women communicate their pain were observed, with women indicating that they were less willing to ask for help with their pain. Results suggest that pain continues to be an important problem for a large number of men and women in hospital, and that the experience of pain impacts negatively upon their well-being. Gender differences in the experience of and response to pain remain important considerations for clinical nurses who have major responsibilities for the management of pain in hospitalized patients.

Improved foreground detection via block-based classifier cascade with probabilistic decision integration

Background subtraction is a fundamental low-level processing task in numerous computer vision applications. The vast majority of algorithms process images on a pixel-by-pixel basis, where an independent decision is made for each pixel. A general limitation of such processing is that rich contextual information is not taken into account. We propose a block-based method capable of dealing with noise, illumination variations, and dynamic backgrounds, while still obtaining smooth contours of foreground objects. Specifically, image sequences are analyzed on an overlapping block-by-block basis. A low-dimensional texture descriptor obtained from each block is passed through an adaptive classifier cascade, where each stage handles a distinct problem. A probabilistic foreground mask generation approach then exploits block overlaps to integrate interim block-level decisions into final pixel-level foreground segmentation. Unlike many pixel-based methods, ad-hoc postprocessing of foreground masks is not required. Experiments on the difficult Wallflower and I2R datasets show that the proposed approach obtains on average better results (both qualitatively and quantitatively) than several prominent methods. We furthermore propose the use of tracking performance as an unbiased approach for assessing the practical usefulness of foreground segmentation methods, and show that the proposed approach leads to considerable improvements in tracking accuracy on the CAVIAR dataset.

Inside the pleasure dome : cultural tourism on Australia’s Gold Coast

The City of the Gold Coast in Queensland, Australia, will host the Commonwealth Games in 2018. In advance of the Games, the City is beginning to reposition the traditional marketing programs that were based around the four S’s- ‘sun, sand, surf and sex.’ There is a new emphasis on urban sophistication, sport, science, education and the environment. At the same time, local communities are asking for renewed attention to residential issues, particularly relating to recognising the importance of culture to the region. In this paper I explore the development of integrated computer technologies (ICTs) as a way of linking tourism, culture and place in the experience economy of the Gold Coast. The discussion is framed by theories of the post-tourist, contemporary cultural tourism and the role of mobile technologies, and the figure of the ‘referential tourist.’ An examination of stakeholder responses to changing business and social frameworks on the Gold Coast shows how discussions about a range of issues coalesce around cultural tourism. Local communities have the opportunity to engage with the new tourist as they move quickly between leisure and cultural experiences, at once connected to tourist expectations but increasingly self-directed. The Surfers Paradise Nights campaign, which is based around social media, is a case in point. This campaign aims to interest visitors in becoming a part of a familiar third place, an online space, but one that will sustain an emotive connection to the physical location and events. The paper also draws on research carried out in Brisbane, Queensland, in relation to building connections between place and culture on designated, self-directed journeys via iPhone technology. Participant responses indicate the importance of narrative to developing cultural frameworks.

On robust face recognition via sparse encoding : the good, the bad, and the ugly

In the field of face recognition, Sparse Representation (SR) has received considerable attention during the past few years. Most of the relevant literature focuses on holistic descriptors in closed-set identification applications. The underlying assumption in SR-based methods is that each class in the gallery has sufficient samples and the query lies on the subspace spanned by the gallery of the same class. Unfortunately, such assumption is easily violated in the more challenging face verification scenario, where an algorithm is required to determine if two faces (where one or both have not been seen before) belong to the same person. In this paper, we first discuss why previous attempts with SR might not be applicable to verification problems. We then propose an alternative approach to face verification via SR. Specifically, we propose to use explicit SR encoding on local image patches rather than the entire face. The obtained sparse signals are pooled via averaging to form multiple region descriptors, which are then concatenated to form an overall face descriptor. Due to the deliberate loss spatial relations within each region (caused by averaging), the resulting descriptor is robust to misalignment & various image deformations. Within the proposed framework, we evaluate several SR encoding techniques: l1-minimisation, Sparse Autoencoder Neural Network (SANN), and an implicit probabilistic technique based on Gaussian Mixture Models. Thorough experiments on AR, FERET, exYaleB, BANCA and ChokePoint datasets show that the proposed local SR approach obtains considerably better and more robust performance than several previous state-of-the-art holistic SR methods, in both verification and closed-set identification problems. The experiments also show that l1-minimisation based encoding has a considerably higher computational than the other techniques, but leads to higher recognition rates.