Image reduction with local reduction operators

In this work we propose an image reduction algorithm based on weak local reduction operators. We use several averaging functions to build these operators and we analyze their properties. We present experimental results where we apply the algorithm and weak local reduction operators in procedures of reduction, and later, reconstruction of images. We analyze these results over natural images and noisy images.

Some averaging functions in image reduction

In this work we propose a definition of weak local reduction operators and an algorithm that uses such operators to reduce images. We show how to construct local reduction operators by means of different aggregation functions. We also present several experimental results obtained using these aggregation functions-based reduction operators.

Image reduction using fuzzy quantifiers

In this work we propose an image reduction algorith based on local reduction operators. We analyze the construction of weak local reduction operators by means of aggregation functions and we analyze the effect of several aggregation functions in image reduction with original and noisy images.

Image reduction operators based on non-monotonic averaging functions

Image reduction is a crucial task in image processing, underpinning many practical applications. This work proposes novel image reduction operators based on non-monotonic averaging aggregation functions. The technique of penalty function minimisation is used to derive a novel mode-like estimator capable of identifying the most appropriate pixel value for representing a subset of the original image. Performance of this aggregation function and several traditional robust estimators of location are objectively assessed by applying image reduction within a facial recognition task. The FERET evaluation protocol is applied to confirm that these non-monotonic functions are able to sustain task performance compared to recognition using nonreduced images, as well as significantly improve performance on query images corrupted by noise. These results extend the state of the art in image reduction based on aggregation functions and provide a basis for efficiency and accuracy improvements in practical computer vision applications.

Image reduction operators as aggregation functions: Fuzzy transform and undersampling

After studying several reduction algorithms that can be found in the literature, we notice that there is not an axiomatic definition of this concept. In this work we propose the definition of weak reduction operators and we propose the properties of the original image that reduced images must keep. From this definition, we study whether two methods of image reduction, undersampling and fuzzy transform, satisfy the conditions of weak reduction operators.

Construction of aggregation operators for automated decision making via optimal interpolation and global optimization

This paper examines methods of point wise construction of aggregation operators via optimal interpolation. It is shown that several types of application-specific requirements lead to interpolatory type constraints on the aggregation function. These constraints are translated into global optimization problems, which are the focus of this paper. We present several methods of reduction of the number of variables, and formulate suitable numerical algorithms based on Lipschitz optimization.

The global obesity pandemic : shaped by global drivers and local environments

The simultaneous increases in obesity in almost all countries seem to be driven mainly by changes in the global food system, which is producing more processed, affordable, and effectively marketed food than ever before. This passive overconsumption of energy leading to obesity is a predictable outcome of market economies predicated on consumption-based growth. The global food system drivers interact with local environmental factors to create a wide variation in obesity prevalence between populations. Within populations, the interactions between environmental and individual factors, including genetic makeup, explain variability in body size between individuals. However, even with this individual variation, the epidemic has predictable patterns in subpopulations. In low-income countries, obesity mostly affects middle-aged adults (especially women) from wealthy, urban environments; whereas in high-income countries it affects both sexes and all ages, but is disproportionately greater in disadvantaged groups. Unlike other major causes of preventable death and disability, such as tobacco use, injuries, and infectious diseases, there are no exemplar populations in which the obesity epidemic has been reversed by public health measures. This absence increases the urgency for evidence-creating policy action, with a priority on reduction of the supply-side drivers.

Image reduction using means on discrete product lattices

We investigate the problem of averaging values on lattices, and in particular on discrete product lattices. This problem arises in image processing when several color values given in RGB, HSL, or another coding scheme, need to be combined. We show how the arithmetic mean and the median can be constructed by minimizing appropriate penalties, and we discuss which of them coincide with the Cartesian product of the standard mean and median. We apply these functions in image processing. We present three algorithms for color image reduction based on minimizing penalty functions on discrete product lattices.

One image reduction algorithm for RGB color images

We investigate the problem of combining or aggregating several color values given in coding scheme RGB. For this reason, we study the problem of averaging values on lattices, and in particular on discrete product lattices. We study the arithemtic mean and the median on product lattices. We apply these aggregation functions in image reduction and we present a new algorithm based on the minimization of penalty functions on discrete product lattices.

Geographical Information Systems (GIS) and their role in sustainable planning : a case study from a Local Government Area (LGA) in Australia

A case study is used to demonstrate the application of Geographical Information Systems (GIS) to inform sustainable development. The suitability of the landscape to support tourism accommodation in a Local Government Area (LGA) is modelled by integrating existing datasets, including conservation areas, residential zones, major roads and known locations of tourism operators into a logistic regression framework. By using a data-driven approach an indication of the relative importance of each explanatory variable can be accounted for, therefore informing planners of the importance of different assets. In a region where tourism is reliant upon natural features, this use of information systems in conjunction with quantitative statistical modelling can value-add to existing datasets. The provision of this kind of knowledge is important as it would otherwise not factor into the decision-making process had the datasets been considered independently of each other – a concept that applies to both the public and private sectors.

Local structure and photocatalytic property of sol-gel synthesized ZnO doped with transition metal oxides

ZnO nanoparticles doped with up to 5 at% of Co and Mn were prepared using a co-precipitation method. The location of dopant ions and the effect of doping on the photocatalytic activity were investigated. The crystal structure of nanoparticles and local atomic arrangements around dopant ions were analyzed by X-ray absorption spectroscopy. The results showed that the Co ions substituted the Zn ions in the ZnO wurtzite phase structure and induced lattice shrinkage, while Mn ions were not completely incorporated in the crystal lattice. The photocatalytic activity under simulated sunlight was characterized by the decomposition of Rhodamine B dye molecules. It was revealed that Co-doping strongly reduced the photocatalytic activity but Mn-doping showed a weaker effect on the reduction of the photoactivity.

Supermarket Healthy Eating for Life (SHELf): protocol of a randomised controlled trial promoting healthy food and beverage consumption through price reduction and skill-building strategies

Background: In the context of rising food prices, there is a need for evidence on the most effective approaches for promoting healthy eating. Individually-targeted behavioural interventions for increasing food-related skills show promise, but are unlikely to be effective in the absence of structural supports. Fiscal policies have been advocated as a means of promoting healthy eating and reducing obesity and nutrition-related disease, but there is little empirical evidence of their effectiveness. This paper describes the Supermarket Healthy Eating for LiFe (SHELf) study, a randomised controlled trial to investigate effectiveness and cost-effectiveness of a tailored skill-building intervention and a price reduction intervention, separately and in combination, against a control condition for promoting purchase and consumption of healthy foods and beverages in women from high and low socioeconomic groups.
Methods/design: SHELf comprises a randomised controlled trial design, with participants randomised to receive either (1) a skill-building intervention; (2) price reductions on fruits, vegetables and low-joule soft drink beverages and water; (3) a combination of skill-building and price reductions; or (4) a control condition. Five hundred women from high and low socioeconomic areas will be recruited through a store loyalty card program and local media. Randomisation will occur on receipt of informed consent and baseline questionnaire. An economic evaluation from a societal perspective using a cost-consequences approach will compare the costs and outcomes between intervention and control groups.
Discussion: This study will build on a pivotal partnership with a major national supermarket chain and the Heart Foundation to investigate the effectiveness of intervention strategies aimed at increasing women’s purchasing and consumption of fruits and vegetables and decreased purchasing and consumption of sugar-sweetened beverages. It will be among the first internationally to examine the effects of two promising approaches - skill-building and price reductions - on diet amongst women.

Role of catecholaminergic inputs to the medial prefrontal cortex in local and subcortical expression of Fos after psychological stress

A wide variety of stressors elicit Fos expression in the medial prefrontal cortex (mPFC). No direct attempts, however, have been made to determine the role of the inputs that drive this response. We examined the effects of lesions of mPFC catecholamine terminals on local expression of Fos after exposure to air puff, a stimulus that in the rat acts as an acute psychological stressor. We also examined the effects of these lesions on Fos expression in a variety of subcortical neuronal populations implicated in the control of adrenocortical activation, one classic hallmark of the stress response. Lesions of the mPFC that were restricted to dopaminergic terminals significantly reduced numbers of Fos-immunoreactive (Fos-IR) cells seen in the mPFC after air puff, but had no significant effect on stress-induced Fos expression in the subcortical structures examined. Lesions of the mPFC that affected both dopaminergic and noradrenergic terminals also reduced numbers of Fos-IR cells observed in the mPFC after air puff. Additionally, these lesions resulted in a significant reduction in stress-induced Fos-IR in the ventral bed nucleus of the stria terminalis. These results demonstrate a role for catecholaminergic inputs to the mPFC, in the generation of both local and subcortical responses to psychological stress.