Brain structures identification based on feature descriptor

Traumatic Brain Injury -TBI- -1- is defined as an acute event that causes certain damage to areas of the brain. TBI may result in a significant impairment of an individuals physical, cognitive and psychosocial functioning. The main consequence of TBI is a dramatic change in the individuals daily life involving a profound disruption of the family, a loss of future income capacity and an increase of lifetime cost. One of the main challenges of TBI Neuroimaging is to develop robust automated image analysis methods to detect signatures of TBI, such as: hyper-intensity areas, changes in image contrast and in brain shape. The final goal of this research is to develop a method to identify the altered brain structures by automatically detecting landmarks on the image where signal changes and to provide comprehensive information to the clinician about them. These landmarks identify injured structures by co-registering the patient?s image with an atlas where landmarks have been previously detected. The research work has been initiated by identifying brain structures on healthy subjects to validate the proposed method. Later, this method will be used to identify modified structures on TBI imaging studies.

HOG-Like gradient-based descriptor for visual vehicle detection

One of the main challenges for intelligent vehicles is the capability of detecting other vehicles in their environment, which constitute the main source of accidents. Specifically, many methods have been proposed in the literature for video-based vehicle detection. Most of them perform supervised classification using some appearance-related feature, in particular, symmetry has been extensively utilized. However, an in-depth analysis of the classification power of this feature is missing. As a first contribution of this paper, a thorough study of the classification performance of symmetry is presented within a Bayesian decision framework. This study reveals that the performance of symmetry-based classification is very limited. Therefore, as a second contribution, a new gradient-based descriptor is proposed for vehicle detection. This descriptor exploits the known rectangular structure of vehicle rears within a Histogram of Gradients (HOG)-based framework. Experiments show that the proposed descriptor outperforms largely symmetry as a feature for vehicle verification, achieving classification rates over 90%.

What is the best size descriptor to use for pharmacokinetic studies in the obese?

The prevalence of obesity in the western world is dramatically rising, with many of these individuals requiring therapeutic intervention for a variety of disease states. Despite the growing prevalence of obesity there is a paucity of information describing how doses should be adjusted, or indeed whether they need to be adjusted, in the clinical setting. This review is aimed at identifying which descriptors of body size provide the most information about the relationship between dose and concentration in the obese. The size descriptors, weight, lean body weight, ideal body weight, body surface area, body mass index, fat-free mass, percent ideal body weight, adjusted body weight and predicted normal body weight were considered as potential size descriptors. We conducted an extensive review of the literature to identify studies that have assessed the quantitative relationship between the parameters clearance (CL) and volume of distribution (V) and these descriptors of body size. Surprisingly few studies have addressed the relationship between obesity and CL or V in a quantitative manner. Despite the lack of studies there were consistent findings: (i) most studies found total body weight to be the best descriptor of V. A further analysis of the studies that have addressed V found that total body weight or another descriptor that incorporated fat mass was the preferred descriptor for drugs that have high lipophilicity; (ii) in contrast, CL was best described by lean body mass and no apparent relationship between lipophilicity or clearance mechanism and preference for body size descriptor was found. In conclusion, no single descriptor described the influence of body size on both CL and V equally well. For drugs that are dosed chronically, and therefore CL is of primary concern, dosing for obese patients should not be based on their total weight. If a weight-based dose individualization is required then we would suggest that chronic drug dosing in the obese subject should be based on lean body weight, at least until a more robust size descriptor becomes available.

A standard weight descriptor for dose adjustment in the obese patient

Objective: To develop a standard weight descriptor that can be used for estimation of patient size for obese patients. Patients and methods: Data were available from 3849 patients: 2839 from oncology patients (index data set) and 1010 from general medical patients (validation data set). The patients had a wide range of age (16-100 years), weight (25-165kg) and body mass index (BMI) [12-52 kg/m(2)] in both data sets. From the normal-weight patients in the oncology data set, an equation for male and female patients was developed to predict their normal weight as the sum of the lean body mass and normal fat body mass. The equations were evaluated by predicting the weight of patients in the general medical data set who had a normal BMI (30 kg/m(2)).

Classification of Paintings by Artist, Movement, and Indoor Setting Using MPEG-7 Descriptor Features

ACM Computing Classification System (1998): I.4.9, I.4.10.

Understanding how to enhance business creativity

This PhD study examines some of what happens in an individual’s mind regarding creativity during problem solving within an organisational context. It presents innovations related to creative motivation, cognitive style and framing effects that can be applied by managers to enhance individual employee creativity within the organisation and thereby assist organisations to become more innovative. The project delivers an understanding of how to leverage natural changes in creative motivation levels during problem solving. This pattern of response is called Creative Resolve Response (CRR). The project also presents evidence of how framing effects can be used to influence decisions involving creative options in order to enhance the potential for managers get employees to select creative options more often for implementation. The study’s objectives are to understand: • How creative motivation changes during problem solving • How cognitive style moderates these creative motivation changes • How framing effects apply to decisions involving creative options to solve problems • How cognitive style moderate these framing effects The thesis presents the findings from three controlled experiments based around self reports during contrived problem solving and decision making situations. The first experiment suggests that creative motivation varies in a predictable and systematic way during problem solving as a function of the problem solver’s perception of progress. The second experiment suggests that there are specific framing effects related to decisions involving creativity. It seems that simply describing an alternative as innovative may activate perceptual biases that overcome risk based framing effects. The third experiment suggests that cognitive style moderates decisions involving creativity in complex ways. It seems that in some contexts, decision makers will prefer a creative option, regardless of their cognitive style, if this option is both outside the bounds of what is officially allowed and yet ultimately safe. The thesis delivers innovation on three levels: theoretical, methodological and empirical. The highlights of these findings are outlined below: 1. Theoretical innovation with the conceptualisation of Creative Resolve Response based on an extension of Amabile’s research regarding creative motivation. 2. Theoretical innovation linking creative motivation and Kirton’s research on cognitive style. 3. Theoretical innovation linking both risk based and attribute framing effects to cognitive style. 4. Methodological innovation for defining and testing preferences for creative solution implementation in the form of operationalised creativity decision alternatives. 5. Methodological innovation to identify extreme decision options by applying Shafir’s findings regarding attribute framing effects in reverse to create a test. 6. Empirical innovation with statistically significant research findings which indicate creative motivation varies in a systematic way. 7. Empirical innovation with statistically significant research findings which identify innovation descriptor framing effects 8. Empirical innovation with statistically significant research findings which expand understanding of Kirton’s cognitive style descriptors including the importance of safe rule breaking. 9. Empirical innovation with statistically significant research findings which validate how framing effects do apply to decisions involving operationalised creativity. Drawing on previous research related to creative motivation, cognitive style, framing effects and supervisor interactions with employees, this study delivers insights which can assist managers to increase the production and implementation of creativity in organisations. Hopefully this will result in organisations which are more innovative. Such organisations have the potential to provide ongoing economic and social benefits.

Letting go of Neo-Liberalism (with some help from Michel Foucault)

Neo-liberalism has become one of the boom concepts of our time. From its original reference point as a descriptor of the economics of the “Chicago School” such as Milton Friedman, or authors such as Friedrich von Hayek, neo-liberalism has become an all-purpose descriptor and explanatory device for phenomena as diverse as Bollywood weddings, standardized testing in schools, violence in Australian cinema, and the digitization of content in public libraries. Moreover, it has become an entirely pejorative term: no-one refers to their own views as “neo-liberal”, but it rather refers to the erroneous views held by others, whether they acknowledge this or not. Neo-liberalism as it has come to be used, then, bears many of the hallmarks of a dominant ideology theory in the classical Marxist sense, even if it is often not explored in these terms. This presentation will take the opportunity provided by the English language publication of Michel Foucault’s 1978-79 lectures, under the title of The Birth of Biopolitics, to consider how he used the term neo-liberalism, and how this equates with its current uses in critical social and cultural theory. It will be argued that Foucault did not understand neo-liberalism as a dominant ideology in these lectures, but rather as marking a point of inflection in the historical evolution of liberal political philosophies of government. It will also be argued that his interpretation of neo-liberalism was more nuanced and more comparative than the more recent uses of Foucault in the literature on neo-liberalism. It will also look at how Foucault develops comparative historical models of liberal capitalism in The Birth of Biopolitics, arguing that this dimension of his work has been lost in more recent interpretations, which tend to retro-fit Foucault to contemporary critiques of either U.S. neo-conservatism or the “Third Way” of Tony Blair’s New Labour in the UK.

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is employed to find nonlinear relationships of the extracted color and texture features. The maximum likelihood approach is used to estimate the intrinsic dimensionality, which is then used as a criterion for automatic selection of optimal feature set from the fused feature. The proposed method is evaluated using SVM as the benchmark classifier and is applied to object-based vegetation species classification using high spatial resolution aerial imagery. Experimental results demonstrate that great improvement can be achieved by using proposed feature fusion method.

Michel Foucault's 'the birth of biopolitics' and contemporary neo-liberalism debates

Neo-liberalism has become one of the boom concepts of our time. From its original reference point as a descriptor of the economics of the ‘Chicago School’ or authors such as Friedrich von Hayek, neo-liberalism has become an all-purpose concept, explanatory device and basis for social critique. This presentation evaluates Michel Foucault’s 1978–79 lectures, published as The Birth of Biopolitics, to consider how he used the term neo-liberalism, and how this equates with its current uses in critical social and cultural theory. It will be argued that Foucault did not understand neo-liberalism as a dominant ideology in these lectures, but rather as marking a point of inflection in the historical evolution of liberal political philosophies of government. It will also be argued that his interpretation of neo-liberalism was more nuanced and more comparative than more recent contributions. The article points towards an attempt to theorize comparative historical models of liberal capitalism.

Re-framing fashion : from original and copy to adaptation

This article discusses the interaction between original and adaptation in the fashion system; the study also analyses, at a micro level, practices of adaptation adopted by consumers when making and re-making fashionable clothes. The article shows that the distinction between original and copy is historically determined as it grew out of the romantic notion of the authentic work of art. This article suggests that, in the impossibility to determine copyright in fashion, adaptation is a better descriptor of practices that transform garments; the concept of adaptation also abolishes trite notions of fashion as pastiche or bricolage, arguing for as a way to look at the many variations and re-contextualisations of garments historically and cross-culturally.

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.

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.

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.