Large margin vector quantization

In this paper we describe the Large Margin Vector Quantization algorithm (LMVQ), which uses gradient ascent to maximise the margin of a radial basis function classifier. We present a derivation of the algorithm, which proceeds from an estimate of the class-conditional probability densities. We show that the key behaviour of Kohonen's well-known LVQ2 and LVQ3 algorithms emerge as natural consequences of our formulation. We compare the performance of LMVQ with that of Kohonen's LVQ algorithms on an artificial classification problem and several well known benchmark classification tasks. We find that the classifiers produced by LMVQ attain a level of accuracy that compares well with those obtained via LVQ1, LVQ2 and LVQ3, with reduced storage complexity. We indicate future directions of enquiry based on the large margin approach to Learning Vector Quantization.

Spatial information infrastructure : what can it possibly mean?

Investment begins with imagining that doing something new in the present will lead to a better future. Investment can vary from incidental improvements as safe and beneficial side-effects of current activity through to a more dedicated and riskier disinvestment in current methods of operation and reinvestment in new processes and products. The role of government has an underlying continuity determined by its constitution that authorises a parliament to legislate for peace, order and good government. ‘Good government’ is usually interpreted as improving the living standards of its citizens. The requirements for social order and social cohesion suggest that improvements should be shared fairly by all citizens through all of their lives. Arguably, the need to maintain an individual’s metabolism has a social counterpart in the ‘collective metabolism’ of a sustainable and productive society.

Fingerprint analysis using wavelet transform with application to compression and feature extraction

The main goal of this research is to design an efficient compression al~ gorithm for fingerprint images. The wavelet transform technique is the principal tool used to reduce interpixel redundancies and to obtain a parsimonious representation for these images. A specific fixed decomposition structure is designed to be used by the wavelet packet in order to save on the computation, transmission, and storage costs. This decomposition structure is based on analysis of information packing performance of several decompositions, two-dimensional power spectral density, effect of each frequency band on the reconstructed image, and the human visual sensitivities. This fixed structure is found to provide the "most" suitable representation for fingerprints, according to the chosen criteria. Different compression techniques are used for different subbands, based on their observed statistics. The decision is based on the effect of each subband on the reconstructed image according to the mean square criteria as well as the sensitivities in human vision. To design an efficient quantization algorithm, a precise model for distribution of the wavelet coefficients is developed. The model is based on the generalized Gaussian distribution. A least squares algorithm on a nonlinear function of the distribution model shape parameter is formulated to estimate the model parameters. A noise shaping bit allocation procedure is then used to assign the bit rate among subbands. To obtain high compression ratios, vector quantization is used. In this work, the lattice vector quantization (LVQ) is chosen because of its superior performance over other types of vector quantizers. The structure of a lattice quantizer is determined by its parameters known as truncation level and scaling factor. In lattice-based compression algorithms reported in the literature the lattice structure is commonly predetermined leading to a nonoptimized quantization approach. In this research, a new technique for determining the lattice parameters is proposed. In the lattice structure design, no assumption about the lattice parameters is made and no training and multi-quantizing is required. The design is based on minimizing the quantization distortion by adapting to the statistical characteristics of the source in each subimage. 11 Abstract Abstract Since LVQ is a multidimensional generalization of uniform quantizers, it produces minimum distortion for inputs with uniform distributions. In order to take advantage of the properties of LVQ and its fast implementation, while considering the i.i.d. nonuniform distribution of wavelet coefficients, the piecewise-uniform pyramid LVQ algorithm is proposed. The proposed algorithm quantizes almost all of source vectors without the need to project these on the lattice outermost shell, while it properly maintains a small codebook size. It also resolves the wedge region problem commonly encountered with sharply distributed random sources. These represent some of the drawbacks of the algorithm proposed by Barlaud [26). The proposed algorithm handles all types of lattices, not only the cubic lattices, as opposed to the algorithms developed by Fischer [29) and Jeong [42). Furthermore, no training and multiquantizing (to determine lattice parameters) is required, as opposed to Powell's algorithm [78). For coefficients with high-frequency content, the positive-negative mean algorithm is proposed to improve the resolution of reconstructed images. For coefficients with low-frequency content, a lossless predictive compression scheme is used to preserve the quality of reconstructed images. A method to reduce bit requirements of necessary side information is also introduced. Lossless entropy coding techniques are subsequently used to remove coding redundancy. The algorithms result in high quality reconstructed images with better compression ratios than other available algorithms. To evaluate the proposed algorithms their objective and subjective performance comparisons with other available techniques are presented. The quality of the reconstructed images is important for a reliable identification. Enhancement and feature extraction on the reconstructed images are also investigated in this research. A structural-based feature extraction algorithm is proposed in which the unique properties of fingerprint textures are used to enhance the images and improve the fidelity of their characteristic features. The ridges are extracted from enhanced grey-level foreground areas based on the local ridge dominant directions. The proposed ridge extraction algorithm, properly preserves the natural shape of grey-level ridges as well as precise locations of the features, as opposed to the ridge extraction algorithm in [81). Furthermore, it is fast and operates only on foreground regions, as opposed to the adaptive floating average thresholding process in [68). Spurious features are subsequently eliminated using the proposed post-processing scheme.

What do IPAQ questions mean to older adults? Lessons from cognitive interviews

Background Most questionnaires used for physical activity (PA) surveillance have been developed for adults aged ≤65 years. Given the health benefits of PA for older adults and the aging of the population, it is important to include adults aged 65+ years in PA surveillance. However, few studies have examined how well older adults understand PA surveillance questionnaires. This study aimed to document older adults’ understanding of questions from the International PA Questionnaire (IPAQ), which is used worldwide for PA surveillance. Methods Participants were 41 community-dwelling adults aged 65-89 years. They each completed IPAQ in a face-to-face semi-structured interview, using the “think-aloud” method, in which they expressed their thoughts out loud as they answered IPAQ questions. Interviews were transcribed and coded according to a three-stage model: understanding the intent of the question; performing the primary task (conducting the mental operations required to formulate a response); and response formatting (mapping the response into pre-specified response options). Results Most difficulties occurred during the understanding and performing the primary task stages. Errors included recalling PA in an “average” week, not in the previous 7 days; including PA lasting ≤10 minutes/session; reporting the same PA twice or thrice; and including the total time of an activity for which only a part of that time was at the intensity specified in the question. Participants were unclear what activities fitted within a question’s scope and used a variety of strategies for determining the frequency and duration of their activities. Participants experienced more difficulties with the moderate-intensity PA and walking questions than with the vigorous-intensity PA questions. The sitting time question, particularly difficult for many participants, required the use of an answer strategy different from that used to answer questions about PA. Conclusions These findings indicate a need for caution in administering IPAQ to adults aged ≥65 years. Most errors resulted in over-reporting, although errors resulting in under-reporting were also noted. Given the nature of the errors made by participants, it is possible that similar errors occur when IPAQ is used in younger populations and that the errors identified could be minimized with small modifications to IPAQ.

Vector control of a high power induction machine

The design and implementation of a high-power (2 MW peak) vector control drive is described. The inverter switching frequency is low, resulting in high-harmonic-content current waveforms. A block diagram of the physical system is given, and each component is described in some detail. The problem of commanded slip noise sensitivity, inherent in high-power vector control drives, is discussed, and a solution is proposed. Results are given which demonstrate the successful functioning of the system

Sustained convergence, axial length, and corneal topography

Purpose: To investigate the influence of convergence on axial length and corneal topography in young adult subjects.---------- Methods: Fifteen emmetropic young adult subjects with normal binocular vision had axial length and corneal topography measured immediately before and after a 15-min period of base out (BO) prismatic spectacle lens wear. Two different magnitude prismatic spectacles were worn in turn (8 [DELTA] BO and 16 [DELTA] BO), and for both tasks, distance fixation was maintained for the duration of lens wear. Eight subjects returned on a separate day for further testing and had axial length measured before, during, and immediately after a 15-min convergence task.---------- Results: No significant change was found to occur in axial length either during or after the sustained convergence tasks (p > 0.6). Some small but significant changes in corneal topography were found to occur after sustained convergence. The most significant corneal change was observed after the 16 [DELTA] BO prism wear. The corneal refractive power spherocylinder power vector J0 was found to change by a small (mean change of 0.03 D after the 16 [DELTA] BO task) but statistically significant (p = 0.03) amount as a result of the convergence task (indicative of a reduction in with-the-rule corneal astigmatism after convergence). Corneal axial power was found to exhibit a significant flattening in superior regions. Conclusions: Axial length appears largely unchanged by a period of sustained convergence. However, small but significant changes occur in the topography of the cornea after convergence.

Correcting mean-field approximations for birth-death-movement processes

On the microscale, migration, proliferation and death are crucial in the development, homeostasis and repair of an organism; on the macroscale, such effects are important in the sustainability of a population in its environment. Dependent on the relative rates of migration, proliferation and death, spatial heterogeneity may arise within an initially uniform field; this leads to the formation of spatial correlations and can have a negative impact upon population growth. Usually, such effects are neglected in modeling studies and simple phenomenological descriptions, such as the logistic model, are used to model population growth. In this work we outline some methods for analyzing exclusion processes which include agent proliferation, death and motility in two and three spatial dimensions with spatially homogeneous initial conditions. The mean-field description for these types of processes is of logistic form; we show that, under certain parameter conditions, such systems may display large deviations from the mean field, and suggest computationally tractable methods to correct the logistic-type description.

Accurate urban road model reconstruction from high resolution remotely sensed imagery based on Support Vector Machine and Gabor filters

In this paper, we presented an automatic system for precise urban road model reconstruction based on aerial images with high spatial resolution. The proposed approach consists of two steps: i) road surface detection and ii) road pavement marking extraction. In the first step, support vector machine (SVM) was utilized to classify the images into two categories: road and non-road. In the second step, road lane markings are further extracted on the generated road surface based on 2D Gabor filters. The experiments using several pan-sharpened aerial images of Brisbane, Queensland have validated the proposed method.

Evaluation of spectral and texture features for object-based vegetation species classification using Support Vector Machines

The use of appropriate features to characterize an output class or object is critical for all classification problems. This paper evaluates the capability of several spectral and texture features for object-based vegetation classification at the species level using airborne high resolution multispectral imagery. Image-objects as the basic classification unit were generated through image segmentation. Statistical moments extracted from original spectral bands and vegetation index image are used as feature descriptors for image objects (i.e. tree crowns). Several state-of-art texture descriptors such as Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Patterns (LBP) and its extensions are also extracted for comparison purpose. Support Vector Machine (SVM) is employed for classification in the object-feature space. The experimental results showed that incorporating spectral vegetation indices can improve the classification accuracy and obtained better results than in original spectral bands, and using moments of Ratio Vegetation Index obtained the highest average classification accuracy in our experiment. The experiments also indicate that the spectral moment features also outperform or can at least compare with the state-of-art texture descriptors in terms of classification accuracy.

Developments in texture-based vector field visualisation techniques

Vector field visualisation is one of the classic sub-fields of scientific data visualisation. The need for effective visualisation of flow data arises in many scientific domains ranging from medical sciences to aerodynamics. Though there has been much research on the topic, the question of how to communicate flow information effectively in real, practical situations is still largely an unsolved problem. This is particularly true for complex 3D flows. In this presentation we give a brief introduction and background to vector field visualisation and comment on the effectiveness of the most common solutions. We will then give some examples of current development on texture-based techniques, and given practical examples of their use in CFD research and hydrodynamic applications.

Molecular evolutionary dynamics of Ross River virus and implications for vaccine efficacy

Ross River virus (RRV) is a mosquito-borne member of the genus Alphavirus that causes epidemic polyarthritis in humans, costing the Australian health system at least US$10 million annually. Recent progress in RRV vaccine development requires accurate assessment of RRV genetic diversity and evolution, particularly as they may affect the utility of future vaccination. In this study, we provide novel RRV genome sequences and investigate the evolutionary dynamics of RRV from time-structured E2 gene datasets. Our analysis indicates that, although RRV evolves at a similar rate to other alphaviruses (mean evolutionary rate of approx. 8x10(-4) nucleotide substitutions per site year(-1)), the relative genetic diversity of RRV has been continuously low through time, possibly as a result of purifying selection imposed by replication in a wide range of natural host and vector species. Together, these findings suggest that vaccination against RRV is unlikely to result in the rapid antigenic evolution that could compromise the future efficacy of current RRV vaccines.

Robust mean super-resolution for less cooperative NIR iris recognition at a distance and on the move

Less cooperative iris identification systems at a distance and on the move often suffers from poor resolution. The lack of pixel resolution significantly degrades the iris recognition performance. Super-resolution has been considered to enhance resolution of iris images. This paper proposes a pixelwise super-resolution technique to reconstruct a high resolution iris image from a video sequence of an eye. A novel fusion approach is proposed to incorporate information details from multiple frames using robust mean. Experiments on the MBGC NIR portal database show the validity of the proposed approach in comparison with other resolution enhancement techniques.