Face and Object Recognition and Detection Using Colour Vector Quantisation

In this paper we present an approach to face and object detection and recognition based on an extension of the contentbased image retrieval method of Lu and Teng (1999). The method applies vector quantisation (VQ) compression to the image stream and uses Mahalonobis weighted Euclidean distance between VQ histograms as the measure of image similarity. This distance measure retains both colour and spatial feature information but has the useful property of being relatively insensitive to changes in scale and rotation. The method is applied to real images for face recognition and face detection applications. Tracking and object detection can be coded relatively efficiently due to the data reduction afforded by VQ compression of the data stream. Additional computational efficiency is obtained through a variation of the tree structured fast VQ algorithm also presented here

Homogenised Virtual Support Vector Machines

In many domains, reliable a priori knowledge exists that may be used to improve classifier performance. For example in handwritten digit recognition, such a priori knowledge may include classification invariance with respect to image translations and rotations. In this paper, we present a new generalisation of the Support Vector Machine (SVM) that aims to better incorporate this knowledge. The method is an extension of the Virtual SVM, and penalises an approximation of the variance of the decision function across each grouped set of "virtual examples", thus utilising the fact that these groups should ideally be assigned similar class membership probabilities. The method is shown to be an efficient approximation of the invariant SVM of Chapelle and Scholkopf, with the advantage that it can be solved by trivial modification to standard SVM optimization packages and negligible increase in computational complexity when compared with the Virtual SVM. The efficacy of the method is demonstrated on a simple problem

Support Vector Machines for Business Applications

This chapter discusses the usage of Support Vector Machines (SVM) for business applications. It provides a brief historical background on inductive learning and pattern recognition, and then an intuitive motivation for SVM methods. The method is compared to other approaches, and the tools and background theory required to successfully apply SVMs to business applications are introduced. The authors hope that the chapter will help practitioners to understand when the SVM should be the method of choice, as well as how to achieve good results in minimal time

Kernel Based Algebraic Curve Fitting

An algebraic curve is defined as the zero set of a multivariate polynomial. We consider the problem of fitting an algebraic curve to a set of vectors given an additional set of vectors labelled as interior or exterior to the curve. The problem of fitting a linear curve in this way is shown to lend itself to a support vector representation, allowing non-linear curves and high dimensional surfaces to be estimated using kernel functions. The approach is attractive due to the stability of solutions obtained, the range of functional forms made possible (including polynomials), and the potential for applying well understood regularisation operators from the theory of Support Vector Machines

Towards a Maximum Entropy Method for Estimating HMM Parameters

Training a Hidden Markov Model (HMM) to maximise the probability of a given sequence can result in over-fitting. That is, the model represents the training sequence well, but fails to generalise. In this paper, we present a possible solution to this problem, which is to maximise a linear combination of the likelihood of the training data, and the entropy of the model. We derive the necessary equations for gradient based maximisation of this combined term. The performance of the system is then evaluated in comparison with three other algorithms, on a classification task using synthetic data. The results indicate that the method is potentially useful. The main problem with the method is the computational intractability of the entropy calculation

Algebraic Curve Fitting Support Vector Machines

An algebraic curve is defined as the zero set of a multivariate polynomial. We consider the problem of fitting an algebraic curve to a set of vectors given an additional set of vectors labelled as interior or exterior to the curve. The problem of fitting a linear curve in this way is shown to lend itself to a support vector representation, allowing non-linear curves and high dimensional surfaces to be estimated using kernel functions. The approach is attractive due to the stability of solutions obtained, the range of functional forms made ossible (including polynomials), and the potential for applying well understood regularisation operators from the theory of Support Vector Machines

Improved Classification Using Hidden Markov Averaging From Multiple Observation Sequences

The enormous popularity of Hidden Markov models (HMMs) in spatio-temporal pattern recognition is largely due to the ability to 'learn' model parameters from observation sequences through the Baum-Welch and other re-estimation procedures. In this study, HMM parameters are estimated from an ensemble of models trained on individual observation sequences. The proposed methods are shown to provide superior classification performance to competing methods

Parasitismo de larvas da mosca-do-mediterrâneo por Diachasmimorpha longicaudata (Ashmead) (Hymenoptera: Braconidae) em diferentes cultivares de goiaba.

As moscas-das-frutas são as pragas que causam os maiores prejuízos à fruticultura mundial. Esses prejuízos podem ser diretos, com perdas na produção e indiretos. Por serem pragas quarentenárias, existem barreiras comerciais impostas pelos países importadores, limitando a exportação de frutos in natura. Apesar de o Brasil ser o terceiro maior produtor mundial de frutas, exporta-se apenas 2% dessa produção, enquanto que Diachasmimorpha longicaudata infestadas com larvas de Ceratitis capitata (Wiedemann) (Diptera: Tephritidae). Cinco lotes de oito frutos de goiaba, sendo dois frutos por cultivar, foram acondicionados em gaiolas contendo adultos de C. capitata, por 2h para oviposição. Após uma semana, quando as larvas já haviam se desenvolvido dentro dos frutos, estes foram expostos aos parasitóides durante 24h. Foram avaliados o peso médio dos frutos, a mortalidade das larvas, o número de pupários e as porcentagens de moscas e parasitóides emergidos

Bilateral Pneumothoraces Following Central Venous Cannulation

We report the occurrence of a bilateral pneumothoraces after unilateral central venous catheterization of the right subclavian vein in a 70-year-old patient. The patient had no history of pulmonary or pleural disease and no history of cardiothoracic surgery. Two days earlier, she had a median laparotomy under general and epidural anaesthesia. Prior to the procedure, the patient was hemodynamically stable and her transcutaneous oxygen saturation was 97% in room air. We punctured the right pleural space before cannulation of the right subclavian vein. After the procedure, the patient slowly became hemodynamically instable with respiratory distress. A chest radiograph revealed a complete left-side pneumothorax and a mild right-side pneumothorax. The right-side pneumothorax became under tension after left chest tube insertion. The symptoms finally resolved after insertion of a right chest tube. After a diagnostic work-up, we suspect a congenital “Buffalo chests” explaining bilateral pneumothoraces and a secondary tension pneumothorax

The Dynamics of Radiative Shock Waves: Linear and Nonlinear Evolution

The stability properties of one-dimensional radiative shocks with a power-law cooling function of the form $\Lambda \propto \rho^2T^\alpha$ are the main subject of this work. The linear analysis originally presented by Chevalier & Imamura, is thoroughfully reviewed for several values of the cooling index $\alpha$ and higher overtone modes. Consistently with previous results, it is shown that the spectrum of the linear operator consists in a series of modes with increasing oscillation frequency. For each mode a critical value of the cooling index, $\alpha_\textrm{c}$, can be defined so that modes with $\alpha < \alpha_\textrm{c}$ are unstable, while modes with $\alpha > \alpha_\textrm{c}$ are stable. The perturbative analysis is complemented by several numerical simulations to follow the time-dependent evolution of the system for different values of $\alpha$. Particular attention is given to the comparison between numerical and analytical results (during the early phases of the evolution) and to the role played by different boundary conditions. It is shown that an appropriate treatment of the lower boundary yields results that closely follow the predicted linear behavior. During the nonlinear regime, the shock oscillations saturate at a finite amplitude and tend to a quasi-periodic cycle. The modes of oscillations during this phase do not necessarily coincide with those predicted by linear theory, but may be accounted for by mode-mode coupling.Comment: 33 pages, 12 figures, accepted for publication on the Astrophysical Journa