190 resultados para User images


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The location of a flame front is often taken as the point of maximum OH gradient. Planar laser-induced fluorescence of OH can be used to obtain the flame front by extracting the points of maximum gradient. This operation is typically performed using an edge detection algorithm. The choice of operating parameters a priori poses significant problems of robustness when handling images with a range of signal-to-noise ratios. A statistical method of parameter selection originating in the image processing literature is detailed, and its merit for this application is demonstrated. A reduced search space method is proposed to decrease computational cost and render the technique viable for large data sets. This gives nearly identical output to the full method. These methods demonstrate substantial decreases in data rejection compared to the use of a priori parameters. These methods are viable for any application where maximum gradient contours must be accurately extracted from images of species or temperature, even at very low signal-to-noise ratios.

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CLADP is an engineering software program developed at Cambridge University for the interactive computer aided design of feedback control systems. CLADP contains a wide range of tools for the analysis of complex systems, and the assessment of their performance when feedback control is applied, thus enabling control systems to be designed to meet difficult performance objectives. The range of tools within CLADP include the latest techniques in the field whose central theme is the extension of classical frequency domain concepts (well known and well proven for single loop systems) to multivariable or multiloop systems, and by making extensive use of graphical presentation information is provided in a readily understood form.

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In spite of over two decades of intense research, illumination and pose invariance remain prohibitively challenging aspects of face recognition for most practical applications. The objective of this work is to recognize faces using video sequences both for training and recognition input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. In particular there are three areas of novelty: (i) we show how a photometric model of image formation can be combined with a statistical model of generic face appearance variation, learnt offline, to generalize in the presence of extreme illumination changes; (ii) we use the smoothness of geodesically local appearance manifold structure and a robust same-identity likelihood to achieve invariance to unseen head poses; and (iii) we introduce an accurate video sequence "reillumination" algorithm to achieve robustness to face motion patterns in video. We describe a fully automatic recognition system based on the proposed method and an extensive evaluation on 171 individuals and over 1300 video sequences with extreme illumination, pose and head motion variation. On this challenging data set our system consistently demonstrated a nearly perfect recognition rate (over 99.7%), significantly outperforming state-of-the-art commercial software and methods from the literature. © Springer-Verlag Berlin Heidelberg 2006.

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Over the past decade, a variety of user models have been proposed for user simulation-based reinforcement-learning of dialogue strategies. However, the strategies learned with these models are rarely evaluated in actual user trials and it remains unclear how the choice of user model affects the quality of the learned strategy. In particular, the degree to which strategies learned with a user model generalise to real user populations has not be investigated. This paper presents a series of experiments that qualitatively and quantitatively examine the effect of the user model on the learned strategy. Our results show that the performance and characteristics of the strategy are in fact highly dependent on the user model. Furthermore, a policy trained with a poor user model may appear to perform well when tested with the same model, but fail when tested with a more sophisticated user model. This raises significant doubts about the current practice of learning and evaluating strategies with the same user model. The paper further investigates a new technique for testing and comparing strategies directly on real human-machine dialogues, thereby avoiding any evaluation bias introduced by the user model. © 2005 IEEE.

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For increasing the usability of a medical device the usability engineering standards IEC 60601-1-6 and IEC 62366 suggest incorporating user information in the design and development process. However, practice shows that integrating user information and the related investigation of users, called user research, is difficult in the field of medical devices. In particular, identifying the most appropriate user research methods is a difficult process. This difficulty results from the complexity of the medical device industry, especially with respect to regulations and standards, the characteristics of this market and the broad range of potential user research methods available from various research disciplines. Against this background, this study aimed at guiding designers and engineers in selecting effective user research methods according to their stage in the design process. Two approaches are described which reduce the complexity of method selection by summarizing the high number of methods into homogenous method classes. These approaches are closely connected to the medical device industry characteristic design phases and therefore provide the possibility of selecting design-phase- specific user research methods. In the first approach potential user research methods are classified after their characteristics in the design process. The second approach suggests a method summarization according to their similarity in the data collection techniques and provides an additional linkage to design phase characteristics. Both approaches have been tested in practice and the results show that both approaches facilitate user research method selection. © 2009 Springer-Verlag.

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FEniCS is a collection of software tools for the automated solution of differential equations by finite element methods. In this note, we describe how FEniCS can be used to solve a simple nonlinear model problem with varying levels of automation. At one extreme, FEniCS provides tools for the fully automated and adaptive solution of nonlinear partial differential equations. At the other extreme, FEniCS provides a range of tools that allow the computational scientist to experiment with novel solution algorithms. © 2010 American Institute of Physics.

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This paper presents the results of a study that specifically looks at the relationships between measured user capabilities and product demands in a sample of older and disabled users. An empirical study was conducted with 19 users performing tasks with four consumer products (a clock-radio, a mobile phone, a blender and a vacuum cleaner). The sensory, cognitive and motor capabilities of each user were measured using objective capability tests. The study yielded a rich dataset comprising capability measures, product demands, outcome measures (task times and errors), and subjective ratings of difficulty. Scatter plots were produced showing quantified product demands on user capabilities, together with subjective ratings of difficulty. The results are analysed in terms of the strength of correlations observed taking into account the limitations of the study sample. Directions for future research are also outlined. © 2011 Springer-Verlag.