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The Gaussian scale-space paradigm and the multiscale local jet 总被引:3,自引:3,他引:0
Luc Florack Bart Ter Haar Romeny Max Viergever Jan Koenderink 《International Journal of Computer Vision》1996,18(1):61-75
A representation of local image structure is proposed which takes into account both the image's spatial structure at a given location, as well as its deep structure, that is, its local behaviour as a function of scale or resolution (scale-space). This is of interest for several low-level image tasks. The proposed basis of scale-space, for example, enables a precise local study of interactions of neighbouring image intensities in the course of the blurring process. It also provides an extrapolation scheme for local image data, obtained at a given spatial location and resolution, to a finite scale-space neighbourhood. This is especially useful for the determination of sampling rates and for interpolation algorithms in a multilocal context. Another, particularly straightforward application is image enhancement or deblurring, which is an instance of data extrapolation in the high-resolution direction.A potentially interesting feature of the proposed local image parametrisation is that it captures a trade-off between spatial and scale extrapolations from a given interior point that do not exceed a given tolerance. This (rade-off suggests the possibility of a fairly coarse scale sampling at the expense of a dense spatial sampling large relative spatial overlap of scale-space kernels).The central concept developed in this paper is an equivalence class called the multiscale local jet, which is a hierarchical, local characterisation of the image in a full scale-space neighbourhood. For this local jet, a basis of fundamental polynomials is constructed that captures the scale-space paradigm at the local level up to any given order. 相似文献
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A portfolio selection model which allocates a portfolio of currencies by maximizing the expected return subject to Value-at-Risk (VaR) constraint is designed and implemented. Based on an econometric implementation using intradaily data, the optimal portfolio allocation is forecasted at regular time intervals. For the estimation of the conditional variance from which the VaR is computed, univariate and multivariate GARCH models are used. Model evaluation is done using two economic criteria and two statistical tests. The result for each model is given by the best forecasted intradaily investment recommendations in terms of the optimal weights of the currencies in the risky portfolio. The results show that estimating the VaR from multivariate GARCH models improves the results of the forecasted optimal portfolio allocation, compared to using a univariate model. 相似文献
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This article presents a visual object tracking method and applies an event-based performance evaluation metric for assessment.
The proposed monocular object tracker is able to detect and track multiple object classes in non-controlled environments.
The tracking framework uses Bayesian per-pixel classification to segment an image into foreground and background objects,
based on observations of object appearances and motions in real-time. Furthermore, a performance evaluation method is presented
and applied to different state-of-the-art trackers based on successful detections of semantically high level events. These
events are extracted automatically from the different trackers an their varying types of low level tracking results. Then,
a general new event metric is used to compare our tracking method with the other tracking methods against ground truth of
multiple public datasets. 相似文献
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Ramón Zatarain Cabada María Lucía Barrón Estrada Carlos Alberto Reyes García 《Expert systems with applications》2011,38(8):9522-9529
This paper presents a Web 2.0 Learning Environment, for a systematic creation of adaptive and intelligent tutoring systems. Authoring contents is made by a community of users including teachers and students. The tutoring systems adapt the contents according to the best learning style using self-organizing maps (SOMs). The SOM was trained for classifying Felder–Silverman learning styles. The most important advantage of these unsupervised neural networks is that they do not require an external teacher for presenting a training set. The approach was implemented under an authoring tool that allows the production of personalized learning material to be used under collaborative and mobile learning environments. The tutoring systems together with the neural network can also be exported to mobile devices. We present different results to the approach working under the authoring tool. 相似文献
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