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动态多目标决策问题的灰色分析方法 总被引:2,自引:0,他引:2
将用于固定时间截面下静态多目标决策的灰色关联理论推广到动态情形,引入局部理想最优效果和整体理想最优效果的概念,提出一种新型的动态多目标决策问题的灰色关联模型,并通过算例说明该方法的合理可行性。 相似文献
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We are concerned with the detection of edges—the location and amplitudes of jump discontinuities of piecewise smooth data realized in terms of its discrete grid values. We discuss the interplay between two approaches. One approach, realized in the physical space, is based on local differences and is typically limited to low-order of accuracy. An alternative approach developed in our previous work [Gelb and Tadmor, Appl. Comp. Harmonic Anal., 7, 101–135 (1999)] and realized in the dual Fourier space, is based on concentration factors; with a proper choice of concentration factors one can achieve higher-orders—in fact in [Gelb and Tadmor, SIAM J. Numer. Anal., 38, 1389–1408 (2001)] we constructed exponentially accurate edge detectors. Since the stencil of these highly-accurate detectors is global, an outside threshold parameter is required to avoid oscillations in the immediate neighborhood of discontinuities. In this paper we introduce an adaptive edge detection procedure based on a cross-breading between the local and global detectors. This is achieved by using the minmod limiter to suppress spurious oscillations near discontinuities while retaining high-order accuracy away from the jumps. The resulting method provides a family of robust, parameter-free edge-detectors for piecewise smooth data. We conclude with a series of one- and two-dimensional simulations.To David Gottlieb, on his 60th birthday, with friendship and appreciation. 相似文献
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A recently proposed argument to explain the improved performance of the eight-point algorithm that results from using normalized
data (Chojnacki, W., et al. in IEEE Trans. Pattern Anal. Mach. Intell. 25(9):1172–1177, 2003) relies upon adoption of a certain model for statistical data distribution. Under this model, the cost function that underlies
the algorithm operating on the normalized data is statistically more advantageous than the cost function that underpins the
algorithm using unnormalized data. Here we extend this explanation by introducing a more refined, structured model for data
distribution. Under the extended model, the normalized eight-point algorithm turns out to be approximately consistent in a
statistical sense. The proposed extension provides a link between the existing statistical rationalization of the normalized
eight-point algorithm and the approach of Mühlich and Mester for enhancing total least squares estimation methods via equilibration.
The paper forms part of a wider effort to rationalize and interrelate foundational methods in vision parameter estimation. 相似文献
129.
Gilles Celeux Author VitaeFlorence ForbesAuthor Vitae Nathalie Peyrard Author Vitae 《Pattern recognition》2003,36(1):131-144
Image segmentation using Markov random fields involves parameter estimation in hidden Markov models for which the EM algorithm is widely used. In practice, difficulties arise due to the dependence structure in the models and approximations are required. Using ideas from the mean field approximation principle, we propose a class of EM-like algorithms in which the computation reduces to dealing with systems of independent variables. Within this class, the simulated field algorithm is a new stochastic algorithm which appears to be the most promising for its good performance and speed, on synthetic and real image experiments. 相似文献
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