Results 261 to 270 of about 369,804 (310)
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IEEE Transactions on Image Processing, 2007
Active contours, or so-called snakes, require some parameters to determine the form of the external force or to adjust the tradeoff between the internal forces and the external forces acting on the active contour. However, the optimal values of these parameters cannot be easily identified in a general sense.
Umut Ozertem, Deniz Erdogmus
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Active contours, or so-called snakes, require some parameters to determine the form of the external force or to adjust the tradeoff between the internal forces and the external forces acting on the active contour. However, the optimal values of these parameters cannot be easily identified in a general sense.
Umut Ozertem, Deniz Erdogmus
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American Journal of Occupational Therapy, 1986
This paper proposes the use of nonparametric statistical methods as a way to increase the quality and quantity of research in occupational therapy. Assumptions underlying parametric versus nonparametric methods are compared and the conditions under which nonparametric methods may be preferred are specified.
C B, Royeen, W L, Seaver
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This paper proposes the use of nonparametric statistical methods as a way to increase the quality and quantity of research in occupational therapy. Assumptions underlying parametric versus nonparametric methods are compared and the conditions under which nonparametric methods may be preferred are specified.
C B, Royeen, W L, Seaver
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Proceedings of the AAAI Conference on Artificial Intelligence, 2011
Anytime variants of Dijkstra's and A* shortest path algorithms quickly produce a suboptimal solution and then improve it over time. For example, ARA* introduces a weighting value "epsilon" to rapidly find an initial suboptimal path and then reduces "epsilon" to improve path quality over time.
Jur van den Berg +3 more
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Anytime variants of Dijkstra's and A* shortest path algorithms quickly produce a suboptimal solution and then improve it over time. For example, ARA* introduces a weighting value "epsilon" to rapidly find an initial suboptimal path and then reduces "epsilon" to improve path quality over time.
Jur van den Berg +3 more
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Nonparametric Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983A nonparametric method of discriminant analysis is proposed. It is based on nonparametric extensions of commonly used scatter matrices. Two advantages result from the use of the proposed nonparametric scatter matrices. First, they are generally of full rank. This provides the ability to specify the number of extracted features desired.
Keinosuke Fukunaga, James M. Mantock
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Nonparametric Background Generation
18th International Conference on Pattern Recognition (ICPR'06), 2006A novel background generation method based on nonparametric background model is presented for background subtraction. We introduce a new model, named as effect components description (ECD), to model the variation of the background, by which we can relate the best estimate of the background to the modes (local maxima) of the underlying distribution ...
Yazhou Liu +4 more
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IEEE Transactions on Information Theory, 1993
Summary: Burg's standard maximum entropy method and the resulting autoregressive model has been widely applied for spectrum estimation and prediction. A generalization of the maximum entropy formalism in a nonparametric setting is presented, and the class of the resulting solutions is identified to be a class of Markov processes.
Politis, Dimitris Nicolas +1 more
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Summary: Burg's standard maximum entropy method and the resulting autoregressive model has been widely applied for spectrum estimation and prediction. A generalization of the maximum entropy formalism in a nonparametric setting is presented, and the class of the resulting solutions is identified to be a class of Markov processes.
Politis, Dimitris Nicolas +1 more
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On Nonparametric Estimation of a Nonparametric Autoregressive Conditionally Heteroscedastic Process
Afrika Statistika, 2022Since the studies of Engel (1982) and Bollerslev (1986), the ARCH and GARCH processes have been used extensively to model volatile series. However, Pagan and Schwert (1990) have shown the limits of these choices. This deficiency is overcome by the NonParametric AutoRegressive Conditionally Heteroscedastic (NPARCH) processes.
Kouassi, Ben Célestin +2 more
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984
A nonparametric data reduction technique is proposed. Its goal is to select samples that are ``representative'' of the entire data set. The technique is iterative and is based on the use of a criterion function and nearest neighbor density estimates. Experiments are presented to demonstrate the algorithm.
Keinosuke Fukunaga, James M. Mantock
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A nonparametric data reduction technique is proposed. Its goal is to select samples that are ``representative'' of the entire data set. The technique is iterative and is based on the use of a criterion function and nearest neighbor density estimates. Experiments are presented to demonstrate the algorithm.
Keinosuke Fukunaga, James M. Mantock
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