B-spline signal processing. II. Efficiency design and applications

Unser, Michaël; Aldroubi, Akram; Eden, Murray

doi:10.1109/78.193221

Cited by 641 publications

(416 citation statements)

References 27 publications

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“…This can be done either by matrix manipulations [20,27], or by means of recursive filtering techniques [55][56][57], the latter of which are easier to implement and computationally much more efficient. When using the latter approach, nth-degree spline interpolation is carried out by evaluating the following expression:…”

Section: Cardinal Spline Kernelsmentioning

confidence: 99%

“…where (b n ) −1 denotes the recursive prefilter, also known as the direct B-spline filter [55][56][57]. Note that the resulting interpolant is an element of C n−1 .…”

Section: Cardinal Spline Kernelsmentioning

confidence: 99%

“…We also note that for n → ∞, the cardinal spline converges to the sinc function [1,50]. For further details regarding the theoretical and implementational aspects of B-spline interpolation, we refer to Unser et al [56,57].…”

Section: Cardinal Spline Kernelsmentioning

confidence: 99%

See 2 more Smart Citations

Quantitative evaluation of convolution-based methods for medical image interpolation

Meijering

Niessen

Viergever

2001

Medical Image Analysis

254

165

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Abstract-Interpolation is required in a variety of medical image processing applications. Although many interpolation techniques are known from the literature, evaluations of these techniques for the specific task of applying geometrical transformations to medical images are still lacking. In this paper we present such an evaluation. We consider convolution-based interpolation methods and rigid transformations (rotations and translations). A large number of sincapproximating kernels are evaluated, including piecewise polynomial kernels and a large number of windowed sinc kernels, with spatial supports ranging from two to ten grid intervals. In the evaluation we use images from a wide variety of medical image modalities. The results show that spline interpolation is to be preferred over all other methods, both for its accuracy and its relatively low computational cost.

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Section: Cardinal Spline Kernelsmentioning

confidence: 99%

“…where (b n ) −1 denotes the recursive prefilter, also known as the direct B-spline filter [55][56][57]. Note that the resulting interpolant is an element of C n−1 .…”

Section: Cardinal Spline Kernelsmentioning

confidence: 99%

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Quantitative evaluation of convolution-based methods for medical image interpolation

Meijering

Niessen

Viergever

2001

Medical Image Analysis

254

165

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“…So far SMA filter is effectively used by different scholars [35,36] in engineering due to its characteristic to remove noise in random samples and compute the monitoring values to predict the future data. Furthermore, the moving average filter (MAF) is also well documented and explained in the discrete signal processing literature [37,38].…”

Section: Proposed Methodsmentioning

confidence: 99%

Performance Analysis of Incremental Conductance MPPT with Simple Moving Voltage Average Method for Distributed PV System

Ali¹,

Li²,

He³

2016

TOEEJ

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Abstract:In order to harvest photovoltaic energy efficiently, several methods exist, yet most of them failed to address the issues related to extract the maximum power under rapidly changing solar irradiance conditions. In conventional incremental conductance, large step size reduces tracking time but oscillation remains around maximum power point (MPP). However, small step size reduces the oscillation but results in slower tracking speed. This paper proposes a simple moving voltage average (SMVA) technique in conjunction with fixed step direct control incremental conductance (INC) maximum power point tracking (MPPT) method in order to reduce the photovoltaic (PV) generated voltage (V PV ) fluctuation and power losses under mismatching solar irradiance conditions in distributed PV system. Theoretical analysis and the simulation results revealed that the proposed SMVA technique provides fast and accurate tracking under mismatching irradiance conditions. Also, it significantly improves the voltage stability because of extremely small |dP/dV| around MPP as compared to the conventional fixed step direct control incremental conductance MPPT method. Finally, results show that the proposed method is suitable for distributed PV system under intermittent weather conditions not only in terms of voltage stability but also in overall system efficiency.

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“…Noting that c = c(K), and according to expression (24), the MAP estimate of the space dimension is given bŷ…”

Section: Unknown Space Dimensionmentioning

confidence: 99%

Adaptive Bayesian Contour Estimation: A Vector Space Representation Approach

Dias¹

1999

Lecture Notes in Computer Science

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Abstract. We p r o p o s e a v ector representation approach t o c o n tour estimation from noisy data. Images are modeled as random elds composed of a set of homogeneous regions contours (boundaries of homogeneous regions) are assumed to be vectors of a subspace of L 2 (T ) generated by a g i v en nite basis B-splines, Sinc-type, and Fourier bases are considered. The main contribution of the paper is a smoothing criterion, interpretable as a priori contour probability, based on the Kullback distance between neighboring densities. The maximum a posteriori probability (MAP) estimation criterion is adopted. To solve the optimization problem one is led to (joint estimation of contours, subspace dimension, and model parameters), we propose a gradient projection type algorithm. A set of experiments performed on simulated an real images illustrates the potencial of the proposed methodology

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B-spline signal processing. II. Efficiency design and applications

Cited by 641 publications

References 27 publications

Quantitative evaluation of convolution-based methods for medical image interpolation

Quantitative evaluation of convolution-based methods for medical image interpolation

Performance Analysis of Incremental Conductance MPPT with Simple Moving Voltage Average Method for Distributed PV System

Adaptive Bayesian Contour Estimation: A Vector Space Representation Approach

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