Edges characterize boundaries and are therefore a problem of fundamental importance in image processing. Edge detecting an image significantly reduces the amount of data and filters out useless information, while pres...
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Edges characterize boundaries and are therefore a problem of fundamental importance in image processing. Edge detecting an image significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. Edge detection is useful for segmentation, registration, and identification of objects in remote sensing images. Two dimensional lattice filters have been shown to be useful in many applications such as multidimensional spectral estimation, image data compression, high-resolution radar imaging, and removal of correlated clutter to enhance the detection ability of small objects in images. In this work, lattice filters are used for detecting the edges in remote sensing images. Lattice filter can be used to predict the correlated parts in an image and the resulting error (the output of the filter) will be edges. Edge detection results have been compared with other conventional edge detection methods as well as wavelet based methods. Results show that the proposed method is a good candidate for edge detection problem in remotely sensed images
The purpose of image segmentation is to partition an image into homogeneous regions. Features are of major importance in image segmentation. In this work, a new method is proposed in which features used for segmentati...
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The purpose of image segmentation is to partition an image into homogeneous regions. Features are of major importance in image segmentation. In this work, a new method is proposed in which features used for segmentation are reflection coefficients of the two-dimensional (2D) orthogonal lattice filters. Principal component analysis (PCA) is applied to the features for reducing the complexity of the work. A minimum distance classifier is used in the classification algorithms. The proposed method is compared with the discrete wavelet transform which is a common segmentation algorithm. In our work, selected image is a monospectral optical image
In this work, the radial time-independent Schrödinger equation of a screened Coulomb potential system at the zero energy limit is first converted to a weighted eigenvalue problem of an ordinary differential opera...
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This work aims to apply High Dimensional Model Representation (HDMR) to the sensitivity coefficient determination of the solutions of a multivariate extrema problem. The derivations are made for general functional str...
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In this work, a novel approximation scheme based on a recently developed representation, called the High Dimensional Model Representation (HDMR), is proposed to approximate evolution operators. The approximation is no...
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This work deals with the optimal control of one dimensional quantum harmonic oscillator under an external field characterized by a linear dipole function. The penalty term is taken as kinetic energy. The objective ope...
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The main purpose of this work is to obtain the general structure of a product type of multivariate function when the values of the function are given randomly at the nodes of a hyperprism. When the dimensionality of m...
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In this work, the optimal control of an harmonic oscillator is considered. The external field is assumed to be weak and hence is represented by only dipole interaction. The dipole function is taken as a second degree ...
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A parallel implementation for linear set of equations of the form Ax = b is presented in this paper. In this implementation, instead of the traditional direct solution of Ax = b, conjugate gradient method is used. The...
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A parallel implementation for linear set of equations of the form Ax = b is presented in this paper. In this implementation, instead of the traditional direct solution of Ax = b, conjugate gradient method is used. The conjugate gradient method is accelerated with an approximate inverse matrix preconditioner obtained from a linear combination of matrix-valued Chebyshev polynomials. This implementation is tested on a Sun SMP machine. Since conjugate gradient method and preconditioner contain mainly matrix-vector and matrix-matrix multiplications, convincing results are obtained in terms of both speed and scalability.
A parallel implementation for linear set of equations of the form Ax = b is presented in this paper. In this implementation, instead of the traditional direct solution of Ax = b, conjugate gradient method is used. The...
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A parallel implementation for linear set of equations of the form Ax = b is presented in this paper. In this implementation, instead of the traditional direct solution of Ax = b, conjugate gradient method is used. The conjugate gradient method is accelerated with an approximate inverse matrix preconditioner obtained from a linear combination of matrix-valued Chebyshev polynomials. This implementation is tested on a Sun SMP machine. Since conjugate gradient method and preconditioner contain mainly matrix-vector and matrix-matrix multiplications, convincing results are obtained in terms of both speed and scalability.
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