The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
Kazuyuki Murase - One of the best experts on this subject based on the ideXlab platform.
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Symmetry Axis BASED OBJECT RECOGNITION UNDER TRANSLATION, ROTATION AND SCALING
International Journal of Neural Systems, 2009Co-Authors: Mashud Hyder, Monirul Islam, M. A. H. Akhand, Kazuyuki MuraseAbstract:This paper presents a new approach, known as Symmetry Axis based feature extraction and recognition (SAFER), for recognizing objects under translation, rotation and scaling. Unlike most previous invariant object recognition (IOR) systems, SAFER puts emphasis on both simplicity and accuracy of the recognition system. To achieve simplicity, it uses simple formulae for extracting invariant features from an object. The scheme used in feature extraction is based on the Axis of Symmetry and angles of concentric circles drawn around the object. SAFER divides the extracted features into a number of groups based on their similarity. To improve the recognition performance, SAFER uses a number of neural networks (NNs) instead of single NN are used for training and recognition of extracted features. The new approach, SAFER, has been tested on two of real world problems i.e., English characters with two different fonts and images of different shapes. The experimental results show that SAFER can produce good recognitio...
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Symmetry Axis based object recognition under translation, rotation and scaling.
International journal of neural systems, 2009Co-Authors: Mashud Hyder, Monirul Islam, M. A. H. Akhand, Kazuyuki MuraseAbstract:This paper presents a new approach, known as Symmetry Axis based feature extraction and recognition (SAFER), for recognizing objects under translation, rotation and scaling. Unlike most previous invariant object recognition (IOR) systems, SAFER puts emphasis on both simplicity and accuracy of the recognition system. To achieve simplicity, it uses simple formulae for extracting invariant features from an object. The scheme used in feature extraction is based on the Axis of Symmetry and angles of concentric circles drawn around the object. SAFER divides the extracted features into a number of groups based on their similarity. To improve the recognition performance, SAFER uses a number of neural networks (NNs) instead of single NN are used for training and recognition of extracted features. The new approach, SAFER, has been tested on two of real world problems i.e., English characters with two different fonts and images of different shapes. The experimental results show that SAFER can produce good recognition performance in comparison with other algorithms.
Alexey Stovas - One of the best experts on this subject based on the ideXlab platform.
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New acoustic approximation for transversely isotropic media with a vertical Symmetry Axis
GEOPHYSICS, 2019Co-Authors: Alexey Stovas, Tariq Alkhalifah, Hitoshi MikadaAbstract:Seismic data processing in the elastic anisotropic model is complicated due to multiparameter dependency. Approximations to the P-wave kinematics are necessary for practical purposes. The acoustic approximation for P-waves in a transversely isotropic medium with a vertical Symmetry Axis (VTI) simplifies the description of wave propagation in elastic media, and as a result, it is widely adopted in seismic data processing and analysis. However, finite-difference implementations of that approximation are plagued with S-wave artifacts. Specifically, the resulting wavefield also includes artificial diamond-shaped S-waves resulting in a redundant signal for many applications that require pure P-wave data. To derive a totally S-wave-free acoustic approximation, we have developed a new acoustic approximation for pure P-waves that is totally free of S-wave artifacts in the homogeneous VTI model. To keep the S-wave velocity equal to zero, we formulate the vertical S-wave velocity to be a function of the model parameters, rather than setting it to zero. Then, the corresponding P-wave phase and group velocities for the new acoustic approximation are derived. For this new acoustic approximation, the kinematics is described by a new eikonal equation for pure P-wave propagation, which defines the new vertical slowness for the P-waves. The corresponding perturbation-based approximation for our new eikonal equation is used to compare the new equation with the original acoustic eikonal. The accuracy of our new P-wave acoustic approximation is tested on numerical examples for homogeneous and multilayered VTI models. We find that the accuracy of our new acoustic approximation is as good as the original one for the phase velocity, group velocity, and the kinematic parameters such as vertical slowness, traveltime, and relative geometric spreading. Therefore, the S-wave-free acoustic approximation could be further applied in seismic processing that requires pure P-wave data.
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S-wave in 2D acoustic transversely isotropic media with a tilted Symmetry Axis
Geophysical Prospecting, 2019Co-Authors: Song Jin, Alexey StovasAbstract:In an acoustic transversely isotropic medium, there are two waves that propagate. One is the P-wave and another one is the S-wave (also known as S-wave artefact). This paper is devoted to analyse the S-wave in two-dimensional acoustic transversely isotropic media with a tilted Symmetry Axis. We derive the S-wave slowness surface and traveltime function in a homogeneous acoustic transversely isotropic medium with a tilted Symmetry Axis. The S-wave traveltime approximations in acoustic transversely isotropic media with a tilted Symmetry Axis can be mapped from the counterparts for acoustic transversely isotropic media with a vertical Symmetry Axis. We consider a layered two-dimensional acoustic transversely isotropic medium with a tilted Symmetry Axis to analyse the S-wave moveout. We also illustrate the behaviour of the moveout for reflected S-wave and converted waves.
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Comparisons of moveout approximations and inversions for P- and S-waves in acoustic transversely isotropic media with a vertical Symmetry Axis
Journal of Applied Geophysics, 2019Co-Authors: Song Jin, Alexey StovasAbstract:Abstract Acoustic transversely isotropic medium with a vertical Symmetry Axis (acoustic VTI) is a very practical anisotropic model for P-wave kinematic analysis. However, apart from P-wave, there is also one additional wave that propagates in this medium, widely known as S-wave artifact. This S-wave possesses bizarre properties and is thoroughly different from the conventional shear waves in elastic VTI media. This paper is devoted to the analysis of S-wave artifact moveout approximations and the Dix-type inversion in acoustic VTI media. We consider four different traveltime approximations originally developed for P-wave and apply them for S-wave moveout. The accuracies of P- and S-waves approximations are numerically compared for both homogeneous and multi-layered acoustic VTI models. With an additional reference ray, the generalized moveout approximation performs the best for S-wave traveltime for a large range of offsets. From the effective parameters estimated for P- and S-waves moveout approximations, we also estimate the interval layer parameters from corresponding Dix-type equations developed for P- and S-waves, respectively. The accuracy and feasibility of the Dix-type inversions are demonstrated in a multi-layered model test.
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generalized moveout approximation for p sv converted waves in vertically inhomogeneous transversely isotropic media with a vertical Symmetry Axis
Geophysical Prospecting, 2016Co-Authors: Qi Hao, Alexey StovasAbstract:We present an overall description of moveout formulas of P–SV converted waves in vertically inhomogeneous transversely isotropic media with a vertical Symmetry Axis by using the generalized moveout approximation. The term “generalized” means that this approximation can be reduced to some existing approximations by specific selections of parameters, which provides flexibility in application depending on objectives. The generalized moveout approximation is separately expressed in the phase and group domains. All five parameters of the group domain (or phase domain) generalized moveout approximation are determined using the zero offset (or horizontal slowness) ray and an additional nonzero offset (or horizontal slowness) ray. We discuss the selection of parameters to link the generalized moveout approximation to some existing approximations. The approximations presented are tested on homogeneous, factorized, and layered transversely isotropic models. The results illustrate that utilizing an additional reference ray significantly improves the accuracy of phase-domain and group-domain moveout approximations for a large range of horizontal slownesses and source–receiver offsets.
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Generalized moveout approximation for P–SV converted waves in vertically inhomogeneous transversely isotropic media with a vertical Symmetry Axis
Geophysical Prospecting, 2015Co-Authors: Qi Hao, Alexey StovasAbstract:We present an overall description of moveout formulas of P–SV converted waves in vertically inhomogeneous transversely isotropic media with a vertical Symmetry Axis by using the generalized moveout approximation. The term “generalized” means that this approximation can be reduced to some existing approximations by specific selections of parameters, which provides flexibility in application depending on objectives. The generalized moveout approximation is separately expressed in the phase and group domains. All five parameters of the group domain (or phase domain) generalized moveout approximation are determined using the zero offset (or horizontal slowness) ray and an additional nonzero offset (or horizontal slowness) ray. We discuss the selection of parameters to link the generalized moveout approximation to some existing approximations. The approximations presented are tested on homogeneous, factorized, and layered transversely isotropic models. The results illustrate that utilizing an additional reference ray significantly improves the accuracy of phase-domain and group-domain moveout approximations for a large range of horizontal slownesses and source–receiver offsets.
Mashud Hyder - One of the best experts on this subject based on the ideXlab platform.
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Symmetry Axis BASED OBJECT RECOGNITION UNDER TRANSLATION, ROTATION AND SCALING
International Journal of Neural Systems, 2009Co-Authors: Mashud Hyder, Monirul Islam, M. A. H. Akhand, Kazuyuki MuraseAbstract:This paper presents a new approach, known as Symmetry Axis based feature extraction and recognition (SAFER), for recognizing objects under translation, rotation and scaling. Unlike most previous invariant object recognition (IOR) systems, SAFER puts emphasis on both simplicity and accuracy of the recognition system. To achieve simplicity, it uses simple formulae for extracting invariant features from an object. The scheme used in feature extraction is based on the Axis of Symmetry and angles of concentric circles drawn around the object. SAFER divides the extracted features into a number of groups based on their similarity. To improve the recognition performance, SAFER uses a number of neural networks (NNs) instead of single NN are used for training and recognition of extracted features. The new approach, SAFER, has been tested on two of real world problems i.e., English characters with two different fonts and images of different shapes. The experimental results show that SAFER can produce good recognitio...
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Symmetry Axis based object recognition under translation, rotation and scaling.
International journal of neural systems, 2009Co-Authors: Mashud Hyder, Monirul Islam, M. A. H. Akhand, Kazuyuki MuraseAbstract:This paper presents a new approach, known as Symmetry Axis based feature extraction and recognition (SAFER), for recognizing objects under translation, rotation and scaling. Unlike most previous invariant object recognition (IOR) systems, SAFER puts emphasis on both simplicity and accuracy of the recognition system. To achieve simplicity, it uses simple formulae for extracting invariant features from an object. The scheme used in feature extraction is based on the Axis of Symmetry and angles of concentric circles drawn around the object. SAFER divides the extracted features into a number of groups based on their similarity. To improve the recognition performance, SAFER uses a number of neural networks (NNs) instead of single NN are used for training and recognition of extracted features. The new approach, SAFER, has been tested on two of real world problems i.e., English characters with two different fonts and images of different shapes. The experimental results show that SAFER can produce good recognition performance in comparison with other algorithms.
Monirul Islam - One of the best experts on this subject based on the ideXlab platform.
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Symmetry Axis BASED OBJECT RECOGNITION UNDER TRANSLATION, ROTATION AND SCALING
International Journal of Neural Systems, 2009Co-Authors: Mashud Hyder, Monirul Islam, M. A. H. Akhand, Kazuyuki MuraseAbstract:This paper presents a new approach, known as Symmetry Axis based feature extraction and recognition (SAFER), for recognizing objects under translation, rotation and scaling. Unlike most previous invariant object recognition (IOR) systems, SAFER puts emphasis on both simplicity and accuracy of the recognition system. To achieve simplicity, it uses simple formulae for extracting invariant features from an object. The scheme used in feature extraction is based on the Axis of Symmetry and angles of concentric circles drawn around the object. SAFER divides the extracted features into a number of groups based on their similarity. To improve the recognition performance, SAFER uses a number of neural networks (NNs) instead of single NN are used for training and recognition of extracted features. The new approach, SAFER, has been tested on two of real world problems i.e., English characters with two different fonts and images of different shapes. The experimental results show that SAFER can produce good recognitio...
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Symmetry Axis based object recognition under translation, rotation and scaling.
International journal of neural systems, 2009Co-Authors: Mashud Hyder, Monirul Islam, M. A. H. Akhand, Kazuyuki MuraseAbstract:This paper presents a new approach, known as Symmetry Axis based feature extraction and recognition (SAFER), for recognizing objects under translation, rotation and scaling. Unlike most previous invariant object recognition (IOR) systems, SAFER puts emphasis on both simplicity and accuracy of the recognition system. To achieve simplicity, it uses simple formulae for extracting invariant features from an object. The scheme used in feature extraction is based on the Axis of Symmetry and angles of concentric circles drawn around the object. SAFER divides the extracted features into a number of groups based on their similarity. To improve the recognition performance, SAFER uses a number of neural networks (NNs) instead of single NN are used for training and recognition of extracted features. The new approach, SAFER, has been tested on two of real world problems i.e., English characters with two different fonts and images of different shapes. The experimental results show that SAFER can produce good recognition performance in comparison with other algorithms.
M. A. H. Akhand - One of the best experts on this subject based on the ideXlab platform.
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Symmetry Axis BASED OBJECT RECOGNITION UNDER TRANSLATION, ROTATION AND SCALING
International Journal of Neural Systems, 2009Co-Authors: Mashud Hyder, Monirul Islam, M. A. H. Akhand, Kazuyuki MuraseAbstract:This paper presents a new approach, known as Symmetry Axis based feature extraction and recognition (SAFER), for recognizing objects under translation, rotation and scaling. Unlike most previous invariant object recognition (IOR) systems, SAFER puts emphasis on both simplicity and accuracy of the recognition system. To achieve simplicity, it uses simple formulae for extracting invariant features from an object. The scheme used in feature extraction is based on the Axis of Symmetry and angles of concentric circles drawn around the object. SAFER divides the extracted features into a number of groups based on their similarity. To improve the recognition performance, SAFER uses a number of neural networks (NNs) instead of single NN are used for training and recognition of extracted features. The new approach, SAFER, has been tested on two of real world problems i.e., English characters with two different fonts and images of different shapes. The experimental results show that SAFER can produce good recognitio...
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Symmetry Axis based object recognition under translation, rotation and scaling.
International journal of neural systems, 2009Co-Authors: Mashud Hyder, Monirul Islam, M. A. H. Akhand, Kazuyuki MuraseAbstract:This paper presents a new approach, known as Symmetry Axis based feature extraction and recognition (SAFER), for recognizing objects under translation, rotation and scaling. Unlike most previous invariant object recognition (IOR) systems, SAFER puts emphasis on both simplicity and accuracy of the recognition system. To achieve simplicity, it uses simple formulae for extracting invariant features from an object. The scheme used in feature extraction is based on the Axis of Symmetry and angles of concentric circles drawn around the object. SAFER divides the extracted features into a number of groups based on their similarity. To improve the recognition performance, SAFER uses a number of neural networks (NNs) instead of single NN are used for training and recognition of extracted features. The new approach, SAFER, has been tested on two of real world problems i.e., English characters with two different fonts and images of different shapes. The experimental results show that SAFER can produce good recognition performance in comparison with other algorithms.
