• Journal of Korea Multimedia Society
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• v.20 no.4
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• pp.606-613
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• 2017

When the planar area is captured by the depth camera, the shape of the plane in the captured image has perspective projection distortion according to the position of the camera. We can correct the distorted image by the depth information in the plane in the captured area. Previous depth information based perspective distortion correction methods fail to satisfy the real-time property due to a large amount of computation. In this paper, we propose the method of applying the conversion table selectively by measuring the motion of the plane and performing the correction process by parallel processing for correcting perspective projection distortion. By appling the proposed method, the system for correcting perspective projection distortion correct the distorted image, whose resolution is 640x480, as 22.52ms per frame, so the proposed system satisfies the real-time property.

• Journal of Integrative Natural Science
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• v.10 no.1
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• pp.33-39
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• 2017

Consider the problem of estimating a $p{\times}1$ mean vector ${\theta}(p-q{\geq}3)$, $q=rank(P_V)$ with a projection matrix $P_v$ under the quadratic loss, based on a sample $X_1$, $X_2$, ${\cdots}$, $X_n$. We find a James-Stein type decision rule which shrinks towards projection vector when the underlying distribution is that of a variance mixture of normals and when the norm ${\parallel}{\theta}-P_V{\theta}{\parallel}$ is restricted to a known interval, where $P_V$ is an idempotent and projection matrix and rank $(P_V)=q$. In this case, we characterize a minimal complete class within the class of James-Stein type decision rules. We also characterize the subclass of James-Stein type decision rules that dominate the sample mean.

• Journal of the Korean Society of Radiology
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• v.8 no.6
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• pp.357-364
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• 2014

The filtered back projection in the image reconstruction algorithms for the clinic computed tomography system has been widely used. Noise of the reconstructed image was examined under the input noise for parallel and fan beam geometries. The reconstruction images of $512{\times}512$ size were carried out under 360 and 720 projection by the Visual C++ for parallel beam and fan beam, respectively, and those agreed with the original Shepp-Logan head phantom very much. Noise was generated because of intrinsic restriction (finite number of projections) for the image reconstruction algorithm, filtered back projection, when no input noise was applied. Because the result noise was rapidly increased under 0.5% input noise ratio, technologies for reducing noise in CT system and image processing is important.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.727-730
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• 1998

In this paper, we propose an implementation of an efficient parallel Radon transform on TMS320C80-based system. For an N$\times$N SAR image, we can obtain O(NM/p) of the conventional parallel Radon transform, by representing the projection patterns in Radon space variables instead of the image space variables, and pipelining the algorithm, where p is the number of processors and M is the number of projection angles. Also, we can reduce the time for the dynamic load distribution among the nodes and the communication overheads of accessing the global memories, by pipelining the memory and processing operations by using tripple buffer structure. Experimental results show an efficient parallel Radon transform of speedup Sp=3.9 and efficiency E=97.5% for 256$\times$256 image, when implemented on TMS320C80 composed of four parallel slave processors with three memory blocks.

• Honam Mathematical Journal
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• v.31 no.2
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• pp.259-265
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• 2009

Let $\mathcal{L}$ be a subspace lattice on a Hilbert space $\mathcal{H}$. Let x and y be vectors in $\mathcal{H}$ and let $P_x$ be the projection onto sp(x). If $P_xE$ = $EP_x$ for each E ${\in}\;\mathcal{L}$, then the following are equivalent. (1) There exists an operator A in Alg$\mathcal{L}$ such that Ax = y, Af = 0 for all f in $sp(x)^{\perp}$ and A ${\geq}$ 0. (2) sup ${\frac{{\parallel}E^{\perp}y{\parallel}}{{\parallel}E^{\perp}x{\parallel}}:E{\in}\mathcal{L}}$ < ${\infty}$ < x, y > ${\geq}$ 0. Let X and Y be operators in $\mathcal{B}(\mathcal{H})$. Let P be the projection onto $\overline{rangeX}$. If PE = EP for each E ${\in}\;\mathcal{L}$, then the following are equivalent: (1) sup ${\frac{{\parallel}E^{\perp}Yf{\parallel}}{{\parallel}E^{\perp}Xf{\parallel}}:f{\in}\mathcal{H},E{\in}\mathcal{L}}$ < ${\infty}$ and < Xf, Yf > ${\geq}$ 0 for all f in H. (2) There exists a positive operator A in Alg$\mathcal{L}$ such that AX = Y.

• Journal of Integrative Natural Science
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• v.11 no.3
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• pp.154-160
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• 2018

Consider the problem of estimating a $p{\times}1$ mean vector ${\theta}(p-r{\geq}3)$, r = rank(K) with a projection matrix K under the quadratic loss, based on a sample $Y_1$, $Y_2$, ${\cdots}$, $Y_n$. In this paper a James-Stein type estimator with shrinkage form is given when it's variance distribution is specified and when the norm ${\parallel}{\theta}-K{\theta}{\parallel}$ is constrain, where K is an idempotent and symmetric matrix and rank(K) = r. It is characterized a minimal complete class of James-Stein type estimators in this case. And the subclass of James-Stein type estimators that dominate the sample mean is derived.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.435-442
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• 2017

Consider the problem of estimating a $p{\times}1$ mean vector ${\theta}$ (p ${\geq}$ 3) under the quadratic loss from multi-variate normal population. We find a James-Stein type estimator which shrinks towards the projection vectors when the underlying distribution is that of a variance mixture of normals. In this case, the norm ${\parallel}{\theta}-K{\theta}{\parallel}$ is known where K is a projection vector with rank(K) = q. The class of this type estimator is quite general to include the class of the estimators proposed by Merchand and Giri (1993). We can derive the class and obtain the optimal type estimator. Also, this research can be applied to the simple and multiple regression model in the case of rank(K) ${\geq}2$.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.3
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• pp.358-379
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• 2010

Multiple object association is an important capability in visual surveillance system with multiple cameras. In this paper, we introduce locally initiating line-based object association with the parallel projection camera model, which can be applicable to the situation without the common (ground) plane. The parallel projection camera model supports the camera movement (i.e. panning, tilting and zooming) by using the simple table based compensation for non-ideal camera parameters. We propose the threshold distance based homographic line generation algorithm. This takes account of uncertain parameters such as transformation error, height uncertainty of objects and synchronization issue between cameras. Thus, the proposed algorithm associates multiple objects on demand in the surveillance system where the camera movement dynamically changes. We verify the proposed method with actual image frames. Finally, we discuss the strategy to improve the association performance by using the temporal and spatial redundancy.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.125-128
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• 2002

This paper propose a rectification technique by applying the Projection matrices derived from perspective projection matrices estimated from self-calibrated stereo image pairs. The derivation is made such that two epipolar lines are in parallel. Rectified images are generated by reprojecting corresponding image points. For the performance analysis of this technique, vertical coordinates of rectified points are compare to those obtained by the technique[3].

• The Journal of the Korea Contents Association
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• v.13 no.6
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• pp.48-54
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• 2013

In these days, the state-of-art technologies employ the heterogeneous parallelization of CPU and GPU for fluid simulations in the field of computer graphics. In this paper, we present a novel CPU-GPU parallel algorithm that solves projection step of fluid simulation more efficiently than existing sequential CPU-GPU processing. Fluid simulation that requires high computational resources can be carried out efficiently by the proposed method.