• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.6
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• pp.667-681
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• 2009

We describe a method to decompose a cube with trilinear interpolation into a collection of tetrahedra with linear interpolation, where the isosurface topology is preserved for all isovalues during decomposition. Visualization algorithms that require input scalar data to be defined on a tetrahedral grid can utilize our method to process 3D rectilinear data with topological correctness. As one of many possible examples, we apply the decomposition method to topologically accurate tetrahedral mesh extraction of an interval volume from trilinear volumetric imaging data. The topological correctness of the resulting mesh can be critical for accurate simulation and visualization.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.169-172
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• 2000

Direct volume rendering has yet been restricted to high-end graphic workstations and special-purpose hardware, due to the large amount of trilinear interpolation, that are necessary to obtain high image quality. In this paper, we implemented the volume rendering techniques using the 2D-texture at the environment of standard PC hardware. In addition, we show how multi-texturing capabilities of modern PC graphics board are enable to volume rendering. Besides using extended OpenGL function, we improved pixel operations and rendering capacity.

• Journal of Biomedical Engineering Research
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• v.27 no.1
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• pp.6-13
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• 2006

Ultrasound visualization is a typical diagnosis method to examine organs, soft tissues and fetus data. It is difficult to visualize ultrasound data because the quality of the data might be degraded by artifact and speckle noise, and gathered with non-linear sampling. Rendering speed is too slow since we can not use additional data structures or procedures in rendering stage. In this paper, we use several visualization methods for fast rendering of ultrasound data. First method, denoted as adaptive ray sampling, is to reduce the number of samples by adjusting sampling interval in empty space. Secondly, we use early ray termination scheme with sufficiently wide sampling interval and low threshold value of opacity during color compositing. Lastly, we use bilinear interpolation instead of trilinear interpolation for sampling in transparent region. We conclude that our method reduces the rendering time without loss of image quality in comparison to the conventional methods.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.11
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• pp.927-933
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• 2020

This paper presents a fuel quantity measurement system (FQMS) for an aircraft supplementary fuel tank considering the change of aircraft attitude. The developed FQMS consists of fuel sensors, a signal process unit, an indicator and a software to estimate the fuel quantity from the sensor data. To replicate the change of the roll and pitch attitude on the ground, the test simulator is developed in this work. Using the test simulator, the sensor data at various fuel quantities, roll and pitch angles are automatically measured to build a training data set. The data-driven software to estimate the fuel quantity is then developed using a trilinear interpolation method with the training data set. The developed FQMS is verified by investigating the fuel estimation error of the test data set that we know the true values. Through the test, it is confirmed that the error of the developed FQMS system satisfies the criteria of TSO-C55 document.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.772-773
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• 2015

This paper presents a dynamic model of 2-D magnetostriction in electrical steel sheet (ESS) under rotating flux magnetization conditions and its implementation in finite element method (FEM). For an arbitrary waveform of magnetic flux density (B), the corresponding magnetostriction waveform can be predicted by the model. In order to apply the model to FEM easily, the model is based on trilinear interpolation method. As an example, the model is applied to a three-phase transformer constructed by highly grain-oriented electrical steel sheets and the numerical results by the magnetostriction model are discussed.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.10
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• pp.162-170
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• 2013

In recent years, the interests and needs of the Pedestrian Protection System (PPS), which is mounted on the vehicle for the purpose of traffic safety improvement is increasing. In this paper, we propose a pedestrian candidate window extraction and unit cell histogram based HOG descriptor calculation methods. At pedestrian detection candidate windows extraction stage, the bright ratio of pedestrian and its circumference region, vertical edge projection, edge factor, and PCA reconstruction image are used. Dalal's HOG requires pixel based histogram calculation by Gaussian weights and trilinear interpolation on overlapping blocks, But our method performs Gaussian down-weight and computes histogram on a per-cell basis, and then the histogram is combined with the adjacent cell, so our method can be calculated faster than Dalal's method. Our PCA reconstruction error based pedestrian detection candidate window extraction method efficiently classifies background based on the difference between pedestrian's head and shoulder area. The proposed method improves detection speed compared to the conventional HOG just using image without any prior information from camera calibration or depth map obtained from stereo cameras.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.64-74
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• 2019

Although a non-rigid registration has high demands in clinical practice, it has a high computational complexity and it is very difficult for ensuring the accuracy and robustness of registration. This study proposes a method of applying a non-rigid registration to 3D magnetic resonance images of brain in an unsupervised learning environment by using a deep-learning network. A feature vector between two images is produced through the network by receiving both images from two different patients as inputs and it transforms the target image to match the source image by creating a displacement vector field. The network is designed based on a U-Net shape so that feature vectors that consider all global and local differences between two images can be constructed when performing the registration. As a regularization term is added to a loss function, a transformation result similar to that of a real brain movement can be obtained after the application of trilinear interpolation. This method enables a non-rigid registration with a single-pass deformation by only receiving two arbitrary images as inputs through an unsupervised learning. Therefore, it can perform faster than other non-learning-based registration methods that require iterative optimization processes. Our experiment was performed with 3D magnetic resonance images of 50 human brains, and the measurement result of the dice similarity coefficient confirmed an approximately 16% similarity improvement by using our method after the registration. It also showed a similar performance compared with the non-learning-based method, with about 10,000 times speed increase. The proposed method can be used for non-rigid registration of various kinds of medical image data.