• Journal of Power System Engineering
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• 제20권6호
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• pp.51-57
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• 2016

Experiment is carried out to investigate the mixed convective flow in three-dimensional horizontal rectangular channels filled with high viscous fluid. The particle image velocimetry(PIV) with thermo-sensitive liquid crystal tracers is used for visualizing and analysis. Quantitative data of temperature and velocity are obtained by applying the color-image processing to a visualized image, and neural network is applied to the color-to-temperature calibration. In this study, the fluid used is silicon oil(Pr=909), the aspect ratio(channel width to heigh) is 4 and Reynolds number is $2{\times}10^{-2}$. From the present study, we can visualize the quantitative temperature and velocity of mixed convective flow in three-dimensional horizontal rectangular channels simultaneously.

• Current Optics and Photonics
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• 제4권5호
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• pp.411-420
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• 2020

Non-contact three-dimensional (3D) measuring technology is used to identify defects in miniature products, such as optics, polymers, and semiconductors. Hence, this technology has garnered significant attention in computer vision research. In this paper, we focus on shape from focus (SFF), which is an optical passive method for 3D shape recovery. In existing SFF techniques using interpolation, all datasets of the focus volume are approximated using one model. However, these methods cannot demonstrate how a predefined model fits all image points of an object. Moreover, it is not reasonable to explain various shapes of datasets using one model. Furthermore, if noise is present in the dataset, an error will be generated. Therefore, we propose an algorithm based on polynomial regression analysis to address these disadvantages. Our experimental results indicate that the proposed method is more accurate than existing methods.

• Journal of the Optical Society of Korea
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• 제16권2호
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• pp.115-120
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• 2012

In this paper, we present a method to automatically quantify the three-dimensional (3D) volume of red blood cells (RBCs) using off-axis digital holographic microscopy. The RBCs digital holograms are recorded via a CCD camera using an off-axis interferometry setup. The RBCs' phase image is reconstructed from the recorded off-axis digital hologram by a computational reconstruction algorithm. The watershed segmentation algorithm is applied to the reconstructed phase image to remove background parts and obtain clear targets in the phase image with many single RBCs. After segmenting the reconstructed RBCs' phase image, all single RBCs are extracted, and the 3D volume of each single RBC is then measured with the surface area and the phase values of the corresponding RBC. In order to demonstrate the feasibility of the proposed method to automatically calculate the 3D volume of RBC, two typical shapes of RBCs, i.e., stomatocyte/discocyte, are tested via experiments. Statistical distributions of 3D volume for each class of RBC are generated by using our algorithm. Statistical hypothesis testing is conducted to investigate the difference between the statistical distributions for the two typical shapes of RBCs. Our experimental results illustrate that our study opens the possibility of automated quantitative analysis of 3D volume in various types of RBCs.

• Journal of Korea Multimedia Society
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• 제23권3호
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• pp.468-475
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• 2020

In this paper, we proposed a three-dimensional visualization system for medical images in augmented reality based on deep learning. In the proposed system, the artificial neural network model performed fully automatic segmentation of the region of lung and pulmonary nodule from chest CT images. After applying the three-dimensional volume rendering method to the segmented images, it was visualized in augmented reality devices. As a result of the experiment, when nodules were present in the region of lung, it could be easily distinguished with the naked eye. Also, the location and shape of the lesions were intuitively confirmed. The evaluation was accomplished by comparing automated segmentation results of the test dataset to the manual segmented image. Through the evaluation of the segmentation model, we obtained the region of lung DSC (Dice Similarity Coefficient) of 98.77%, precision of 98.45%, recall of 99.10%. And the region of pulmonary nodule DSC of 91.88%, precision of 93.05%, recall of 90.94%. If this proposed system will be applied in medical fields such as medical practice and medical education, it is expected that it can contribute to custom organ modeling, lesion analysis, and surgical education and training of patients.

• Journal of Fashion Business
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• 제8권6호
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• pp.68-77
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• 2004

Hollow filament yarns provide better warmth to the touch, lighter in weight, increased opacity, and subtle luster compared to the regular synthetic filament yarns. However, luster properties of textile fibers or fabrics are often difficult to characterize, partly due to the fineness of the surface texture, the anisotropic nature of the weave structure, the complexity of the fiber array comprising a yarn, and the fiber structure itself. In this study, the fabric surface luster image was analyzed using image analysis methods after image acquisition. The hollow filament fiber was modeled using a three-dimensional modeling software. It was then ray-traced for comparing the virtual luster images of the hollow fiber and the regular fiber models based on shading models including photon mapping. The luster object size of the actual hollow filament fabric was smaller than that of the regular filament fabric. The shape of the luster object of the hollow filament fabric was dual peak type while that of the regular filament was single.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• 제17권1호
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• pp.11-19
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• 1999

Lately, with the development of the fields of computer and photogrammetry, Digital Photogrammetric Systems are widely used for the generation of GIS basemap, the acquisition of topographic information and DEM, the formation of digital orthophoto, three-dimensional viewing and so on. According as the demand for the systems is rapidly increasing, we suggest keenly the necessity of domestic technical development, because all of these systems depend on foreign technology until now. In this study, by using digital photogrammetry method, with Visual C++ language, we have developed Digital Photogrammetric Systems for Windows which is able to get three-dimensional coordinates through interior orientation, exterior orientation, epipolar line, image matching from a pair of aerial photos taken with metric camera. This system consists of not only a module which can revise digital map that is being made at National Geographic Institute as a part of data construction project of National Geographic Information System, but also a module which can view three-dimensional image on the screen monitor by using anaglyph for three-dimensional analysis. The digital photogrammetry modules developed in this study are expected to be used as primary modules for the effective management of the urban as well as main modules in developing professional digital photogrammetric systems.

• ETRI Journal
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• 제46권2호
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• pp.290-306
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• 2024

To treat the novel COronaVIrus Disease (COVID), comparatively fewer medicines have been approved. Due to the global pandemic status of COVID, several medicines are being developed to treat patients. The modern COVID medicines development process has various challenges, including predicting and detecting hazardous COVID medicine responses. Moreover, correctly predicting harmful COVID medicine reactions is essential for health safety. Significant developments in computational models in medicine development can make it possible to identify adverse COVID medicine reactions. Since the beginning of the COVID pandemic, there has been significant demand for developing COVID medicines. Therefore, this paper presents the transferlearning methodology and a multilabel convolutional neural network for COVID (MLCNN-COV) medicines development model to identify negative responses of COVID medicines. For analysis, a framework is proposed with five multilabel transfer-learning models, namely, MobileNetv2, ResNet50, VGG19, DenseNet201, and Inceptionv3, and an MLCNN-COV model is designed with an image augmentation (IA) technique and validated through experiments on the image of three-dimensional chemical conformer of 17 number of COVID medicines. The RGB color channel is utilized to represent the feature of the image, and image features are extracted by employing the Convolution2D and MaxPooling2D layer. The findings of the current MLCNN-COV are promising, and it can identify individual adverse reactions of medicines, with the accuracy ranging from 88.24% to 100%, which outperformed the transfer-learning model's performance. It shows that three-dimensional conformers adequately identify negative COVID medicine responses.

• Journal of the Korea Institute of Information and Communication Engineering
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• 제16권3호
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• pp.591-597
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• 2012

Medical device performance has been advanced while images are expected to be acquired with further higher quality and pertinent applicability as images have been increasing in importance in analyzing major organs. Recent high frequency of image processing by MATLAB in image analysis area accounts for the intent of this study to segment pulmonary vessels by means of MATLAB. This study is to consist of 3 phases including pulmonary region segmentation, pulmonary vessel segmentation and three dimensional connectivity assessment, in which vessel was segmented, using threshold level, from the pulmonary region segmented, vessel thickness was measured as two dimensional refining process and three dimensional connectivity was assessed as three dimensional refining process. It is expected that MATLAB-based image processing should contribute to diversity and reliability of medical image processing and that the study results may lay a foundation for chest CT images-related researches.

• The Transactions of the Korea Information Processing Society
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• 제3권7호
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• pp.1906-1916
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• 1996

For the medical analysis of the three-dimensional structure such as the mandible, it is necessary to reconstruct the structure into the finite number of analyzable elements. The information of the three-dimensional structure can be obtained from the cross-sections of the magnetic resonance image (MRI). A region corresponding to the structure is extracted from the inner part of the cross- section. By the triangulation of the sampled cross-section image, two-dimensional finite elements are generated. Three-dimensional finite elements are constructed by matching the two dimensional finite elements each other in space. In this paper a construction method of the optimal three-dimensional finite elements has been suggested, which uses the adjacent information abstracted from the triangulated two-dimensional finite elements. The elements are classified into the identical property sets by using the adjacent information of the traingulated two-dimensional elements. After applying the multistep matching algorithm to the classified two-dimensional finite elements, the optimal three-dimensional finite elements can be construccted. By analyzing the constructed finite elements, it is possible to get much more useful medical information about the three-dimensional struture of mandible.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권5호
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• pp.2273-2286
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• 2018

This paper describes transformations between elemental image arrays and a RGBD image for three-dimensional integral imaging and transmitting systems. Two transformations are introduced and analyzed in the proposed method. Normally, a RGBD image is utilized in efficient 3D data transmission although 3D imaging and display is restricted. Thus, a pixel-to-pixel mapping is required to obtain an elemental image array from a RGBD image. However, transformations and their analysis have little attention in computational integral imaging and transmission. Thus, in this paper, we introduce two different mapping methods that are called as the forward and backward mapping methods. Also, two mappings are analyzed and compared in terms of complexity and visual quality. In addition, a special condition, named as the hole-free condition in this paper, is proposed to understand the methods analytically. To verify our analysis, we carry out experiments for test images and the results indicate that the proposed methods and their analysis work in terms of the computational cost and visual quality.