• Proceedings of the KSRS Conference
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• v.1
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• pp.98-101
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• 2006

In general, stereo images are widely used to remote sensing or photogrametric applications for the purpose of image understanding and feature extraction or cognition. However, the most cases of these stereo-based application deal with 2-D satellite images or the airborne photos so that its main targets are generation of small-scaled or large-scaled DEM(Digital Elevation Model) or DSM(Digital Surface Model), in the 2.5-D. Contrast to these previous approaches, the scope of this study is to investigate 3-D stereo processing and visualization of true geo-referenced 3-D features based on anaglyph technique, and the aim is at the prototype development for stereo visualization system of complex typed 3-D GIS features. As for complex typed 3-D features, the various kinds of urban landscape components are taken into account with their geometric characteristics and attributes. The main functions in this prototype are composed of 3-D feature authoring and modeling along with database schema, stereo matching, and volumetric visualization. Using these functions, several technical aspects for migration into actual 3-D GIS application are provided with experiment results. It is concluded that this result will contribute to more specialized and realistic applications by linking 3-D graphics with geo-spatial information.

• Journal of the Korea Society of Computer and Information
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• v.16 no.10
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• pp.83-92
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• 2011

The aim of this study is to evaluate whether 3D nuclear chromatin texture features are significant in recognizing the progression of cervical cancer. In particular, we assessed that our method could detect subtle differences in the chromatin pattern of seemingly normal cells on specimens with malignancy. We extracted nuclear texture features based on 3D GLCM(Gray Level Co occurrence Matrix) and 3D Wavelet transform from 100 cell volume data for each group (Normal, LSIL and HSIL). To evaluate the feasibility of 3D chromatin texture analysis, we compared the correct classification rate for each of the classifiers using them. In addition to this, we compared the correct classification rates for the classifiers using the proposed 3D nuclear texture features and the 2D nuclear texture features which were extracted in the same way. The results showed that the classifier using the 3D nuclear texture features provided better results. This means our method could improve the accuracy and reproducibility of quantification of cervical cell.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.4
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• pp.259-265
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• 2016

In this paper, new method was proposed to classify lung tissues such as Broncho vascular, Emphysema, Ground Glass Reticular, Ground Glass, Honeycomb, Normal for early lung disease detection. 459 Statistical features was extraced from joint histogram matrix based on multi resolution analysis, volumetric LBP, and CT intensity, then dominant features was selected by using adaboost learning. Accuracy of proposed features and 3D AMFM was 90.1% and 85.3%, respectively. Proposed joint histogram based features shows better classification result than 3D AMFM in terms of accuracy, sensitivity, and specificity.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.54-59
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• 1993

This paper presents a method of estimating 3-D surface geometrical features that are necessary for 3-D object recognition and image interpretation. The features, such as surface needle maps and curvatures, are computed from range or intensity images. In general, the range and intensity images are prone to noises, and hence the features computed by differentiation calculi on such a noisy image are hardly applicable to industrial recognition tasks. In our approach, we try to obtain a more accurate estimate of the features by using a least-squares minimization procedure subject to local curvature consistency constraints. The algorithm is robust with respect to noises and is completely independent of the viewpoint at which the image is taken. The performance of the ajgoritlim is evaluated using both synthetic data and real intensity images.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.11
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• pp.53-61
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• 1996

In this paper, efficient techniques for solder joint inspection have been described. Using three layers of ring shaped LED's with different illumination angles, three frames of images are sequentially obtained. From these images the regions of interest (soldered regions) are segmented, and their characteristic features including the average gray level and the percentage of highlights - refereed to as 2D features - are extracted. Based on the backpropagation algorithm of neural networks, each solder joint is classified intor one of the pre-defined types. If the output value is not in the confidence interval, the distribution of tilt angles-referred to as 3D features - is claculated, and the solder joint is classified based on the bayes classfier. The second classifier requires more computation while providing more information and better performance. The proposed inspection system has been implemented and tested with various types of solder joints in SMDs. The experimental results have verified the validity of this scheme in terms of speed and recognition rate.

• The Research Journal of the Costume Culture
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• v.29 no.1
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• pp.65-86
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• 2021

This study aims to develop a hanbok design method in response to recent changes in consumption trends that emphasize new aesthetic and cultural values, which contrast with the existing cultural order and repetitive normative styles in fashion designing. With this in mind, our study explores the main features of deconstruction fashion design as a theoretical guide for developing a methodology for deconstruction hanbok design, on the basis of which new, experimental and creative hanbok design works can be produced. To do this, we first investigate current trends in hanbok design and changing concepts of Korean fashion design through literature review of previous studies. Secondly, we explore deconstructionism and analyze its features to lay down the foundation for a post-modern approach in hanbok design. As the result of analysis, the main features of deconstruction fashion design are summarized as the following: 1) non-finishing, 2) decomposing and recomposing, 3) recycling, 4) transparent, 5) grunge, 6) flattening, and 7) exaggeration. Based on the identified core features of deconstruction fashion design, we develop a creative method of hanbok design in the context of modern Korean fashion design. Finally, we show five design outputs via a 3D digital fashion design process using the CLO3D software program.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.91-99
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• 2018

Sketch-based 3D object retrieval is a convenient way to search for various 3D data using human-drawn sketches as query. In this paper, we propose a new method of using Sketch CNN, Wasserstein CNN and Wasserstein center loss for sketch-based 3D object search. Specifically, Wasserstein center loss is a method of learning the center of each object category and reducing the Wasserstein distance between center and features of the same category. To do this, the proposed 3D object retrieval is performed as follows. Firstly, Wasserstein CNN extracts 2D images taken from various directions of 3D object using CNN, and extracts features of 3D data by computing the Wasserstein barycenters of features of each image. Secondly, the features of the sketch are extracted using a separate Sketch CNN. Finally, we learn the features of the extracted 3D object and the features of the sketch using the proposed Wasserstein center loss. In order to demonstrate the superiority of the proposed method, we evaluated two sets of benchmark data sets, SHREC 13 and SHREC 14, and the proposed method shows better performance in all conventional metrics compared to the state of the art methods.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.177-186
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• 2023

Hyperspectral imaging technology is one of the most efficient and fast-growing technologies in recent years. Hyperspectral image (HSI) comprises contiguous spectral bands for every pixel that is used to detect the object with significant accuracy and details. HSI contains high dimensionality of spectral information which is not easy to classify every pixel. To confront the problem, we propose a novel RGB channel Assimilation for classification methods. The color features are extracted by using chromaticity computation. Additionally, this work discusses the classification of hyperspectral image based on Domain Transform Interpolated Convolution Filter (DTICF) and 3D-CNN with Bi-directional-Long Short Term Memory (Bi-LSTM). There are three steps for the proposed techniques: First, HSI data is converted to RGB images with spatial features. Before using the DTICF, the RGB images of HSI and patch of the input image from raw HSI are integrated. Afterward, the pair features of spectral and spatial are excerpted using DTICF from integrated HSI. Those obtained spatial and spectral features are finally given into the designed 3D-CNN with Bi-LSTM framework. In the second step, the excerpted color features are classified by 2D-CNN. The probabilistic classification map of 3D-CNN-Bi-LSTM, and 2D-CNN are fused. In the last step, additionally, Markov Random Field (MRF) is utilized for improving the fused probabilistic classification map efficiently. Based on the experimental results, two different hyperspectral images prove that novel RGB channel assimilation of DTICF-3D-CNN-Bi-LSTM approach is more important and provides good classification results compared to other classification approaches.

• Bulletin of the Korean Chemical Society
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• v.29 no.3
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• pp.656-662
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• 2008

Antagonists of the d -opioid receptor are effective in overcoming resistance against analgesic drugs such as morphine. To identify novel antagonists of the d -opioid receptor that display high potency and low resistance, we performed 3D-QSAR analysis using chemical feature-based pharmacophore models. Chemical features for d -opioid receptor antagonists were generated using quantitative (Catalyst/HypoGen) and qualitative (Catalyst/HipHop) approaches. For HypoGen analysis, we collected 16 peptide and 16 non-peptide antagonists as the training set. The best-fit pharmacophore hypotheses of the two antagonist models comprised identical features, including a hydrophobic aromatic (HAR), a hydrophobic (HY), and a positive ionizable (PI) function. The training set of the HipHop model was constructed with three launched opioid drugs. The best hypothesis from HipHop included four features: an HAR, an HY, a hydrogen bond donor (HBD), and a PI function. Based on these results, we confirm that HY, HAR and PI features are essential for effective antagonism of the d -opioid receptor, and determine the appropriate pharmacophore to design such antagonists.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.4
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• pp.173-180
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• 2006

A key component of autonomous navigation of intelligent home robot is localization and map building with recognized features from the environment. To validate this, accurate measurement of relative location between robot and features is essential. In this paper, we proposed relative localization algorithm based on 3D reconstruction of scale invariant features of two images which are captured from two parallel cameras. We captured two images from parallel cameras which are attached in front of robot and detect scale invariant features in each image using SIFT(scale invariant feature transform). Then, we performed matching for the two image's feature points and got the relative location using 3D reconstruction for the matched points. Stereo camera needs high precision of two camera's extrinsic and matching pixels in two camera image. Because we used two cameras which are different from stereo camera and scale invariant feature point and it's easy to setup the extrinsic parameter. Furthermore, 3D reconstruction does not need any other sensor. And the results can be simultaneously used by obstacle avoidance, map building and localization. We set 20cm the distance between two camera and capture the 3frames per second. The experimental results show :t6cm maximum error in the range of less than 2m and ${\pm}15cm$ maximum error in the range of between 2m and 4m.