• Proceedings of the KOSOMBE Conference
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• 대한의용생체공학회 1997년도 추계학술대회
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• pp.142-145
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• 1997

This paper presents the development of depth extraction algorithm or the 3D Endoscopic Data using a stereo matching method and depth calculation. The purpose of other's algorithms is to reconstruct 3D object surface and make depth map, but a one of this paper is to measure exact depth information on the base of [cm] from camera to object. For this, we carried out camera calibration.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 한국방송∙미디어공학회 2022년도 하계학술대회
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• pp.1207-1210
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• 2022

2D 이미지로부터 카메라의 위치 정보를 추정할 수 있는 Structure-from-Motion (SfM) 기술과 dense depth map 을 추정하는 Multi-view Stereo (MVS) 기술을 이용하여 2D 이미지에서 point cloud 와 같은 3D data 를 얻을 수 있다. 3D data 는 VR, AR, 메타버스와 같은 컨텐츠에 사용되기 위한 핵심 요소이다. Point cloud 는 보통 VR, AR, 메타버스와 같은 많은 분야에 이용되기 위해 mesh 형태로 변환된 후 texture 를 입히는 Texturing 과정이 필요하다. 기존의 Texturing 방법에서는 mesh의 face에 사용될 image의 outlier를 제거하기 위해 color 정보만을 이용했다. Color 정보를 이용하는 방법은 mesh 의 face 에 대응되는 image 의 수가 충분히 많고 움직이는 물체에 대한 outlier 에는 효과적이지만 image 의 수가 부족한 경우와 부정확한 카메라 파라미터에 대한 outlier 에는 부족한 성능을 보인다. 본 논문에서는 Texturing 과정의 view selection 에서 depth 정보를 추가로 이용하여 기존 방법의 단점을 보완할 수 있는 방법을 제안한다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 한국정밀공학회 2011년도 춘계학술대회 논문집
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• pp.101-102
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• 2011

• JSTS:Journal of Semiconductor Technology and Science
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• 제3권4호
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• pp.175-180
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• 2003

In this paper, we present a new method for early depth test for a 3D rendering engine. We add a filter stage to the rasterizer in the 3D rendering engine, in an attempt to identify and avoid the occluded pixels. This filtering block determines if a pixel is hidden by a certain plane. If a pixel is hidden by the plane, it can be removed. The simulation results show that the filter reduces the number of pixels to the next stage up to 71.7%. As a result, 67% of memory bandwidth is saved with simple extra hardware.

• Journal of the Korea Institute of Information and Communication Engineering
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• 제20권1호
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• pp.167-174
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• 2016

In this paper, we propose a generation of 2D elemental images for 3D objects using Kinect in 3D depth-priority integral imaging (DPII) display. First, we analyze a principle to pickup elemental images based on ray optics. Based on our analysis, elemental images are generated with both RGB image and depth image recorded from Kinect. We reconstruct 3D images from the elemental images with the computational integral imaging reconstruction technique and then compare various perspective images. To show the usefulness of the proposed method, we carried out the preliminary experiments. The experimental results reveal that our method can provide correct 3D images with full parallax.

• Journal of the Institute of Electronics Engineers of Korea SC
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• 제49권1호
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• pp.1-7
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• 2012

This paper presents a method for 3D image construction using the hybrid (color and depth) camera system, in which the drawbacks of each camera can be compensated for. Prior to an image generation, intrinsic parameters and extrinsic parameters of each camera are extracted through experiments. The geometry between two cameras is established with theses parameters so as to match the color and depth images. After the preprocessing step, the relation between depth information and distance is derived experimentally as a simple linear function, and 3D image is constructed by coordinate transformations of the matched images. The present scheme has been realized using the Microsoft hybrid camera system named Kinect, and experimental results of 3D image and the distance measurements are given to evaluate the method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2012년도 춘계학술대회
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• pp.162-163
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• 2012

In this paper, we propose a synthesis method of elemental images from Kinect images for 3D integral imaging display. Since RGB images and depth image obtained from Kinect are not able to display 3D images in integral imaging system, we need transform the elemental images in integral imaging display. To do so, we synthesize the elemental images based on the geometric optics mapping from the depth plane images obtained from RGB image and depth image. To show the usefulness of the proposed system, we carry out the preliminary experiments using the two person object and present the experimental results.

• Magazine of the Korean Society of Agricultural Engineers
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• 제20권1호
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• pp.4592-4598
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• 1978

The purpose of this research is to find out mathemetically an economical intake water depth in the design of head works through the derivation of some formulas. For the performance of the purpose the following formulas were found out for the design intake water depth in each flow type of intake sluice, such as overflow type and orifice type. (1) The conditional equations of !he economical intake water depth in .case that weir body is placed on permeable soil layer ; (a) in the overflow type of intake sluice, {{{{ { zp}_{1 } { Lh}_{1 }+ { 1} over {2 } { Cp}_{3 }L(0.67 SQRT { q} -0.61) { ( { d}_{0 }+ { h}_{1 }+ { h}_{0 } )}^{- { 1} over {2 } }- { { { 3Q}_{1 } { p}_{5 } { h}_{1 } }^{- { 5} over {2 } } } over { { 2m}_{1 }(1-s) SQRT { 2gs} }+[ LEFT { b+ { 4C TIMES { 0.61}^{2 } } over {3(r-1) }+z( { d}_{0 }+ { h}_{0 } ) RIGHT } { p}_{1 }L+(1+ SQRT { 1+ { z}^{2 } } ) { p}_{2 }L+ { dcp}_{3 }L+ { nkp}_{5 }+( { 2z}_{0 }+m )(1-s) { L}_{d } { p}_{7 } ] =0}}}} (b) in the orifice type of intake sluice, {{{{ { zp}_{1 } { Lh}_{1 }+ { 1} over {2 } C { p}_{3 }L(0.67 SQRT { q} -0.61)}}}} {{{{ { ({d }_{0 }+ { h}_{1 }+ { h}_{0 } )}^{ - { 1} over {2 } }- { { 3Q}_{1 } { p}_{ 6} { { h}_{1 } }^{- { 5} over {2 } } } over { { 2m}_{ 2}m' SQRT { 2gs} }+[ LEFT { b+ { 4C TIMES { 0.61}^{2 } } over {3(r-1) }+z( { d}_{0 }+ { h}_{0 } ) RIGHT } { p}_{1 }L }}}} {{{{+(1+ SQRT { 1+ { z}^{2 } } ) { p}_{2 } L+dC { p}_{4 }L+(2 { z}_{0 }+m )(1-s) { L}_{d } { p}_{7 }]=0 }}}} where, z=outer slope of weir body (value of cotangent), h1=intake water depth (m), L=total length of weir (m), C=Bligh's creep ratio, q=flood discharge overflowing weir crest per unit length of weir (m3/sec/m), d0=average height to intake sill elevation in weir (m), h0=freeboard of weir (m), Q1=design irrigation requirements (m3/sec), m1=coefficient of head loss (0.9∼0.95) s=(h1-h2)/h1, h2=flow water depth outside intake sluice gate (m), b=width of weir crest (m), r=specific weight of weir materials, d=depth of cutting along seepage length under the weir (m), n=number of side contraction, k=coefficient of side contraction loss (0.02∼0.04), m2=coefficient of discharge (0.7∼0.9) m'=h0/h1, h0=open height of gate (m), p1 and p4=unit price of weir body and of excavation of weir site, respectively (won/㎥), p2 and p3=unit price of construction form and of revetment for protection of downstream riverbed, respectively (won/㎡), p5 and p6=average cost per unit width of intake sluice including cost of intake canal having the same one as width of the sluice in case of overflow type and orifice type respectively (won/m), zo : inner slope of section area in intake canal from its beginning point to its changing point to ordinary flow section, m: coefficient concerning the mean width of intak canal site,a : freeboard of intake canal. (2) The conditional equations of the economical intake water depth in case that weir body is built on the foundation of rock bed ; (a) in the overflow type of intake sluice, {{{{ { zp}_{1 } { Lh}_{1 }- { { { 3Q}_{1 } { p}_{5 } { h}_{1 } }^{- {5 } over {2 } } } over { { 2m}_{1 }(1-s) SQRT { 2gs} }+[ LEFT { b+z( { d}_{0 }+ { h}_{0 } )RIGHT } { p}_{1 }L+(1+ SQRT { 1+ { z}^{2 } } ) { p}_{2 }L+ { nkp}_{5 }}}}} {{{{+( { 2z}_{0 }+m )(1-s) { L}_{d } { p}_{7 } ]=0 }}}} (b) in the orifice type of intake sluice, {{{{ { zp}_{1 } { Lh}_{1 }- { { { 3Q}_{1 } { p}_{6 } { h}_{1 } }^{- {5 } over {2 } } } over { { 2m}_{2 }m' SQRT { 2gs} }+[ LEFT { b+z( { d}_{0 }+ { h}_{0 } )RIGHT } { p}_{1 }L+(1+ SQRT { 1+ { z}^{2 } } ) { p}_{2 }L}}}} {{{{+( { 2z}_{0 }+m )(1-s) { L}_{d } { p}_{7 } ]=0}}}} The construction cost of weir cut-off and revetment on outside slope of leeve, and the damages suffered from inundation in upstream area were not included in the process of deriving the above conditional equations, but it is true that magnitude of intake water depth influences somewhat on the cost and damages. Therefore, in applying the above equations the fact that should not be over looked is that the design value of intake water depth to be adopted should not be more largely determined than the value of h1 satisfying the above formulas.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권5호
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• pp.1690-1710
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• 2014

In this paper, we propose the rapid implementation of a 3-dimensional (3D) facial reconstruction from a single frontal face image and introduce a design for its application on a mobile device. The proposed system can effectively reconstruct human faces in 3D using an approach robust to lighting conditions, and a fast method based on a Canonical Correlation Analysis (CCA) algorithm to estimate the depth. The reconstruction system is built by first creating 3D facial mapping from a personal identity vector of a face image. This mapping is then applied to real-world images captured with a built-in camera on a mobile device to form the corresponding 3D depth information. Finally, the facial texture from the face image is extracted and added to the reconstruction results. Experiments with an Android phone show that the implementation of this system as an Android application performs well. The advantage of the proposed method is an easy 3D reconstruction of almost all facial images captured in the real world with a fast computation. This has been clearly demonstrated in the Android application, which requires only a short time to reconstruct the 3D depth map.

• Journal of KIISE:Software and Applications
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• 제29권11호
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• pp.818-825
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• 2002

Depth information is one of the most important factor for the recognition of a digital face image. Range images are very useful, when comparing one face with other faces, because of implicating depth information. As the processing for the whole fare produces a lot of calculations and data, face images ran be represented in terms of a vector of feature descriptors for a local area. In this paper, depth areas of a 3 dimensional(3D) face image were extracted by the contour line from some depth value. These were resampled and stored in consecutive location in feature vector using multiple feature method. A comparison between two faces was made based on their distance in the feature space, using Euclidian distance. This paper reduced the number of index data in the database and used fewer feature vectors than other methods. Proposed algorithm can be highly recognized for using local depth information and less feature vectors or the face.