• Journal of KIISE:Software and Applications
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• v.36 no.11
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• pp.947-954
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• 2009

This paper proposes a novel method that reconstructs a surfel-based object by using visual hull from multiple images. The surfel is a point primitive that effectively approximates point-set surface. We create the surfel representation of an object from images by combining the LDC(Layered Depth Cube) surfel sampling with the concept of visual hull that represents the approximated shape from input images. Because the surfel representation requires relatively smaller memory resources than the polygonal one and its LDC resolution is freely changed, we can control the reconstruction quality of the target object and acquire the maximal quality on the given memory resource.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.113-120
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• 2020

The multi-view video is a collection of multiple videos capturing the same scene at different viewpoints. Thus, there is an advantage of providing for user oriented view pointed video. This paper is suggested that the compression performance of layered depth image structure expression has improved by using more improved method. We confirm the data size of layer depth image by encoding H.264 technology and the each performances of reconstructed images. The H.264/AVC technology has easily extended for H.264 technology of video contents. In this paper, we suggested that layered depth structure can be applied for an efficient new image contents. We show that the huge data size of multi-view video image is decreased, and the higher performance of image is provided, and there is an advantage of for stressing error restoring.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.375-378
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• 2005

The multi-view video is a collection of multiple videos capturing the same scene at different viewpoints. The multi-view video can be used in various applications, including free viewpoint TV and three-dimensional TV. Since the data size of the multi-view video linearly increases as the number of cameras, it is necessary to compress multi-view video data for efficient storage and transmission. The multi-view video can be coded using the concept of the layered depth image (LDI). In this paper, we describe a procedure to generate LDI from the natural multi-view video and present a method to encode multi-view video using the concept of LDI.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.5
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• pp.1155-1163
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• 2011

In this paper, we propose a new pre-processing algorithm applied to depth map to improve the coding efficiency. Now, 3DV/FTV group in the MPEG is working for standard of 3DVC(3D video coding), but compression method for depth map images are not confirmed yet. In the proposed algorithm, after dividing the histogram distribution of a given depth map by EM clustering method based on GMM, we classify the depth map into several layered images. Then, we apply different mean shift filter to each classified image according to the existence of background or foreground in it. In other words, we try to maximize the coding efficiency while keeping the boundary of each object and taking average operation toward inner field of the boundary. The experiments are performed with many test images and the results show that the proposed algorithm achieves bits reduction of 19% ~ 20% and computation time is also reduced.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.481-483
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• 2000

영상 기반 모델링 및 렌더링을 위해 제안된 LDI(Layered Depth Images) 기법은 여러 장의 2차원 영상과 깊이 정보, 카메라 정보를 입력으로 받아 3차원 와핑을 이용해 새로운 장면을 렌더링한다. 하지만 이 기법은 홀 발생 문제 등 몇가지 결함을 가지고 있다. 본 논문은 이러한 LDI의 문제를 해결하고자, 의료 영상 가시화 분야에서 널리 사용되는 쉬어-왑 렌더링 알고리즘을 사용한 결과를 설명한다. 한편, 본 논문에서 제안된 알고리즘은 적은 데이터를 필요로 하는데, 웹 상에서 오브젝트 플레이어 플러그인으로 개발한 결과 좋은 성능을 보였다.

• The Journal of the Acoustical Society of Korea
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• v.15 no.4E
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• pp.9-13
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• 1996

We developed an iterative algorithm which could improve the resolution of reconstructed tomograms having random attenuation patterns and analyzed the limitation of this algorithm. The simple back-and forth propagation algorithm has depth resolution about four wavelengths. An iterative algorithm, based on back-and-forth propagation, can be used to improve the resolution of reconstructed tomograms. We analyzed the wavefield for multi-layered specimen and programmed iterative algorithm using Clanguage. Simulation results show that the images get clearer as the number of iterations increases. Also, unambiguous images can be reconstructed using this algorithm even when the layer separation is only two wavelengths. However, this iteration algorithm comes up with an incorrect solution for the number of projections less than five.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.1
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• pp.13-20
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• 2016

In this paper, we propose an object recognition system that can effectively find out its category, its instance name, and several attributes from the color and depth images of an object with hierarchical feature learning. In the preprocessing stage, our system transforms the depth images of the object into the surface normal vectors, which can represent the shape information of the object more precisely. In the feature learning stage, it extracts a set of patch features and image features from a pair of the color image and the surface normal vector through two-layered learning. And then the system trains a set of independent classification models with a set of labeled feature vectors and the SVM learning algorithm. Through experiments with UW RGB-D Object Dataset, we verify the performance of the proposed object recognition system.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.3 s.309
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• pp.30-38
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• 2006

In this paper, we improved to drops recognition rate according to distance using distance and depth information with 3D from stereo face images. A monocular face image has problem to drops recognition rate by uncertainty information such as distance of an object, size, moving, rotation, and depth. Also, if image information was not acquired such as rotation, illumination, and pose change for recognition, it has a very many fault. So, we wish to solve such problem. Proposed method consists of an eyes detection algorithm, analysis a pose of face, md principal component analysis (PCA). We also convert the YCbCr space from the RGB for detect with fast face in a limited region. We create multi-layered relative intensity map in face candidate region and decide whether it is face from facial geometry. It can acquire the depth information of distance, eyes, and mouth in stereo face images. Proposed method detects face according to scale, moving, and rotation by using distance and depth. We train by using PCA the detected left face and estimated direction difference. Simulation results with face recognition rate of 95.83% (100cm) in the front and 98.3% with the pose change were obtained successfully. Therefore, proposed method can be used to obtain high recognition rate with an appropriate scaling and pose change according to the distance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.4
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• pp.935-944
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• 2013

In this paper, a new illumination compensation algorithm by segmentation with depth information is proposed to improve the coding efficiency of multi-view images. In the proposed algorithm, a reference image is first segmented into several layers where each layer is composed of objects with a similar depth value. Then we separate objects from each other even in the same layer by labeling each separate region in the layered image. Then, the labeled reference depth image is converted to the position of the distortion image view by using 3D warping algorithm. Finally, we apply an illumination compensation algorithm to each of matched regions in the converted reference view and distorted view. The occlusion regions that occur by 3D warping are also compensated by a global compensation method. Through experimental results, we are able to confirm that the proposed algorithm has better performance to improve coding efficiency.

• Geophysics and Geophysical Exploration
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• v.20 no.1
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• pp.33-42
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• 2017

Recently, the grounded electrical-source airborne transient electromagnetic (GREATEM) system with high power source was introduced to achieve deeper investigation depth and to overcome high noise level. Although the GREATEM is a transient electromagnetic system using a long grounded wire as the transmitter, GREATEM data have been interpreted with 1D earth models because 2D or 3D modeling and inversion of vast airborne data are complicated and expensive to calculate. Generally, 1D inversion is subsequently applied to every survey point and combining 1D images together forms the stitched conductivity-depth image. However, the stitched models often result in abrupt variations in neighboring models. To overcome this problem, laterally constrained inversion (LCI) has been developed in inversion of ATEM data, which can yield layered sections with lateral smooth transitions. In this study, we analysed the GREATEM data through 1D numerical modeling for a curved grounded wire source. Furthermore, we developed a laterally constrained inversion scheme for continuous GREATEM data based on a layered earth model. All 1D data sets and models are inverted as one system, producing layered sections with lateral smooth transitions. Applying the developed LCI technique to the GREATEM data, it was confirmed that the laterally constrained inversion can provide laterally smooth model sections that reflect the layering of the survey area effectively.