• KIPS Transactions on Software and Data Engineering
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• v.6 no.1
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• pp.23-28
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• 2017

Shape from focus (SFF) is a technique used to reconstruct 3D shape of objects from a sequence of images obtained at different focus settings of the lens. In this paper, a new shape from focus method for 3D reconstruction of microscopic objects is described, which is based on gradient operator in Mathematical Morphology. Conventionally, in SFF methods, a single focus measure is used for measuring the focus quality. Due to the complex shape and texture of microscopic objects, single measure based operators are not sufficient, so we propose morphological operators with multi-structuring elements for computing the focus values. Finally, an optimal focus measure is obtained by combining the response of all focus measures. The experimental results showed that the proposed algorithm has provided more accurate depth maps than the existing methods in terms of three-dimensional shape recovery.

• Journal of Korea Multimedia Society
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• v.16 no.1
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• pp.11-18
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• 2013

This paper presents a SFF(shape from focus) algorithm using a new focus measure based on higher order statistics for the exact depth estimation. Since conventional SFF-based 3D depth reconstruction algorithms used SML(sum of modified Laplacian) as the focus measure, their performance is strongly depended on the image characteristics. These are efficient only for the rich texture and well focused images. Therefore, this paper adopts a new focus measure using HOS(higher order statistics), in order to extract the focus value for relatively poor texture and focused images. The initial best focus area map is generated by the measure. Thereafter, the area refinement, thinning, and corner detection methods are successively applied for the extraction of the locally best focus points. Finally, a 3D model from the carefully selected points is reconstructed by Delaunay triangulation.

• Journal of Broadcast Engineering
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• v.24 no.2
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• pp.281-291
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• 2019

Depth from defocus estimates the 3D depth by using a phenomenon in which the object in the focal plane of the camera forms a clear image but the object away from the focal plane produces a blurred image. In this paper, algorithms are studied to estimate 3D depth by analyzing the degree of blur of the image taken with a single camera. The optimized object range was obtained by 3D depth estimation derived from depth from defocus using one image of a single camera or two images of different focus of a single camera. For depth estimation using one image, the best performance was achieved using a focal length of 250 mm for both smartphone and DSLR cameras. The depth estimation using two images showed the best 3D depth estimation range when the focal length was set to 150 mm and 250 mm for smartphone camera images and 200 mm and 300 mm for DSLR camera images.

• Korea Journal of Hospital Management
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• v.26 no.1
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• pp.55-70
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• 2021

Purpose: The purpose of this study was to examine the influencing factors of the patient leaning phenomenon in tertiary hospitals. Based on the results of this study, we intended to find implications for improving the problems of the delivery system imbalance in tertiary hospitals caused by patient leaning phenomenon. Methodology/Approach: Qualitative studies were conducted, using focus group interviews and in-depth interviews. The focus group interviews were conducted for 12 users of tertiary hospitals by 2 groups. And in-depth interviews were conducted for 6 tertiary hospital managers. This was considered to be the most effective approach to gather diverse and in-depth information about the influencing factor of patient leaning phenomenon in tertiary hospitals. Findings: In focus group interviews, the reason for choosing tertiary hospitals was the reliability of the hospital(physician, reputation, etc.). And the effect of the policy to strengthen coverage of National Health Insurance and private medical insurance was relatively small. In other words, we found that the individual's desire to receive medical services suitable for one's health status and disease condition was the biggest factor, rather than the cost and policy factors. Practical Implications: We suggested that the appropriate medical care provision should be strengthened according to the role and function of medical institutions. In addition, the education system needs to be reorganized to activate the referral program, expand community medical capabilities, and foster quality primary medical care.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.28-33
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• 2020

Plasma etching process employs high density plasma to create surface chemistry and physical reactions, by which to remove material. Plasma chamber includes silicon-based materials such as a focus ring and gas distribution plate. Focus ring needs to be replaced after a short period. For this reason, there is a need to find materials resistant to erosion by plasma. The developed chemical vapor deposition processing to produce silicon carbide parts with high purity has also supported its widespread use in the plasma etch process. Silicon carbide maintains mechanical strength at high temperature, it have been use to chamber parts for plasma. Recently, besides the structural aspects of silicon carbide, its electrical conductivity and possibly its enhanced life time under high density plasma with less generation of contamination particles are drawing attention for use in applications such as upper electrode or focus rings, which have been made of silicon for a long time. However, especially for high purity silicon carbide focus ring, which has usually been made by the chemical vapor deposition method, there has been no study about quality improvement. The goal of this study is to reduce surface roughness and depth of damage by diamond tool grit size and tool dressing of diamond tools for precise dimensional assurance of focus rings.

• Journal of Digital Convergence
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• v.10 no.4
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• pp.191-197
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• 2012

In this paper we present creating 3D image from 2D image by extract initial depth values calculated from focal values. The initial depth values are created by using the extracted focal information, which is calculated by the comparison of original image and Gaussian filtered image. This initial depth information is allocated to the object segments obtained from normalized cut technique. Then the depth of the objects are corrected to the average of depth values in the objects so that the single object can have the same depth. The generated depth is used to convert to 3D image using DIBR(Depth Image Based Rendering) and the generated 3D image is compared to the images generated by other techniques.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1081-1086
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• 2006

일반적으로 프로젝터는 심도(depth of field)가 제한되어 있기 때문에 스크린이 곡면일 경우, 일부 영역에서는 초점이 맞지 않게(out-of-focus) 된다. 이런 out-of-focus 영역의 정보는 블러링(blurring)되기 때문에 사용자에게 정확한 정보를 전달할 수 없다. 여러 대의 프로젝터를 이용할 경우, 각 프로젝터는 다른 in-focus 영역을 가지기 때문에 각 프로젝터 픽셀의 in-focus/out-of-focus 판별을 통해 in-focus 픽셀만을 투사함으로써, out-of-focus 픽셀의 영향을 제거할 수 있다. 그러나 여러 대의 프로젝터의 in-focus영역이 거의 일치할 경우, out-of-focus영역은 여전히 out-of-focus상태일 수 밖에 없다. 따라서, 각 프로젝터의 초점을 유연하게 조절하면서 동시에 여러 대의 프로젝터에 의한 in-focus 영역을 최대한 크게 할 수 있는 방법이 필요하다. 본 논문에서는 각 프로젝터의 초점을 유동적으로 조절하면서 취득된 영상을 처리하여 in-focus 영역을 판별하고 각 프로젝터에 대한 in-focus 영역을 조합하여 전체in-focus 영역의 면적을 최대화하는 방법을 제안한다. 제안된 방법의 유용성을 검증하기 위해, 각 프로젝터의 in-focus 영역을 적절한 컬러를 이용해 시각적으로 표현해주고 이 정보를 참조해 각 프로젝터의 초점을 유동적으로 조절하여 전초점 영상을 만들어내는 시스템을 구현하였다.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.4
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• pp.209-215
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• 2022

This paper proposes a novel approach for training an unsupervised depth estimation algorithm. The objective of unsupervised depth estimation is to estimate pixel-wise distances from camera without external supervision. While most previous works focus on model architectures, loss functions, and masking methods for considering dynamic objects, this paper focuses on the training framework to effectively use depth cue. The main loss function of unsupervised depth estimation algorithms is known as the photometric error. In this paper, we claim that direct depth cue is more effective than the photometric error. To obtain the direct depth cue, we adopt the technique of knowledge distillation which is a teacher-student learning framework. We train a teacher network based on a previous unsupervised method, and its depth predictions are utilized as pseudo labels. The pseudo labels are employed to train a student network. In experiments, our proposed algorithm shows a comparable performance with the state-of-the-art algorithm, and we demonstrate that our teacher-student framework is effective in the problem of unsupervised depth estimation.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.4
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• pp.599-607
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• 2000

In this study, experiments have been performed to examine the effects of background gray-level on the depth-of-field and on the in-focus criteria. The normalized value of contrast(VC) and the gradient indicator(GI) were used as the in-focus criteria for the small and the large size-ranges of particles, respectively. The slightly larger number of pixels were detected with the brighter background. The maximum of the normalized value of contrast(VCmax) is decreased with the brighter background and its deviation from that with the background gray-level of 160 turned out to be about $pm$15% when the background gray-level changes from 100 to 200. However, the maximum gradient indicator(GImax) changes with the background gray-level within only $pm$5%. The depth-of-field for the VC-applicable particle-size range is largely dependent on the background gray-level. On the other hand, the depth-of-field for the GI-applicable particle-size range changes only slightly with the background gray-level. To keep the normalized standard deviation of the particle size within 0.1, the background gray-level should be set 160$pm$20 for both the VC-applicable and GI-applicable ranges which cover the particle size between $10{\mu}m$ and $300{\mu}m$.

• Journal of Digital Convergence
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• v.12 no.11
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• pp.309-316
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• 2014

In this paper, we propose an efficient detph map creation method using Graph Cut and Discrete Wavelet Transform. First, we have segmented the original image by using Graph Cut to process with its each areas. After that, the information which describes segmented areas of original image have been created by proposed labeling method for segmented areas. And then, we have created four subbands which contain the original image's frequency information. Finally, the depth map have been created by frequency map which made with HH, HL subbands and depth information calculation along the each segmented areas. The proposed method can perform efficient depth map creation process because of dynamic allocation using depth information. We also have tested the proposed method using PSNR(Peak Signal to Noise Ratio) method to evaluate ours.