• 제목/요약/키워드: depth accuracy

검색결과 995건 처리시간 0.028초

다시점 영상 생성을 위한 DIBR 기반의 깊이 정확도 향상 방법 (Enhancement Method of Depth Accuracy in DIBR-Based Multiview Image Generation)

  • 김민영;조용주;박경신
    • 정보처리학회논문지:컴퓨터 및 통신 시스템
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    • 제5권9호
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    • pp.237-246
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    • 2016
  • DIBR (Depth Image Based Rendering)은 동일 시점의 색상 영상과 깊이 영상을 참조해서 임의 개수의 중간 시점 영상을 생성하는 기법으로 무안경식 다시점 입체 디스플레이를 위한 콘텐츠 제작에 활용할 수 있다. 본 연구에서는 DIBR 기법을 사용해서 생성되는 다시점 중간 영상의 객관적 품질에 깊이 정확도가 미치는 영향에 대해 설명한다. 본 연구는 먼저 사람이 인지할 수 없는 범위에서 왜곡을 보장하기 위한 최소 깊이 양자화 계수를 도출한다. 그리고 장면 구성의 특성에 따라 같은 양자화 수준에서 깊이 정보의 정확도를 효과적으로 표현하기 위한 비균등 영역분할 양자화 방법을 선형 양자화와 비교 분석한 결과를 제시한다.

원격 로봇선에 의한 운항속도에 따른 수심측량의 정확도 분석 (Accuracy Analysis of Sounding Data Caused by Speed of Robot-ship)

  • 최병길;박홍기;조광희
    • 대한공간정보학회지
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    • 제15권4호
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    • pp.111-116
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    • 2007
  • 본 연구의 목적은 DGPS와 음향측심기를 장착한 소형의 로봇선을 이용하여 저수지의 수심정보를 획득하고 이를 이용하여 정확도를 분석하는데 있다. 즉 로봇선의 속도를 변화시키고 이에 따른 속도별 수심측정의 정확도를 분석하였다. 현장실험 결과, 로봇선의 속도가 느릴수록 수심측정의 오차가 적었으며, 로봇선의 속도가 5km/hr까지는 정규분포의 신뢰구간인 95% 이내에 포함되는 것을 알 수 있었다.

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Implementation of Nose and Face Detections in Depth Image

  • Kim, Heung-jun;Lee, Dong-seok;Kwon, Soon-kak
    • Journal of Multimedia Information System
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    • 제4권1호
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    • pp.43-50
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    • 2017
  • In this paper, we propose a method which detects the nose and face of certain human by using the depth image. The proposed method has advantages of the low computational complexity and the high accuracy even in dark environment. Also, the detection accuracy of nose and face does not change in various postures. The proposed method first locates the locally protruding part from the depth image of the human body captured through the depth camera, and then confirms the nose through the depth characteristic of the nose and surrounding pixels. After finding the correct pixel of the nose, we determine the region of interest centered on the nose. In this case, the size of the region of interest is variable depending on the depth value of the nose. Then, face region can be found by performing binarization using the depth histogram in the region of interest. The proposed method can detect the nose and the face accurately regardless of the pose or the illumination of the captured area.

대면적 가공물의 마이크로 그루빙에서 고속 절삭 깊이 제어를 통한 미세형상의 정밀도 향상 (Improvement of Form Accuracy of Micro-Features on Thin, Large-area Plate using Fast Depth Adjustment in Micro-grooving)

  • 강동배;손성민;이효렬;안중환
    • 한국생산제조학회지
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    • 제22권3호
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    • pp.408-413
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    • 2013
  • Micro-features such as grooves and lenses, which perform optical functions in flat displays, should be manufactured with a good form accuracy because this is directly related to their optical performance. As the size of the display increases, it is very difficult to maintain a high relative accuracy because of the inherent geometric errors such as the waviness of a large-area plate. In this paper, the optical effect of these geometric errors is investigated, and surface-referenced micro-grooving to measure and compensate for such geometric errors on line is proposed to improve the form accuracy of the micro-grooves. A PZT-based fast depth adjustment servo system is implemented in the tool holder to maintain a uniform groove depth in reference to the wavy surface. Through experiments, the proposed method is shown to be an efficient way to produce high-quality micro- grooves on a wavy die surface.

AdaMM-DepthNet: Unsupervised Adaptive Depth Estimation Guided by Min and Max Depth Priors for Monocular Images

  • ;김문철
    • 한국방송∙미디어공학회:학술대회논문집
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    • 한국방송∙미디어공학회 2020년도 추계학술대회
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    • pp.252-255
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    • 2020
  • Unsupervised deep learning methods have shown impressive results for the challenging monocular depth estimation task, a field of study that has gained attention in recent years. A common approach for this task is to train a deep convolutional neural network (DCNN) via an image synthesis sub-task, where additional views are utilized during training to minimize a photometric reconstruction error. Previous unsupervised depth estimation networks are trained within a fixed depth estimation range, irrespective of its possible range for a given image, leading to suboptimal estimates. To overcome this suboptimal limitation, we first propose an unsupervised adaptive depth estimation method guided by minimum and maximum (min-max) depth priors for a given input image. The incorporation of min-max depth priors can drastically reduce the depth estimation complexity and produce depth estimates with higher accuracy. Moreover, we propose a novel network architecture for adaptive depth estimation, called the AdaMM-DepthNet, which adopts the min-max depth estimation in its front side. Intensive experimental results demonstrate that the adaptive depth estimation can significantly boost up the accuracy with a fewer number of parameters over the conventional approaches with a fixed minimum and maximum depth range.

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머시닝센터 평면가공 시 가공횟수에 따른 치수정밀도 특성에 관한 연구 (A Study on Characteristics of Dimensional Accuracy using Planning Number of Machining in Machining Center)

  • 양용모
    • 한국기계가공학회지
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    • 제17권6호
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    • pp.61-67
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    • 2018
  • The face milling cutter, which is mainly used for the face milling, is used to cut the Carbon steel(SM20C) in the machining center for 5 times and 10 times respectively. This study clarify the dimensional accuracy characteristics according to the number of fine machining varied the condition of cutting depth, table feed speed and spindle speed. Cutting depth is varied 0.05~0.2mm, table feed speed is varied 0.05~0.2mm/min and spindle speed is varied 1500~2500rpm. As a result, the dimensional accuracy was stable 6 times machining with table feed speed 150mm/min and 10 times machining with table speed 100mm/min and cutting depth 0.05mm regardless times of machining.

준설공정관리시스템 개발에 관한 연구 (A Study on the Development of Dredge Process Management System)

  • 정대득;이중우;조증언
    • 한국항만학회지
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    • 제15권1호
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    • pp.75-85
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    • 2001
  • Accuracy of dredging processes depends on the types of equipment used, the sediments encountered, whether the work to be performed is new or maintenance dredging, pre- and post-hydrographic surveying and so forth. Among those, position surveying accuracy which is directly determined by the control of the dredge's position and depth surveying accuracy being surveyed at the dredging point during dredging work are important factors. The purpose of this study is to develop 'Dredge Management System'for Grab dredge which is composed of 4 sub-system using LADGPS for dredge position determining system and dredging point determining system, tide gauge system and optical sensor for depth determining system and GIS and ENC for total management system. This system is installed on the grab dredge 'EUNJIN G-18'and applied to anchorage dredging work. at Pohang Harbor. The results revealed that this system is easy to operate, achieves good accuracy with only 45cm unevenness, reduces working period by 22 percent and saves cost 16.6 percent.

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마이크로 가공에서 절삭깊이 보정을 위한 AE 센서의 적용 (Application of AE Sensor for Calibration of Depth of Cut in Micro-machining)

  • 강익수;김정석;김전하
    • 한국정밀공학회지
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    • 제26권9호
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    • pp.53-57
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    • 2009
  • There are technical requirements to manufacture large size functional parts with not only simple geometries like a flat or spherical surface but also sculptured geometries. In addition, the required machining accuracy for these parts is becoming more severe. In general, the form accuracy of machined parts is determined by the relative position between workpiece and tool during machining process. To improve machining accuracy the relative position errors should be maintained within the required accuracy. This study deals with the estimation and calibration of depth of cut using the AE signal in micro-machining. Also, this sensing technique can be applied to detect the initial contact between workpiece and tool.

A novel MobileNet with selective depth multiplier to compromise complexity and accuracy

  • Chan Yung Kim;Kwi Seob Um;Seo Weon Heo
    • ETRI Journal
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    • 제45권4호
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    • pp.666-677
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    • 2023
  • In the last few years, convolutional neural networks (CNNs) have demonstrated good performance while solving various computer vision problems. However, since CNNs exhibit high computational complexity, signal processing is performed on the server side. To reduce the computational complexity of CNNs for edge computing, a lightweight algorithm, such as a MobileNet, is proposed. Although MobileNet is lighter than other CNN models, it commonly achieves lower classification accuracy. Hence, to find a balance between complexity and accuracy, additional hyperparameters for adjusting the size of the model have recently been proposed. However, significantly increasing the number of parameters makes models dense and unsuitable for devices with limited computational resources. In this study, we propose a novel MobileNet architecture, in which the number of parameters is adaptively increased according to the importance of feature maps. We show that our proposed network achieves better classification accuracy with fewer parameters than the conventional MobileNet.

상향절삭에 의한 깊은 홈 가공시 정밀도 향상에 대한 연구 (Improvement of the Accuracy in Machining Deep Pocket by Up Milling)

  • 이상규;고성림
    • 한국정밀공학회지
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    • 제16권4호통권97호
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    • pp.220-228
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    • 1999
  • The machining accuracy has been improved with the development of NC machine tools and cutting tools. However, it is difficult to obtain a high degree of accuracy when machining deep pocket with long end mill, since machining accuracy is mainly dependant on the stiffness of the cutting tool. To improve surface accuracy in machining deep pocket using end mill, the performance by down cut and up cut is compared theoretically and experimentally. To verify usefulness of up milling, various experiments were carried out. As a result, it is found that up milling produce more accurate surface than down milling in machining deep pocket. For effective application of up milling, various values in helix angle, number of teeth, radial depth of cut and axial depth of cut are applied in experiment.

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