• 제목/요약/키워드: Depth

검색결과 26,306건 처리시간 0.052초

Scalable Coding of Depth Images with Synthesis-Guided Edge Detection

  • Zhao, Lijun;Wang, Anhong;Zeng, Bing;Jin, Jian
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제9권10호
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    • pp.4108-4125
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    • 2015
  • This paper presents a scalable coding method for depth images by considering the quality of synthesized images in virtual views. First, we design a new edge detection algorithm that is based on calculating the depth difference between two neighboring pixels within the depth map. By choosing different thresholds, this algorithm generates a scalable bit stream that puts larger depth differences in front, followed by smaller depth differences. A scalable scheme is also designed for coding depth pixels through a layered sampling structure. At the receiver side, the full-resolution depth image is reconstructed from the received bits by solving a partial-differential-equation (PDE). Experimental results show that the proposed method improves the rate-distortion performance of synthesized images at virtual views and achieves better visual quality.

비지도학습 기반의 뎁스 추정을 위한 지식 증류 기법 (Knowledge Distillation for Unsupervised Depth Estimation)

  • 송지민;이상준
    • 대한임베디드공학회논문지
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    • 제17권4호
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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.

콘크리트 균열 깊이와 이미지 특성정보간의 상관성 분석 (Correlation Analysis between Crack Depth of Concrete and Characteristics of Images)

  • 정서영;유정호
    • 한국건축시공학회:학술대회논문집
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    • 한국건축시공학회 2021년도 봄 학술논문 발표대회
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    • pp.162-163
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    • 2021
  • Currently, the depth of cracks is measured using ultrasonic detectors in maintenance practice. This method consists of measuring the depth of cracks by attaching ultrasonic depth measuring equipment to the concrete surface, and there are restrictions on the timing and location of the inspection. These limitations can be addressed through the development of image-based crack depth measurement AI technology. If crack depth measurements are made based on images, restrictions on the timing and location of inspections can be lifted because images acquired with simple filming equipment can be used as input information. To efficiently develop these artificial intelligence technologies, it is essential to identify the interrelationship between crack depth measurements and image characteristic information. Thus, this study is a basic study of the development of image-based crack depth measurement AI technology and aims to identify image characteristic information related to crack depth.

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깊이 카메라를 이용한 객체 분리 및 고해상도 깊이 맵 생성 방법 (Foreground Segmentation and High-Resolution Depth Map Generation Using a Time-of-Flight Depth Camera)

  • 강윤석;호요성
    • 한국통신학회논문지
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    • 제37C권9호
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    • pp.751-756
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    • 2012
  • 본 논문에서는 색상 카메라와 Time-of-Flight (TOF) 깊이 카메라를 이용해 촬영된 장면에서 전경 영역을 분리하고 영상의 고해상도 깊이 정보를 구하는 방법에 대해 제안한다. 깊이 카메라는 장면의 깊이 정보를 실시간으로 측정할 수 있는 장점이 있지만 잡음과 왜곡이 발생하고 색상 영상과의 상관도도 떨어진다. 따라서 이를 색상 영상과 함께 사용하기 위한 색상 영상의 영역화 및 깊이 카메라 영상의 3차원 투영(warping) 작업, 깊이 경계 영역 탐색 등을 진행한 후, 전경의 객체를 분리하고, 객체와 배경에 대하여 깊이 값 계산한다. 깊이 카메라로부터 얻은 초기 깊이 정보를 이용하여 색상 영상에서 구해진 깊이 맵은 기존의 방법인 스테레오 정합 등의 방법보다 우수한 성능을 나타내었고, 무늬가 없는 영역이나 객체 경계 영역에서도 정확한 깊이 정보를 구할 수 있었다.

Resolution-independent Up-sampling for Depth Map Using Fractal Transforms

  • Liu, Meiqin;Zhao, Yao;Lin, Chunyu;Bai, Huihui;Yao, Chao
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제10권6호
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    • pp.2730-2747
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    • 2016
  • Due to the limitation of the bandwidth resource and capture resolution of depth cameras, low resolution depth maps should be up-sampled to high resolution so that they can correspond to their texture images. In this paper, a novel depth map up-sampling algorithm is proposed by exploiting the fractal internal self-referential feature. Fractal parameters which are extracted from a depth map, describe the internal self-referential feature of the depth map, do not introduce inherent scale and just retain the relational information of the depth map, i.e., fractal transforms provide a resolution-independent description for depth maps and could up-sample depth maps to an arbitrary high resolution. Then, an enhancement method is also proposed to further improve the performance of the up-sampled depth map. The experimental results demonstrate that better quality of synthesized views is achieved both on objective and subjective performance. Most important of all, arbitrary resolution depth maps can be obtained with the aid of the proposed scheme.

심폐소생술에서 두 개의 가속도 센서를 활용한 흉부 압박 깊이 추정 (Estimation of Chest Compression Depth using two Accelerometers during CPR)

  • 송영탁;오재훈;서영수;지영준
    • 대한의용생체공학회:의공학회지
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    • 제31권5호
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    • pp.407-411
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    • 2010
  • During the cardiopulmonary resuscitation (CPR), the correct chest compression depth and period are very important to increase the resuscitation possibility. For the feedback of chest compression depth, the depth monitoring device based on the accelerometer is developed and widely used. But this method tends to overestimate the compression depth on the bed. To overcome this limitation, the chest compression depth estimation method using two accelerometers is suggested With the additional accelerometer between the patient and mattress on the bed, the compression of the mattress is also measured and it is used to compensate the overestimation error. The experimental results show that the single accelerometer estimates as 61.4mm for the actual compression depth of 43.6mm on the mattress. The depth estimation with the dual accelerometer was 44.6mm which is close to the actual depth. With the automatic zeroing in every single compression, the integration error for the depth can be reduced. The dual accelerometer method is effective to increase the accuracy of the chest compression depth estimation.

MRI를 통한 풍부혈(GV16)의 안전 자침 깊이에 대한 연구 (Safe Needling Depth of Pungbu(GV16) with MRI-a Retrospective Study)

  • 양현정;박해인;이광호
    • Journal of Acupuncture Research
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    • 제32권4호
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    • pp.11-16
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    • 2015
  • Objectives : The purpose of this study is to determine the safe needling depth of Pungbu($GV_{16}$) retrospectively by using magnetic resonance imaging (MRI). Methods : We chose 114 Brain or C-spine MRI images from the Sang-Ji hospital picture archiving communication system. We measured the shortest distance from skin to cerebral dura mater passing by posterior edge of the foramen magnum on the sagittal view for the depth of Pungbu. We analyzed the differences between male and female measured values by using a student t-test. Results : The average depth of male insertion was $49.71{\pm}6.32mm$ and the shortest depth of insertion was 36.29 mm. The average depth of female insertion was $39.84{\pm}5.25mm$ and the shortest depth of insertion was 30.02 mm. The results showed a significant difference according to gender (p=0.00). Conclusions : The depth of male insertion is deeper than that of female, and the safe needling depth in the case of males is 36.29-67.35 mm, while the safe needling depth in the of females is 30.02-52.18 mm.

Effects of Maximum Repeated Squat Exercise on Number of Repetition, Trunk and Lower Extremity EMG Response according to Water Depth

  • Jang, Tae Su;Lee, Dong Sub;Kim, Ki Hong;Kim, Byung Kwan
    • International Journal of Internet, Broadcasting and Communication
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    • 제13권1호
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    • pp.152-160
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    • 2021
  • The purpose of this study was to investigate the difference in the number of repetitions and the change in electromyographic response during the maximum speed squat exercise according to the depth conditions and the maximum speed squat exercise according to the time of each depth. Ten men in their 20s were selected as subjects and the maximum speed squat was performed for one minute in three environmental conditions (ground, knee depth, waist depth). We found that the number of repetitions according to the depth of water showed a significant difference, and as a result of the post-mortem comparison, the number of repetitions was higher in the ground condition and the knee depth than in the waist depth. And the muscle activity of rectus abdominis, erector spinae, rectus femoris, biceps femoris was increased during ground squat exercise, activity of all muscle was decreased during knee depth squat exercise, and activity of rectus abdominis, erector spinae, biceps femoris, tibialis anterior, gastrocnemius was decreased during waist depth squat. In conclusion, muscle activity of lower extremities during squat exercise in underwater environment can be lowered as the depth of water is deep due to buoyancy, but muscle activity of trunk muscles can be increased rather due to the effect of viscosity and drag.

깊이 불연속 정보를 이용한 저해상도 깊이 영상의 업샘플링 방법 (Low-Resolution Depth Map Upsampling Method Using Depth-Discontinuity Information)

  • 강윤석;호요성
    • 한국통신학회논문지
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    • 제38C권10호
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    • pp.875-880
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    • 2013
  • 시청자에게 입체감과 몰입감을 줄 수 있는 3차원 영상의 제작을 위해서는 장면의 색상 영상과 함께 깊이 정보가 필요하다. 일반적으로 장면의 깊이를 측정하는 깊이 센서에서 획득된 깊이 영상은 매우 작은 해상도를 가진다. 따라서 색상 영상과 함께 3차원 영상 제작에 이러한 깊이 영상을 사용하기 위해서는 저해상도 깊이 영상의 업샘플링 기술이 필요하다. 본 논문에서는 깊이 불연속 정보를 이용하여 저해상도 깊이 영상을 업샘플링하는 방법을 설명한다. 깊이 영상을 업샘플링할 때 가장 민감하게 다루어야 할 깊이 불연속 부분을 고해상도 색상과 저해상도 깊이 영상으로부터 찾아낸다. 그리고 깊이 불연속 부분을 고려하여 깊이 영상 업샘플링을 위한 에너지 함수를 모델링하고, 신뢰 확산(belief propagation) 방법을 이용하여 해상도가 확대된 깊이 영상을 획득한다. 제안하는 방법은 필터 기반이나 에너지 함수 기반의 다른 방법들보다 우수한 성능을 나타내었다.

오류 보정을 이용한 초점 이미지들로부터의 깊이 추출 (Depth Extraction From Focused Images Using The Error Interpolation)

  • 김진사;노경완;김충원
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 1999년도 하계종합학술대회 논문집
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    • pp.627-630
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    • 1999
  • For depth extraction from the focus and recovery the shape, determination of criterion function for focus measure and size of the criterion window are very important. However, Texture, illumination, and magnification have an effect on focus measure. For that reason, depth map has a partial high and low peak. In this paper, we propose a depth extraction method from focused images using the error interpolation. This method is modified the error depth into mean value between two normal depth in order to improve the depth map.

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