• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.4
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• pp.1128-1139
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• 2012

With the development of three-dimensional display and related technologies, depth video coding becomes a new topic and attracts great attention from industries and research institutes. Because (1) the depth video is not a sequence of images for final viewing by end users but an aid for rendering, and (2) depth video is simpler than the corresponding color video, fast algorithm for depth video is necessary and possible to reduce the computational burden of the encoder. This paper proposes a fast mode decision algorithm for depth video coding based on depth segmentation. Firstly, based on depth perception, the depth video is segmented into three regions: edge, foreground and background. Then, different mode candidates are searched to decide the encoding macroblock mode. Finally, encoding time, bit rate and video quality of virtual view of the proposed algorithm are tested. Experimental results show that the proposed algorithm save encoding time ranging from 82.49% to 93.21% with negligible quality degradation of rendered virtual view image and bit rate increment.

• Journal of radiological science and technology
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• v.41 no.2
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• pp.129-134
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• 2018

The present study made a phantom for gamma ray of 140 keV radiated from $^{99m}Tc$, examined shielding effect of lead by thickness of the shielding material, and measured surface dose and depth dose by body depth. The OSL Nano Dot dosimeter was inserted at 0, 3, 15, 40, 90, and 180 mm depths of the phantom, and when there was no shield, 0.2 mm lead shield, 0.5 mm lead shield, The depth dose was measured. Experimental results show that the total cumulative dose of dosimeters with depth is highest at 366.24 uSv without shield and lowest at 94.12 uSv with 0.5 mm lead shield. The shielding effect of 0.2 mm lead shielding was about 30.18% and the shielding effect of 0.5 mm lead shielding was 74.30%, when the total sum of the accumulated doses of radiation dosimeter was 100%. The phantom depth and depth dose measurements showed the highest values at 0 mm depth for all three experiments and the dose decreases as the depth increases. This study proved that the thicker a shielding material, the highest its shielding effect is against gamma ray of 140 keV. However, it was known that shielding material can't completely shield a body from gamma ray; it reached deep part of a human body. Aside from the International Commission on Radiation Units and Measurements (ICRU) recommending depth dose by 10 mm in thickness, a plan is necessary for employees working in department of nuclear medicine where they deal with gamma ray, which is highly penetrable, to measure depth dose by body depth, which can help them manage exposed dose properly.

• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.3-10
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• 2018

Depth map has been utilized in a varity of fields. Recently research on generating depth map by artificial neural network (ANN) has gained much interest. This paper validates the feasibility of implementing the ready-made depth map generation by convolutional neural network (CNN). First, for a given image, a depth map is generated by the weighted average of a saliency map as well as a motion history image. Then CNN network is trained by test images and depth maps. The objective and subjective experiments are performed on the CNN and showed that the CNN can replace the ready-made depth generation method.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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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.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1848-1851
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• 1997

This paper presents the development of depth detection algorithm for the 3D Endoscopic Data using a stereo matchod and depth calcuation.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.4
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• pp.395-399
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• 2006

A method to recognize unpaved road region using a 3D depth measurement system is proposed for mobile robots. For autonomous maneuvering of mobile robots, recognition of obstacles or recognition of road region is the essential task. In this paper, the 3D depth measurement system which is composed of a rotating mirror, a line laser and mono-camera is employed to detect depth, where the laser light is reflected by the mirror and projected to the scene objects whose locations are to be determined. The obtained depth information is converted into an image. Such depth images of the road region represent even and plane while that of off-road region is irregular or textured. Therefore, the problem falls into a texture identification problem. Road region is detected employing a simple spatial differentiation technique to detect the plain textured area. Identification results of the diverse situation of unpaved trail are included in this paper.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.980-983
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• 2002

In micro-machining, diamond tool is commonly used because it brings much better micro-machinability due to its edge sharpness. However, it is a big question even how thinly the sharp edge of a diamond tool can cut a ship from the workpiece surface. This paper is to investigate the critical cutting depth, at which the dominant cutting mode changes from chip formation to burnishing or vice versa, for a given edge radius. The theoretically critical cutting depth is 0.25$\mu\textrm{m}$(0.8$\mu\textrm{m}$) in cutting using a square type(V-type) diamond tool that has edge radius of 1$\mu\textrm{m}$(1.5$\mu\textrm{m}$). Experimentally, the dominant cutting mode changes and cutting surface becomes better at critical cutting depth. To get high quality surface, depth of cut must be critical cutting depth because less plastically deformed substrate is left on the surface.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4968-4986
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• 2017

In this paper, we primarily address the difficulty of automatic generation of a plausible depth map from a single image in an unstructured environment. The aim is to extrapolate a depth map with a more correct, rich, and distinct depth order, which is both quantitatively accurate as well as visually pleasing. Our technique, which is fundamentally based on a preexisting DepthTransfer algorithm, transfers depth information at the level of superpixels. This occurs within a framework that replaces a pixel basis with one of instance-based learning. A vital superpixels feature enhancing matching precision is posterior incorporation of predictive semantic labels into the depth extraction procedure. Finally, a modified Cross Bilateral Filter is leveraged to augment the final depth field. For training and evaluation, experiments were conducted using the Make3D Range Image Dataset and vividly demonstrate that this depth estimation method outperforms state-of-the-art methods for the correlation coefficient metric, mean log10 error and root mean squared error, and achieves comparable performance for the average relative error metric in both efficacy and computational efficiency. This approach can be utilized to automatically convert 2D images into stereo for 3D visualization, producing anaglyph images that are visually superior in realism and simultaneously more immersive.

• Communications for Statistical Applications and Methods
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• v.30 no.3
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• pp.227-244
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• 2023

In this paper, a depth-based nonparametric test for a multivariate two-sample scale problem is proposed. The proposed test statistic is based on the depth-induced ranks and is thus distribution-free. In this article, the depth values of data points of one sample are calculated with respect to the other sample or distribution and vice versa. A comprehensive simulation study is used to examine the performance of the proposed test for symmetric as well as skewed distributions. Comparison of the proposed test with the existing depth-based nonparametric tests is accomplished through empirical powers over different depth functions. The simulation study admits that the proposed test outperforms existing nonparametric depth-based tests for symmetric and skewed distributions. Finally, an actual life data set is used to demonstrate the applicability of the proposed test.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.794-802
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• 2022

The recovery of reasonable depth information from different scenes is a popular topic in the field of computer vision. For generating depth maps with better details, we present an efficacious monocular depth prediction framework with coordinate attention and feature fusion. Specifically, the proposed framework contains attention, multi-scale and feature fusion modules. The attention module improves features based on coordinate attention to enhance the predicted effect, whereas the multi-scale module integrates useful low- and high-level contextual features with higher resolution. Moreover, we developed a feature fusion module to combine the heterogeneous features to generate high-quality depth outputs. We also designed a hybrid loss function that measures prediction errors from the perspective of depth and scale-invariant gradients, which contribute to preserving rich details. We conducted the experiments on public RGBD datasets, and the evaluation results show that the proposed scheme can considerably enhance the accuracy of depth prediction, achieving 0.051 for log10 and 0.992 for δ<1.25³ on the NYUv2 dataset.