• 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.

• Journal of Biomedical Engineering Research
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• v.31 no.5
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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.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.171-174
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• 2003

In this paper, we propose the method of stereo images composition using adaptive dense disparity estimation. For the correct composition of stereo image and 3D virtual object, we need correct marker position and depth information. The existing algorithms use position information of markers in stereo images for calculating depth of calibration object. But this depth information may be wrong in case of inaccurate marker tracking. Moreover in occlusion region, we can't know depth of 3D object, so we can't composite stereo images and 3D virtual object. In these reasons, the proposed algorithm uses adaptive dense disparity estimation for calculation of depth. The adaptive dense disparity estimation is the algorithm that use pixel-based disparity estimation and the search range is limited around calibration object.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.11
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• pp.52-62
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• 1995

In the laser surface hardening process the geometrical parameters, especially the depth, of the hardened layer are utilized to assess the integrity of the hardening layer quality. Monitoring of this geometrical parameter ofr on-line process control as well as for on-line quality evaluation, however, is an extremely difficult problem because the hardening layer is formed beneath a material surface. Moreover, the uncertainties in monitoring the depth can be raised by the inevitable use of a surface coating to enhance the processing efficiency and the insufficient knowledge on the effects of coating materials and its thicknesses. The paper describes the extimation results using neural network to estimate the hardening layer depth from measured surface temperanture and process variables (laser beam power and feeding velocity) under various situations. To evaluate the effec- tiveness of the measured temperature in estimating the harding layer depth, estimation was performed with or without temperature informations. Also to investigate the effects of coating thickness variations in the real industry situations, in which the coating thickness cannot be controlled uniform with good precision, estimation was done over only uniformly coated specimen or various thickness-coated specimens. A series of hardening experiments were performed to find the relationships between the hardening layer depth, temperature and process variables. The estimation results show the temperature informations greatly improve the estimation accuracy over various thickness-coated specimens.

• Structural Monitoring and Maintenance
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• v.9 no.4
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• pp.373-388
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• 2022

A USW based diagnostic procedure is presented for estimating the depth of surface-breaking cracks. The diagnosis is demonstrated on seven lab-scale SFRC beam specimens, which are subjected to the CMOD controlled three-point bending test to create real bending cracks. Then, the recorded multiple ultrasonic signals are examined with the signal processing techniques, including wavelet transform and two-dimensional Fourier transform, to investigate the relationships between the crack depth and two diagnostic indices, namely the attenuation coefficient and dispersion index (DI). Finally, the reliabilities of these indices for depth estimation are verified with the visually measured crack depths as well as the crack features obtained with a digital image processing algorithm. It is found that the DI outperforms the attenuation coefficient in depth estimation, where this index displays good agreement with the visual inspection for 86% of the inspected specimens.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.45-54
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• 2022

Image depth estimation is a technology that is the basis of various image analysis. As analysis methods using deep learning models emerge, studies using deep learning in image depth estimation are being actively conducted. Currently, most deep learning-based depth estimation models are being trained with clean and ideal images. However, due to the lack of data on adverse conditions such as haze or fog, the depth estimation may not work well in such an environment. It is hard to sufficiently secure an image in these environments, and in particular, obtaining non-homogeneous haze data is a very difficult problem. In order to solve this problem, in this study, we propose a method of synthesizing non-homogeneous haze images and a learning method for a monocular depth estimation deep learning model using this method. Considering that haze mainly occurs outdoors, datasets mainly containing outdoor images are constructed. Experiment results show that the model with the proposed method is good at estimating depth in both synthesized and real haze data.

• Journal of Korea Society of Digital Industry and Information Management
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• v.4 no.2
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• pp.15-19
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• 2008

In this paper, we propose a new algorithm of image depth detection using motion estimation and object tracking. In industry, robots are used for automobile, conveyer system, etc. But, these have much necessary time. Thus, in this paper, we develop the efficient method of image depth detection based on motion estimation and object tracking.

• ETRI Journal
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• v.44 no.1
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• pp.38-50
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• 2022

This paper presents a study on the use of encoder-derived features in decoder-side depth estimation. The scheme of multiview video encoding does not require the transmission of depth maps (which carry the geometry of a three-dimensional scene) as only a set of input views and their parameters are compressed and packed into the bitstream, with a set of features that could make it easier to estimate geometry in the decoder. The paper proposes novel recursive block splitting for the feature extraction process and evaluates different scenarios of feature-driven decoder-side depth estimation, performed by assessing their influence on the bitrate of metadata, quality of the reconstructed video, and time of depth estimation. As efficient encoding of multiview sequences became one of the main scopes of the video encoding community, the experimental results are based on the "geometry absent" profile from the incoming MPEG Immersive video standard. The results show that the quality of synthesized views using the proposed recursive block splitting outperforms that of the state-of-the-art approach.

• Journal of Korea Multimedia Society
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• v.20 no.11
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• pp.1711-1719
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• 2017

In this paper, we propose a method of the zoom motion estimation for the depth video coding. The proposed method calculates the zoom ratio using the average of the depth values in the current block and in the reference block. It resizes the reference block by the zoom ratio and interpolates the reference block to size of the current block. It compares the current block with the reference block that is obtained by subtracting the average of pixels from the current block to the reference block in order to find the reference block that is the best closest one to the current block. The results of the simulation for the proposed method show that the motion estimation errors are significantly reduced.

• Journal of Korea Multimedia Society
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• v.16 no.2
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• pp.131-137
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• 2013

Zoom motion estimation of video sequence is very complicated for implementation. In this paper, we propose a method to implement the zoom motion estimation using together the depth camera and color camera. Depth camera obtains the distance information between current block and reference block, then zoom ratio between both blocks is calculated from this distance information. As the reference block is appropriately zoomed by the zoom ratio, the motion estimated difference signal can be reduced. Therefore, the proposed method is possible to increase the accuracy of motion estimation with keeping zoom motion estimation complexity not greater. Simulation was to measure the motion estimation accuracy of the proposed method, we can see the motion estimation error was decreased significantly compared to conventional block matching method.