• ETRI Journal
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• v.35 no.6
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• pp.1011-1020
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• 2013

In this paper, we present a method for obtaining a high-quality 3D depth. The advantages of active pattern projection and passive stereo matching are combined and a system is established. A diffractive optical element (DOE) is developed to project the active pattern. Cross guidance (CG) and auto guidance (AG) are proposed to perform the passive stereo matching in a stereo image in which a DOE pattern is projected. When obtaining the image, the CG emits a DOE pattern periodically and consecutively receives the original and pattern images. In addition, stereo matching is performed using these images. The AG projects the DOE pattern continuously. It conducts cost aggregation, and the image is restored through the process of removing the pattern from the pattern image. The ground truth is generated to estimate the optimal parameter among various stereo matching algorithms. Using the ground truth, the optimal parameter is estimated and the cost computation and aggregation algorithm are selected. The depth is calculated and bad-pixel errors make up 4.45% of the non-occlusion area.

• Proceedings of the Korean Geotechical Society Conference
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• 2004.03b
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• pp.219-226
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• 2004

We measure subsidences occurred in a reclaimed coastal land, Noksan industrial complex, by using JERS-1 SAR (1996-1998) and RADARSAT-1 SAR (2002-2003) dataset. SAR with a high spatial resolution (about several or several tens meter) can reveal the two-dimensional distribution of settlement that would be bardly estimated from in situ measurements. The DInSAR results show significant deformation signal associated with soil consolidation. Accuracy of the settlements estimated by 2-pass differential interferometry (DInSAR) is evaluated using the measurements of settlement gauge. A two-dimensional subsidence map is constructed from 7 qualified pairs. Comparing the JERS-1 radar measurements with the ground truth data yields the correlation coefficient of 0.87 (RMSE of 1.44 cm). The regression line shows the gradient of 1.04 and intercepts close to the origin, which implies that the unbiased settlement can be measured by DInSAR technique. The residual settlements are also detected from RADARSAT-1 pairs. The extent and amount of the settlements are matched well with ground truth data.

• Journal of Biosystems Engineering
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• v.36 no.3
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• pp.180-186
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• 2011

The objective of this research was to establish the estimation method of growth information on potato using Multi-Spectral Image Sensor (MSIS) and Global Positioning System (GPS). And growth estimation map for determining a prescription map over the entire field was generated. To determine the growth model, 10 ground-truth points of areas of $4m^2$ each were selected and investigated. The growth information included stem number, crop height and SPAD value. In addition, images information involving the ground-truth points were also taken by an unmanned helicopter, and reflectance value of Green, Red, and NIR bands were calculated with image processing. Then, growth status of potato was modeled by multi-regression analysis using these reflectance value of Green, Red, and NIR. As a result, potato growth information could be detected by analyzing Green, Red, and NIR images. Stem number, crop height and SPAD value could be estimated with $R^2$ values of 0.600, 0.657 and 0.747 respectively. The generated GIS map would describe variability of the potato growth in a whole field.

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.503-513
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• 2020

This paper presents a method to evaluate denoising filters based on edge locations in their denoised images. Image quality assessment has often been performed by using structural similarity (SSIM). However, SSIM does not provide clearly the geometric accuracy of features in denoised images. Thus, in this paper, a method to localize edge locations with subpixel accuracy based on adaptive weighting of gradients is used for obtaining the subpixel locations of edges in ground truth image, noisy images, and denoised images. Then, this paper proposes a method to evaluate the geometric accuracy of edge locations based on root mean squares error (RMSE) and jaggedness with reference to ground truth locations. Jaggedness is a measure proposed in this study to measure the stability of the distribution of edge locations. Tested denoising filters are anisotropic diffusion (AF), bilateral filter, guided filter, weighted guided filter, weighted mean of patches filter, and smoothing filter (SF). SF is a simple filter that smooths images by applying a Gaussian blurring to a noisy image. Experiments were performed with a set of simulated images and natural images. The experimental results show that AF and SF recovered edge locations more accurately than the other tested filters in terms of SSIM, RMSE, and jaggedness and that SF produced better results than AF in terms of jaggedness.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.111-116
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• 2021

The hyper-spectral image is data obtained by dividing the electromagnetic wave band in the infrared region into hundreds of wavelengths. It is used to find or classify objects in various fields. Recently, deep learning classification method has been attracting attention. In order to use hyper-spectral image data as deep learning training data, a processing technique is required compared to conventional visible light image data. To solve this problem, we developed a software that selects specific wavelength images from the hyper-spectral data cube and performs the ground truth task. We also developed software to manage data including environmental information. This paper describes the configuration and function of the software.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2547-2567
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• 2021

It has been widely acknowledged that occlusion impairments adversely distress many face recognition algorithms' performance. Therefore, it is crucial to solving the problem of face image occlusion in face recognition. To solve the image occlusion problem in face recognition, this paper aims to automatically de-occlude the human face majority or discriminative regions to improve face recognition performance. To achieve this, we decompose the generative process into two key stages and employ a separate generative adversarial network (GAN)-based network in both stages. The first stage generates an initial coarse face image without an occlusion mask. The second stage refines the result from the first stage by forcing it closer to real face images or ground truth. To increase the performance and minimize the artifacts in the generated result, a new refine loss (e.g., reconstruction loss, perceptual loss, and adversarial loss) is used to determine all differences between the generated de-occluded face image and ground truth. Furthermore, we build occluded face images and corresponding occlusion-free face images dataset. We trained our model on this new dataset and later tested it on real-world face images. The experiment results (qualitative and quantitative) and the comparative study confirm the robustness and effectiveness of the proposed work in removing challenging occlusion masks with various structures, sizes, shapes, types, and positions.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.11-19
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• 2023

In this paper, it is confirmed that the metrics used to evaluate image quality can be applied to low-light images. Due to the nature of low-illumination images, factors related to light create various noise patterns, and the smaller the amount of light, the more severe the noise. Therefore, in situations where it is difficult to obtain a clean image without noise, the quality of a low-illuminance image from which noise has been removed is often judged by the human eye. In this paper, noise in low-illuminance images for which ground truth cannot be obtained is removed using Noise2Noise, and spatial resolution and radial resolution are evaluated using ISO 12233 charts and colorchecker as metrics such as MTF and SNR. It can be shown that the quality of the low-illuminance image, which has been evaluated mainly for qualitative evaluation, can also be evaluated quantitatively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.222-224
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• 2022

Deep learning-based computer vision applications in the field of object segmentation take a transfer learning method using hyperparameters and models pretrained and distributed by STOA techniques to improve performance. Custom datasets used in this process require a lot of resources, such as time and labeling, in labeling tasks to generate Ground Truth information. In this paper, we present an automatic labeling construction method using object segmentation techniques so that resources such as time and labeling can be used less to build custom datasets used in deep learning neural networks.

• Journal of Korean Academy of Nursing
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• v.24 no.2
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• pp.206-215
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• 1994

Intuition is an abstract concept which is most often thought of as a nonrational, nonscientific mode of thought. However, since there are so many amorphous definitions of intuition that it seems important to clarify the meaning of this concept. Therefore, this study use the process of Walker & Avant’s concept analysis to define of the concept of intuition Attributes of intuition were defined as 1) Knowledge of truth that is difficult to explicate ; 2) A type of immediate knowing ; 3) Knowlwdge without reasining analysis ; 4) Knowledge that is attained based on virtue character which integrates all matter and is not attained through individual experience. Antecedents of intuition consists of 1) as ground for knowledge or truth that is not availables to trace through the analytic procedures ; & 2) the flow of Ki which unites human beings and the universe. Consequences of intuition events or incidents occuring as a result of the concept consist of verification of the truth though analytic procedures and application of knowledge in both theoretical and practical ways. To develop intuitive ability, as an educator should not only make studies in recognizine, analysing and teaching concepts related to logical, rational decision making but should also recognize and teach concepts related to intuitive components of making decisions in clinical practice and classroom learning as well.

• Structural Monitoring and Maintenance
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• v.1 no.2
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• pp.197-211
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• 2014

An investigation of tunnel linings is performed at two tunnels in the US using complimentary noncontact techniques: air-coupled ground penetrating radar (GPR), and a vehicle-mounted scanning system (SPACETEC) that combines laser, visual, and infrared thermography scanning methods. This paper shows that a combination of such techniques can maximize inspection coverage in a comprehensive and efficient manner. Since ground-truth is typically not available in public tunnel field evaluations, the noncontact techniques used are compared with two reliable in-depth contact nondestructive testing methods: ground-coupled GPR and ultrasonic tomography. The noncontact techniques are used to identify and locate the reinforcement mesh, structural steel ribs, internal layer interfaces, shallow delamination, and tile debonding. It is shown that this combination of methods can be used synergistically to provide tunnel owners with a comprehensive and efficient approach for monitoring tunnel lining conditions.