• Journal of Broadcast Engineering
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• v.19 no.1
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• pp.31-43
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• 2014

A depth map is an important component for stereoscopic image generation. Since the depth map acquired from a depth camera has a low resolution, upsamling a low-resolution depth map to a high-resolution one has been studied past decades. Upsampling methods are evaluated by objective evaluation tools such as PSNR, Sharpness Degree, Blur Metric. As well, the subjective quality is compared using virtual views generated by DIBR (depth image based rendering). However, works on the analysis of the relation between depth map upsampling and stereoscopic images are relatively few. In this paper, we investigate the relationship between subjective evaluation of stereoscopic images and objective performance of upsampling methods using cross correlation and linear regression. Experimental results demonstrate that the correlation of edge PSNR and visual fatigue is the highest and the blur metric has lowest correlation. Further, from the linear regression, we found relative weights of objective measurements. Further we introduce a formulae that can estimate 3D performance of conventional or new upsampling methods.

• Journal of Broadcast Engineering
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• v.17 no.1
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• pp.73-80
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• 2012

Recently, medical equipments are developed and used for diagnosis or studies. In addition, demand of techniques which automatically deal with three dimensional medical images obtained from the medical equipments is growing. One of the techniques is automatic bone segmentation which is expected to enhance the diagnosis efficiency of osteoporosis, fracture, and other bone diseases. Although various researches have been proposed to solve it, they are unable to be used in practice since a size of the medical data is large and there are many low contrast boundaries with other tissues. In this paper, we present a fast and accurate automatic framework for bone segmentation based on multi-resolutions. On a low resolution step, a position of the bone is roughly detected using constrained branch and mincut which find the optimal template from the training set. Then, the segmentation and the registration are iteratively conducted on the multiple resolutions. To evaluate the performance of the proposed method, we make an experiment with femur and tibia from 50 test knee magnetic resonance images using 100 training set. The proposed method outperformed the constrained branch and mincut in aspect of segmentation accuracy and implementation time.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1321-1330
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• 2023

Normalized Difference Vegetation Index (NDVI) is the most widely used remote sensing data in the agricultural field and is currently provided by most optical satellites. In particular, as high-resolution optical satellite images become available, the selection of optimal optical satellite images according to agricultural applications has become a very important issue. In this study, we aim to define the most optimal optical satellite image when monitoring NDVI in rice fields in Korea and derive the resolution-related requirements necessary for this. For this purpose, we compared and analyzed the spatial distribution and time series patterns of the Dangjin rice paddy in Korea from 2019 to 2022 using NDVI images from MOD13, Landsat-8, Sentinel-2A/B, and PlanetScope satellites, which are widely used around the world. Each data is provided with a spatial resolution of 3 m to 250 m and various periods, and the area of the spectral band used to calculate NDVI also has slight differences. As a result of the analysis, Landsat-8 showed the lowest NDVI value and had very low spatial variation. In comparison, the MOD13 NDVI image showed similar spatial distribution and time series patterns as the PlanetScope data but was affected by the area surrounding the rice field due to low spatial resolution. Sentinel-2A/B showed relatively low NDVI values due to the wide near-infrared band area, and this feature was especially noticeable in the early stages of growth. PlanetScope's NDVI provides detailed spatial variation and stable time series patterns, but considering its high purchase price, it is considered to be more useful in small field areas than in spatially uniform rice paddy. Accordingly, for rice field areas, 250 m MOD13 NDVI or 10 m Sentinel-2A/B are considered to be the most efficient, but high-resolution satellite images can be used to estimate detailed physical quantities of individual crops.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.1
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• pp.73-80
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• 2012

Recent research efforts have focused on combining high dynamic range imaging with super-resolution reconstruction to enhance both the intensity range and resolution of images. The processes developed to date start with a set of multiple-exposure input images with low dynamic range (LDR) and low resolution (LR), and require several procedural steps: conversion from LDR to HDR, SR reconstruction, and tone mapping. Input images captured with irregular exposure steps have an impact on the quality of the output images from this process. In this paper, we present a simplified framework to replace the separate procedures of previous methods that is also robust to different sets of input images. The proposed method first calculates weight maps to determine the best visible parts of the input images. The weight maps are then applied directly to SR reconstruction, and the best visible parts for the dark and highlighted areas of each input image are preserved without LDR-to-HDR conversion, resulting in high dynamic range. A new luminance control factor (LCF) is used during SR reconstruction to adjust the luminance of input images captured during irregular exposure steps and ensure acceptable luminance of the resulting output images. Experimental results show that the proposed method produces SR images of HDR quality with luminance compensation.

• Geophysics and Geophysical Exploration
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• v.5 no.4
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• pp.250-256
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• 2002

Resistivity tomography has become an important tool to image underground resistivity distribution. This method has been widely applied to site investigation for engineering and environmental purpose. In resistivity tomography, various electrode arrays can be used and each array has both merits and demerits. For example, the pole-pole array has high signal to noise ratio (S/N ratio), but its resolution is too low. The dipole-dipole array has low S/N ratio, but its resolution is very high. The Pole-dipole may has intermediate Snf ratio and resolution. The modified Pole-dipole array, recently proposed, shows reasonable S/N ratio and resolution, which are comparable to the pole-dipole array. These electrode arrays except the pole-pole array, however, have the problem that the apparent resistivity can diverge at some special electrode Positions. Also, the Pole-Pole array may not reflect the doe resistivity of an anomalous body. In this study, we propose a new electrode array, mixed array, where pole-dipole and modified pole-dipole ways are selectively used with the relative positions of current and potential electrodes. The mixed array has the same level of S/N ratio and resolution as the pole-dipole array and the apparent resistivity does not diverge in the receiver hole. Furthermore, the apparent resistivity using the array can reflect the true resistivity of the anomalous body.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.295-302
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• 2014

Essential to the establishment of such 3D spatial information are the laser scanning technology to obtain high-precision 3D point group data and the photography-metric camera to obtain high-resolution multispectral image information. The photography-metric camera, however, lacks in usability for its broad scope of utilization due to the high purchase price, difficult purchase channel, and low applicability. This study thus set out to investigate a technique to establish multi-dimensional ortho-image data with a single lens reflex digital camera of high speed and easy accessibility for general users. That is, the study remodeled a single lens reflex digital camera and calibrated the remodeled camera to establish 3D multispectral image information, which is the essential data of 3D spatial information. Multi-dimensional ortho-image data were collected by surveying the reference points for stereo photos, taking multispectral shots of the objects, and converting them into ortho-images.

• Journal of Digital Convergence
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• v.15 no.11
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• pp.463-470
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• 2017

Currently, virtual reality contents using VR images are spotlighted since they can be easily created and utilized. But because VR images are in a state of lack of interaction, there are limitations in their applications and usability.In order to overcome this problem, we propose a new method in which 360 degree panorama image and game engine are utilized to develop a high resolution of interactive VR service in real time. In particular, since the background image, which is represented by a form of panorama image, is pre-generated through a heavy rendering computation, it can be used to provide a immersive VR service with a relatively small amount of computation in run time on a low performance device. In order to show the effectiveness of our proposed method, an interactive game of a virtual zoo environment was implemented and illustrated showing that it can improve user interaction and immersion experience in a pretty good way.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.97-102
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• 2010

A laser speckle is a random pattern that has a granular appearance produced by reflected light when a coherent laser illuminates an irregular course surface. Most important property of laser speckle is detecting micro-vascular. Speckle image needs image processing to detect micro-vascular. This paper proposes a new image processing method for laser speckle, adaptive window method that adaptively processes laser speckle images in the spatial. Conventional fixed window based LASCA has shortcoming in that it uses the same window size regardless of target areas. Inherently laser speckle contains undesired noise. Thus a large window is helpful for removing the noise but it results in low resolution of image. Otherwise a small window may detect micro vascular but it has limits in noise removal. To overcome this trade-off, we newly introduce the concept of adaptive window method to conventional laser speckle image analysis. We have compared conventional LASCA and its variants with the proposed method in terms of image quality and processing complexity.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.493-497
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• 2016

Infrared (IR) imaging is capable of detecting targets that are not visible at night, thus it has been widely used for the security and defense system. However, the quality of the IR image is often degraded by low resolution and noise corruption. This paper addresses target segmentation with the IR image. Multiple regions of interest (ROI) are extracted by the multi-level segmentation and targets are segmented from the individual ROI. Each level of the multi-level segmentation is composed of a k-means clustering algorithm an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering algorithm initializes the parameters of the Gaussian mixture model (GMM) and the EM algorithm iteratively estimates those parameters. Each pixel is assigned to one of clusters during the decision. This paper proposes the selection and the merging of the extracted ROIs. ROI regions are selectively merged in order to include the overlapped ROI windows. In the experiments, the proposed method is tested on an IR image capturing two pedestrians at night. The performance is compared with conventional methods showing that the proposed method outperforms others.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.35 no.6
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• pp.164-172
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• 2023

The grain size distribution of sand provides crucial information for understanding coastal erosion and sediment deposition. The commonly used sieve analysis for grain size distribution analysis has limitations such as time-consuming processes and the inability to obtain information about individual particle shapes and colors. In this study, we propose a grain size distribution analysis method using smartphone digital images, which is simpler and more efficient than the sieve analysis method. During the image analysis process, we effectively detect particles from relatively low-resolution smartphone digital images by extracting particle boundaries through image gradient calculation. Using samples collected from four beaches in Gyeongsangbuk-do, we compare and validate the proposed boundary extraction image analysis method with the analysis method that does not extract boundaries, against sieve analysis results. The proposed method shows an average error rate of 8.21% at D₅₀, exhibiting a 65% lower error compared to the method without boundary extraction. Therefore, grain size distribution analysis using smartphone digital images is convenient, efficient, and demonstrated accuracy comparable to sieve analysis.