• Journal of the Korean Society for Precision Engineering
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• v.14 no.5
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• pp.143-149
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• 1997

This paper was undertaken to do image analysis of wear debris on operating condition of the lubricated moving surfaces. This lubricating wear test was performed under different experimental conditions using the wear test device was made in our laboratory and wear testing specimen of the pin on dist type was rubbed in paraffine series base oil, by materials, varying applied load, sliding distance. The four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) to describe wear debris have been developed and are outlined in the paper. A system using such techniques promises to obviate the need for subjective, human interpretation of particle morphology for machine condition monitoring, this to overcome many of the difficulties with current methods and facilitating wider use of wear particle analysis in machine condition monitouing.

• Journal of Cerebrovascular and Endovascular Neurosurgery
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• v.26 no.2
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• pp.119-129
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• 2024

Knowledge of the venous anatomy is essential for appropriately treating dural arteriovenous fistulas (AVFs). It is challenging to determine the overall venous structure despite performing selective angiography for dural AVFs with feeder from multiple selected arteries. This is because only a part of the veins can be observed through the shunt in the selected artery. Therefore, after performing selective angiography of all vessels to understand the approximate venous anatomy, the venous anatomy can be easily understood by closely examining the source image of computed tomographic angiography or magnetic resonance angiography. Through this, it is possible to specify the vein that is to be blocked (target embolization), thereby avoiding extensive blocking of the vein and avoiding various complications. In the case of dural AVF with feeder from single selected artery, if the multiplanar reconstruction image of the three-dimensional rotational computed tomography obtained by performing angiography is analyzed thoroughly, a shunted pouch can be identified. If embolization is performed by targeting this area, unnecessary sinus total packing can be avoided.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.257-274
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• 2021

Although image segmentation is a critical part of object-based analysis of high resolution imagery, there has been lack of studies to evaluate the quality of image segmentation. In this study, we aimed to find practical and effective methods to obtain optimal parameters for image segmentation. Evaluations of image segmentation are divided into unsupervised, supervised, and qualitative visual interpretation methods. Using the multispectral UAV images, sampled from urban and forest over the Incheon Metropolitan City Park, three evaluation methods were compared. In overall, three methods showed very similar results regardless of the computational costs and applicability, although the optimal parameters determined by the evaluations were different between the urban and forest images. There is no single measure that outperforms in the unsupervised evaluation. Any combinations of intra-segment measures (V, COV, WV) and inter-segment measures (MI, BSH, DTNP) provided almost the same results. Although supervised method may be biased by subjective selection of reference data, it can be easily applied to detect object of interest. The qualitative visual interpretation on the segmentation results corresponded with the unsupervised and supervised evaluations.

• Journal of the Korean Society of Radiology
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• v.85 no.4
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• pp.693-704
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• 2024

Artificial intelligence (AI) technology is actively being applied for the interpretation of medical imaging, such as chest X-rays. AI-based software medical devices, which automatically detect various types of abnormal findings in chest X-ray images to assist physicians in their interpretation, are actively being commercialized and clinically implemented in Korea. Several important issues need to be considered for AI-based detection assistant tools to be applied in clinical practice: the evaluation of performance and efficacy prior to implementation; the determination of the target application, range, and method of delivering results; and monitoring after implementation and legal liability issues. Appropriate decision making regarding these devices based on the situation in each institution is necessary. Radiologists must be engaged as medical assessment experts using the software for these devices as well as in medical image interpretation to ensure the safe and efficient implementation and operation of AI-based detection assistant tools.

• Journal of the Korean Society of Clothing and Textiles
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• v.18 no.1
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• pp.3-14
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• 1994

This study is intended to identify the clothing mage as clothing form and dot space variation. This study consists of pre-experiment for setting the space between dot which shows the difference of the image and main experiment and also is made of factorial design for two variables-clothing form(H-line, A-line, V-line, X-line) dot (dot 1.0, dot 2.2, dot 3.4, dot 5.8) Qestionaire consists of 21 semantic differential scale expressing clothing form and .dot. Data is obtained from 50 female students maforing clothing and textile. The results of this study are as follows. 1) The image by the clothing form and the space dot variation is composed 5 factors' attention, activity, attraction, maturity, boldness. 2) The image by variation in clothing form dot has significant differences in attractive and attention factors, especially shows remarkable differences in attention. By choosing narrow space dot for attentive image, broden space dot for attractive image, you would be able to create the image you want. 3) The image by variation in dot clothing form has almost signific and differences in all factors, especially shows remarkable differences in activity. By choosing A-line for active image, V-line for mature and fashionable image, X-line for attractive, pretty and delicate mage, you would be able to create the image you want. 4) The interaction effect between clothing form and space dot was in attraction and maturity factor, V-line, X-line, dot 3.4 and dot 5.8 intensify attractive image, V-line and dot 1.0 mature image, X-line and dot 5.8 young image.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.727-731
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• 2003

ASTER image has some advantages for classification such as 15 spectral bands and 15m ${\sim}$ 90m spatial resolution. However, in the classification using general remote sensing image, shadow areas are often classified into water area. It is very difficult to divide shadow and water. Because reflectance characteristics of water is similar to characteristics of shadow. Many land cover items are consisted in one pixel which is 15m spatial resolution. Nowadays, very high resolution satellite image (IKONOS, Quick Bird) and Digital Surface Model (DSM) by air borne laser scanner can also be used. In this study, mixed pixel analysis of ASTER image has carried out using IKONOS image and DSM. For mixed pixel analysis, high accurated geometric correction was required. Image matching method was applied for generating GCP datasets. IKONOS image was rectified by affine transform. After that, one pixel in ASTER image should be compared with corresponded 15×15 pixel in IKONOS image. Then, training dataset were generated for mixed pixel analysis using visual interpretation of IKONOS image. Finally, classification will be carried out based on Linear Mixture Model. Shadow extraction might be succeeded by the classification. The extracted shadow area was validated using shadow image which generated from 1m${\sim}$2m spatial resolution DSM. The result showed 17.2% error was occurred in mixed pixel. It might be limitation of ASTER image for shadow extraction because of 8bit quantization data.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.2
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• pp.172-180
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• 2008

PET/CT fused image with anatomical and functional information have improved medical diagnosis and interpretation. This fusion has resulted in more precise localization and characterization of sites of radio-tracer uptake. However, a motion during whole-body imaging has been recognized as a source of image quality degradation and reduced the quantitative accuracy of PET/CT study. The respiratory motion problem is more challenging in combined PET/CT imaging. In combined PET/CT, CT is used to localize tumors and to correct for attenuation in the PET images. An accurate spatial registration of PET and CT image sets is a prerequisite for accurate diagnosis and SUV measurement. Correcting for the spatial mismatch caused by motion represents a particular challenge for the requisite registration accuracy as a result of differences in PET/CT image. This paper provides a brief summary of the materials and methods involved in multiple investigations of the correction for respiratory motion in PET/CT imaging, with the goal of improving image quality and quantitative accuracy.

• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.243-252
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• 2005

Wavelet transform and its inverse processing provide the effective framework for data fusion. The purpose of this study is to investigate applicability of wavelet transform using texture images for the urban remote sensing application. We tried several experiments regarding image fusion by wavelet transform and texture imaging using high resolution images such as IKONOS and KOMPSAT EOC. As for texture images, we used homogeneity and ASM (Angular Second Moment) images according that these two types of texture images reveal detailed information of complex features of urban environment well. To find out the useful combination scheme for further applications, we performed DWT(Discrete Wavelet Transform) and IDWT(Inverse Discrete Wavelet Transform) using texture images and original images, with adding edge information on the fused images to display texture-wavelet information within edge boundaries. The edge images were obtained by the LoG (Laplacian of Gaussian) processing of original image. As the qualitative result by the visual interpretation of these experiments, the resultant image by each fusion scheme will be utilized to extract unique details of surface characterization on urban features around edge boundaries.

• Smart Structures and Systems
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• v.30 no.4
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• pp.385-395
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• 2022

Detecting cracks on a concrete structure is crucial for structural maintenance, a crack being an indicator of possible damage. Conventional crack detection methods which include visual inspection and non-destructive equipment, are typically limited to a small region and require time-consuming processes. Recently, to reduce the human intervention in the inspections, various researchers have sought computer vision-based crack analyses: One class is filter-based methods, which effectively transforms the image to detect crack edges. The other class is using deep-learning algorithms. For example, convolutional neural networks have shown high precision in identifying cracks in an image. However, when the objective is to classify not only the existence of crack but also the types of cracks, only a few studies have been reported, limiting their practical use. Thus, the presented study develops an image processing procedure that detects cracks and classifies crack types; whether the image contains a crazing-type, single crack, or multiple cracks. The properties and steps in the algorithm have been developed using field-obtained images. Subsequently, the algorithm is validated from additional 227 images obtained from an open database. For test datasets, the proposed algorithm showed accuracy of 92.8% in average. In summary, the developed algorithm can precisely classify crazing-type images, while some single crack images may misclassify into multiple cracks, yielding conservative results. As a result, the successful results of the presented study show potentials of using vision-based technologies for providing crack information with reduced human intervention.

• Economic and Environmental Geology
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• v.53 no.4
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• pp.479-489
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• 2020

A fault is a geological structure that can be a migration path or a cap rock of hydrocarbon such as oil and gas, formed from source rock. The fault is one of the main targets of seismic exploration to find reservoirs in which hydrocarbon have accumulated. However, conventional fault detection methods using lateral discontinuity in seismic data such as semblance, coherence, variance, gradient magnitude and fault likelihood, have problem that professional interpreters have to invest lots of time and computational costs. Therefore, many researchers are conducting various studies to save computational costs and time for fault interpretation, and machine learning technologies attracted attention recently. Among various machine learning technologies, many researchers are conducting fault interpretation studies using the support vector machine, multi-layer perceptron, deep neural networks and convolutional neural networks algorithms. Especially, researchers use not only their own convolution networks but also proven networks in image processing to predict fault locations and fault information such as strike and dip. In this paper, by investigating and analyzing these studies, we found that the convolutional neural networks based on the U-Net from image processing is the most effective one for fault detection and interpretation. Further studies can expect better results from fault detection and interpretation using the convolutional neural networks along with transfer learning and data augmentation.