• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.4_1
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• pp.319-326
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• 2014

The accurate geo-referencing processes that apply ground control points is prerequisite for effective end use of HRSI (High-resolution satellite imagery). Since the conventional control point acquisition by human operator takes long time, demands for the automated matching to existing reference data has been increasing its popularity. Among many options of reference data, the airborne LiDAR (Light Detection And Ranging) data shows high potential due to its high spatial resolution and vertical accuracy. Additionally, it is in the form of 3-dimensional point cloud free from the relief displacement. Recently, a new matching method between LiDAR data and HRSI was proposed that is based on the image projection of whole LiDAR data into HRSI domain, however, importing and processing the large amount of LiDAR data considered as time-consuming. Therefore, we wmotivated to ere propose a local LiDAR chip generation for the HRSI geo-referencing. In the procedure, a LiDAR point cloud was rasterized into an ortho image with the digital elevation model. After then, we selected local areas, which of containing meaningful amount of edge information to create LiDAR chips of small data size. We tested the LiDAR chips for fully-automated geo-referencing with Kompsat-2 and Kompsat-3 data. Finally, the experimental results showed one-pixel level of mean accuracy.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.2
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• pp.109-114
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• 2012

Honeycombs are dense structures that small cysts, which generally have about 2~10 mm in diameter, are surrounded by the wall of fibrosis. When honeycomb is found in the patients, the incidence of acute exacerbation is generally very high. Thus, the observation and quantitative measurement of honeycomb are considered as a significant marker for clinical diagnosis. In this point of view, we propose an automatic segmentation method using morphological image processing and assessment of the degree of clustering techniques. Firstly, image noises were removed by the Gaussian filtering and then a morphological dilation method was applied to segment lung regions. Secondly, honeycomb cyst candidates were detected through the 8-neighborhood pixel exploration, and then non-cyst regions were removed using the region growing method and wall pattern testing. Lastly, final honeycomb regions were segmented through the extraction of dense regions which are consisted of two or more cysts using cluster analysis. The proposed method applied to 80 High resolution computed tomography (HRCT) images and achieved a sensitivity of 89.4% and PPV (Positive Predictive Value) of 72.2%.

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.327-336
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• 2004

For visual inspection system in real industrial environment, it is one of most important tasks to design fast and stable pattern matching algorithm. This paper presents an edge-based point correlation algorithm avoiding full search in visual inspection system. Conventional algorithms based on NGC(normalized gray-level correlation) have to overcome some difficulties for applying to automated inspection system in factory environment. First of all, NGC algorithms need high time complexity and thus high performance hardware to satisfy real-time process. In addition, lighting condition in realistic factory environments if not stable and therefore intensity variation from uncontrolled lights gives many roubles for applying directly NGC as pattern matching algorithm in this paper, we propose an algorithm to solve these problems from using thinned and binarized edge data and skipping full point search with edge-map analysis. A point correlation algorithm with the thinned edges is introduced with image pyramid technique to reduce the time complexity. Matching edges instead of using original gray-level pixel data overcomes NGC problems and pyramid of edges also provides fast and stable processing. All proposed methods are preyed from experiments using real images.

• Journal of Radiation Industry
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• v.10 no.4
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• pp.219-225
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• 2016

The apron is one of the essential protectors to reduce the exposure dose of radiological technologists. This study is to provide a guideline for purchasing the aprons with excellent performance and to help reducing the exposure dose by measuring the shielding ration and uniformity of aprons according to lead equivalent and form types. The shielding ratio of aprons were measured by using radiation generator and dosimeter. Exposure conditions were 81 kVp, 25 mAs, source to image receptor distance (SID) 100 cm and field of view (FOV) $17^{{\prime}{\prime}}{\times}17^{{\prime}{\prime}}$. Exposure areas for front type and around type aprons were divided into 9 areas and for 2 pieces type aprons were divided into 3 areas of top and 4 areas of skirt. The uniformity of aprons were measured by using fluoroscopy and Image J. The 4 regions of interest (ROI) were set into acquired images and measured uniformity by measuring the standard deviation of pixel intensity in ROIs. In continuous shielding ration measurement of aprons according to exposure area, there was not statistical significance (P>0.05). In ANOVA test of aprons, there was statistical significance (P<0.01). In the results of sheilding ratio, although the aprons had equal lead equivalent, there were difference in shielding ratio from 83.59% to 98.15%. In the results of uniformity, the front type aprons with equal lead equivalent indicated the similar uniformity. However, the around type and 2 pieces type apron with equal lead equivalent indicated the different uniformity each other, from 1.8 to 22.2. If the performance evaluation in this study were conducted regularly before and after purchase the aprons, the exposure does to patients and radiological technologists could be reduced.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.768-779
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• 2018

In this paper, we propose a novel video quality function for video processing based on MSSSIM to select an appropriate video processing filter and to accommodate multiple processing filters to each pixel block in a picture frame by a mathematical selection law so as to maintain video quality and to reduce the bitrate of compressed video. In viewpoint of video compression, since the properties of video quality and bitrate is different for each picture of video frames and for each areas in the same frame, it is difficult for the video filter with single property to satisfy the object of increasing video quality and decreasing bitrate. Consequently, to maintain the subjective video quality in spite of decreasing bitrate, we propose the methodology about the MSSSIM as the measure of subjective video quality, the KL-Divergence as the measure of bitrate, and the combination method of those two measurements. Moreover, using the proposed combinatorial measurement, when we use the multiple image filters with mutually different properties as a pre-processing filter for video, we can verify that it is possible to compress video with maintaining the video quality under decreasing the bitrate, as possible.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.367-378
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• 2021

Field compost is a representative non-point pollution source for livestock. If the field compost flows into the water system due to rainfall, nutrients such as phosphorus and nitrogen contained in the field compost can adversely affect the water quality of the river. In this paper, we propose a method for detecting field compost using unmanned aerial vehicle images and deep learning-based semantic segmentation. Based on 39 ortho images acquired in the study area, about 30,000 data were obtained through data augmentation. Then, the accuracy was evaluated by applying the semantic segmentation algorithm developed based on U-net and the filtering technique of Open CV. As a result of the accuracy evaluation, the pixel accuracy was 99.97%, the precision was 83.80%, the recall rate was 60.95%, and the F1-Score was 70.57%. The low recall compared to precision is due to the underestimation of compost pixels when there is a small proportion of compost pixels at the edges of the image. After, It seems that accuracy can be improved by combining additional data sets with additional bands other than the RGB band.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1579-1590
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• 2020

In this paper, spatially-adaptive denormalization (SPADE) based U-Net is proposed to detect changes by using high-resolution satellite images. The proposed network is to preserve spatial information using SPADE. Change detection methods using high-resolution satellite images can be used to resolve various urban problems such as city planning and forecasting. For using pixel-based change detection, which is a conventional method such as Iteratively Reweighted-Multivariate Alteration Detection (IR-MAD), unchanged areas will be detected as changing areas because changes in pixels are sensitive to the state of the environment such as seasonal changes between images. Therefore, in this paper, to precisely detect the changes of the objects that consist of the city in time-series satellite images, the semantic spatial objects that consist of the city are defined, extracted through deep learning based image segmentation, and then analyzed the changes between areas to carry out change detection. The semantic objects for analyzing changes were defined as six classes: building, road, farmland, vinyl house, forest area, and waterside area. Each network model learned with KOMPSAT-3A satellite images performs a change detection for the time-series KOMPSAT-3 satellite images. For objective assessments for change detection, we use F1-score, kappa. We found that the proposed method gives a better performance compared to U-Net and UNet++ by achieving an average F1-score of 0.77, kappa of 77.29.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.427-434
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• 2022

The purpose of this study is to create a self-portrait that provides an immersive experience that immerses the viewer into the problem of self-awareness. We propose a method to implement an interactive self-portrait by using audio and image information obtained from viewers. The viewer's voice information is converted into text and visualized. In this case, the viewer's face image is used as pixel information composing the text. Text is the result of a mixture of one's own emotions, imaginations, and intentions based on personal experiences and memories. People have different interpretations of certain texts in different ways.The proposed digital self-portrait not only reproduces the viewer's self-consciousness in the inner aspect by utilizing the intertextuality of the text, but also expands the meanings inherent in the text. Intertextuality in a broad sense refers to the totality of all knowledge that occurs between text and text, and between subject and subject. Therefore, the self-portrait expressed in text expands and derives various relationships between the viewer and the text, the viewer and the viewer, and the text and the text. In addition, this study shows that the proposed self-portrait can confirm the formativeness of text and re-create spatial and temporality in the external aspect. This dynamic self-portrait reflects the interests of viewers in real time, and has the characteristic of being updated and created.

• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.31-36
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• 2023

Among imaging and treatment devices for small animals, positron emission tomography(PET) causes a change in spatial resolution within a field of view. This is a phenomenon caused by using a small gantry and a thin and long scintillation pixel, and detectors that measure the interaction depth are being developed and researched to solve this problem. In this study, a detector that measures the interaction depth was designed using several scintillator blocks and light guides with different reflector patterns. The scintillator block composed of 4 × 4 arrays of 3 mm × 3 mm × 5 mm scintillation pixels formed four layers, and a light guide was inserted in each layer to configure the entire detector. In order to check whether the interaction depth was measured, a gamma ray interaction was generated at the center of all scintillation pixels to acquire data and then reconstructed into a flood image. The reflector patterns of the light guides inserted between the layers were all different, so the positions of the scintillation pixels for each layer were formed in different locations. It is considered that even spatial resolution can be achieved over all regions of the field of view if all positions of the scintillation pixels thus formed are separated and used for image reconstruction.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.1
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• pp.15-22
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• 2022

In order to improve the accuracy of the deep learning object detection technique, the effect of magnification rate conditions and seasonal factors on detection accuracy in aerial photographs and drone images was analyzed through experiments. Among the deep learning object detection techniques, Mask R-CNN, which shows fast learning speed and high accuracy, was used to detect the vehicle to be detected in pixel units. Through Seoul's aerial photo service, learning images were captured at different screen magnifications, and the accuracy was analyzed by learning each. According to the experimental results, the higher the magnification level, the higher the mAP average to 60%, 67%, and 75%. When the magnification rates of train and test data of the data set were alternately arranged, low magnification data was arranged as train data, and high magnification data was arranged as test data, showing a difference of more than 20% compared to the opposite case. And in the case of drone images with a seasonal difference with a time difference of 4 months, the results of learning the image data at the same period showed high accuracy with an average of 93%, confirming that seasonal differences also affect learning.