• Journal of Broadcast Engineering
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• v.18 no.2
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• pp.225-236
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• 2013

3D contents could cause unique 3D visual fatigue. Screen disparity, image blurring, and crosstalk are known to be the three major factors responsible for the fatigue. Among these, screen disparity and image blurring are content factors, that is, one can directly manipulate contents themselves to handle visual fatigue caused by these two factors. On the other hand, because crosstalk is closely tied to physical characteristics of 3D display, it is difficult or even impossible to reduce crosstalk-driven visual fatigue unless one replaces 3D display itself (for example, from active to passive display). However, the effects of crosstalk on 3D visual fatigue depends on visual stimulus features (that is, contents), and thus it is possible to manipulate stimulus features in order to handle visual fatigue caused by crosstalk. Hence, this research tested the effects of visual stimulus features on crosstalk (which then causes 3D visual fatigue). Using relative depth discrimination task, we tested the effects of stimulus-background contrast, background texture density, and screen disparity on the degree of perceived crosstalk. The results showed that crosstalk decreases with presence of background texture and with less degree of screen disparity.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.4
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• pp.87-98
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• 2011

Detecting a moving object from videos and tracking it are basic and necessary preprocessing steps in many video systems like object recognition, context aware, and intelligent visual surveillance. In this paper, we propose a method that is able to detect a moving object quickly and accurately in a condition that background and light change in a real time. Furthermore, our system detects strongly an object in a condition that the target object is covered with other objects. For effective detection, effective Eigen-space and FCM are combined and employed, and a CONDENSATION algorithm is used to trace a detected object strongly. First, training data collected from a background image are linear-transformed using Principal Component Analysis (PCA). Second, an Eigen-background is organized from selected principal components having excellent discrimination ability on an object and a background. Next, an object is detected with FCM that uses a convolution result of the Eigen-vector of previous steps and the input image. Finally, an object is tracked by using coordinates of an detected object as an input value of condensation algorithm. Images including various moving objects in a same time are collected and used as training data to realize our system that is able to be adapted to change of light and background in a fixed camera. The result of test shows that the proposed method detects an object strongly in a condition having a change of light and a background, and partial movement of an object.

• Journal of the Korean Society of Marine Environment & Safety
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• v.14 no.2
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• pp.163-168
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• 2008

Ship detection from satellite remote sensing is a crucial application for global monitoring for the purpose of protecting the marine environment and ensuring marine security. It permits to monitor sea traffic including fisheries, and to associate ships with oil discharge. An automatic ship detection approach for RADARSAT Fine Synthetic Aperture Radar (SAR) image is described and assessed using in situ ship validation information collected during field experiments conducted on August 6, 2004. Ship detection algorithms developed here consist of five stages: calibration, land masking, prescreening, point positioning, and discrimination. The fine image was acquired of Ulsan Port, located in southeast Korea, and during the acquisition, wind speeds between 0 m/s and 0.4 m/s were reported. The detection approach is applied to anchoring ships in the anchorage area of the port and its results are compared with validation data based on Vessel Traffic Service (VTS) radar. Our analysis for anchoring ships, above 68 m in length (LOA), indicates a 100% ship detection rate for the RADARSAT single beam mode. It is shown that the ship detection performance of SAR for smaller ships like barge could be higher than the land-based radar. The proposed method is also applied to estimate the ship's dimensions of length and breadth from SAR radar cross section(RCS), but those values were comparatively higher than the actual sizes because of layover and shadow effects of SAR.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.6
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• pp.1161-1175
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• 2018

An unexpected event could be easily followed by a large secondary accident due to the limitation in sight of drivers in road tunnels. Therefore, a series of automated incident detection systems have been under operation, which, however, appear in very low detection rates due to very low image qualities on CCTVs in tunnels. In order to overcome that limit, deep learning based tunnel incident detection system was developed, which already showed high detection rates in November of 2017. However, since the object detection process could deal with only still images, moving direction and speed of moving vehicles could not be identified. Furthermore it was hard to detect stopping and reverse the status of moving vehicles. Therefore, apart from the object detection, an object tracking method has been introduced and combined with the detection algorithm to track the moving vehicles. Also, stopping-reverse discrimination algorithm was proposed, thereby implementing into the combined incident detection processes. Each performance on detection of stopping, reverse driving and fire incident state were evaluated with showing 100% detection rate. But the detection for 'person' object appears relatively low success rate to 78.5%. Nevertheless, it is believed that the enlarged richness of image big-data could dramatically enhance the detection capacity of the automatic incident detection system.

• The Journal of Society for e-Business Studies
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• v.26 no.2
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• pp.19-43
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• 2021

The prediction of box office performance in performing arts institutions is an important issue in the performing arts industry and institutions. For this, traditional prediction methodology and data mining methodology using standardized data such as cast members, performance venues, and ticket prices have been proposed. However, although it is evident that audiences tend to seek out their intentions by the performance guide poster, few attempts were made to predict box office performance by analyzing poster images. Hence, the purpose of this study is to propose a deep learning application method that can predict box office success through performance-related poster images. Prediction was performed using deep learning algorithms such as Pure CNN, VGG-16, Inception-v3, and ResNet50 using poster images published on the KOPIS as learning data set. In addition, an ensemble with traditional regression analysis methodology was also attempted. As a result, it showed high discrimination performance exceeding 85% of box office prediction accuracy. This study is the first attempt to predict box office success using image data in the performing arts field, and the method proposed in this study can be applied to the areas of poster-based advertisements such as institutional promotions and corporate product advertisements.

• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.575-583
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• 2022

Magnetic resonance diffusion tensor imaging (DTI) has revealed the disruption of brain white matter microstructure in normal aging and alcoholism undetectable with conventional structural MR imaging. we plan to analyze the FA measurements of the ROI of dangerous drinkers selected from Alcohol Use Disorders Identification Test (AUDIT) and Tract-Based Spatial Statics (TBSS) tool was used to extract FA values in the ROI from the image acquired through the pre-processing process. TBSS has a higher sensitivity of the FA value and MD value in the white matter than the brain gray matter, and has the advantage of quantitatively deriving the unlimited degree of brain nerve fibers, and more specialized in the brain white matter. We plan to analyze the fractional anisotropy (FA) measurement value for damage by selecting the center of the anatomical structure of the white matter region of the brain with high anisotropy among the brain neural networks that are particularly vulnerable to alcohol as the region of interest (ROI). In this study, we expected that alcohol causes damage to the brain white matter microstructure from FA value in various areas including both Choroid plexus. Especially, In the case of the moderate drunker, the mean value of FA in Lt, Rt. Choroid plexus was 0.2831 and 0.2872, whereas, in the case of the severe drunker, the mean value of FA was 0.1972 and 0.1936. We found that the higher the score on the AUDIT scale, the lower the FA value in ROI region of the brain white matter. Using the AUDIT scale, the guideline for the FA value of DTI can be presented, and it is possible to select a significant number of potentially severe drinkers. In other words, AUDIT was proved as useful tool in screening and discrimination of severe drunker through DTI.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.41-46
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• 2023

Human urine is a process of excreting waste products in the blood, and it is easy to collect and contains various substances. Urinalysis is used to check for diseases, health conditions, and urinary tract infections. There are three methods of urinalysis: physical property test, chemical test, and microscopic test, and chemical test results can be easily confirmed using urine test strips. A variety of items can be tested on the urine test strip, through which various diseases can be identified. Recently, with the spread of smart phones, research on reading urine test strips using smart phones is being conducted. There is a method of detecting and reading the color change of a urine test strip using a smartphone. This method uses the RGB values and the color difference formula to discriminate. However, there is a problem in that accuracy is lowered due to various environmental factors. This paper applies a deep learning model to solve this problem. In particular, color discrimination of a urine test strip is improved in a smartphone using a lightweight CNN (Convolutional Neural Networks) model. CNN is a useful model for image recognition and pattern finding, and a lightweight version is also available. Through this, it is possible to operate a deep learning model on a smartphone and extract accurate urine test results. Urine test strips were taken in various environments to prepare deep learning model training images, and a urine test service application was designed using MobileNet V3.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.683-690
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• 2009

The purpose of this study is to evaluate the usefulness of high-b-values diffusion weighted magnetic resonance imaging for the preoperative detection of focal rectum cancers. 60patients with diffusion weighted imaging were evaluated for the presence of rectal cancers. Forty were male and twenty were female, and their ages ranged from 38 to 71 (mean, 56) years. Used equipment was 1.5Tesla MRI((GE, General Electric Medical System, Excite HD). Examination protocols were used the fast spin echo T2, T1 weighted imaging. All examination protocols were performed by the same location with diffusion weighted imaging for accuracy detection. The b-values used in DWI were 250, 500, 750, 1000. 1500, 2000$(s/mm^2)$. The rectum, bladder to tumor contrast-to-noise ratio (CNR) of MR images were quantitativlely analyzed using GE software Functool tool, four experienced radiologists and three radiotechnologists qualitatively evaluated image quality in terms of image artifacts, lesion conspicuity and rectal wall. These data were analysed by using ANOVA and Freedman test with each b-value(p<0.05). Contrast to noise ratio of rectum, bladder and tumor in b-value 1000 were 27.21, 24.44, respectively(p<0.05) and aADC value was $0.73\times10^{-3}$. As a qualitative analysis, the conspicuity and discrimination from the rectal wall of lesions were high results as $4.0\pm0.14$, $4.4\pm0.16$ on b-value 1000(p<0.05), image artifacts were high results as $4.8\pm0.25$ on b-value 2000(p<0.05). In conclusion, DWI was provided useful information with depicting the pre-operative detection of rectal cancers, High-b-value 1000 image was the most excellent DWI value.

• Investigative Magnetic Resonance Imaging
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• v.1 no.1
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• pp.119-124
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• 1997

Purpose: To assess the utility of magnetic resonance(MR) cerebral blood volume (CBV) map in the evaluation of brain tumors. Materials and Methods: We performed perfusion MR imaing preoperatively in the consecutive IS patients with intracranial masses(3 meningiomas, 2 glioblastoma multiformes, 3 low grade gliomas, 1 lymphoma, 1 germinoma, 1 neurocytoma, 1 metastasis, 2 abscesses, 1 radionecrosis). The average age of the patients was 42 years (22yr -68yr), composed of 10 males and S females. All MR images were obtained at l.ST imager(Signa, CE Medical Systems, Milwaukee, Wisconsin). The regional CBV map was obtained on the theoretical basis of susceptibility difference induced by first pass circulation of contrast media. (contrast media: IScc of gadopentate dimeglumine, about 2ml/sec by hand, starting at 10 second after first baseline scan). For each patient, a total of 480 images (6 slices, 80 images/slice in 160 sec) were obtained by using gradient echo(CE) single shot echo-planar image(EPI) sequence (TR 2000ms, TE SOms, flip angle $90^{\circ}$, FOV $240{\times}240mm,{\;}matrix{\;}128{\times}128$, slice-thick/gap S/2.S). After data collection, the raw data were transferred to CE workstation and rCBV maps were generated from the numerical integration of ${\Delta}R2^{*} on a voxel by voxel basis, with home made software (${\Delta}R2^{*}=-ln (S/SO)/TE). For easy visual interpretation, relative RCB color coding with reference to the normal white matter was applied and color rCBV maps were obtained. The findings of perfusion MR image were retrospectively correlated with Cd-enhanced images with focus on the degree and extent of perfusion and contrast enhancement. Results: Two cases of glioblastoma multiforme with rim enhancement on Cd-enhanced Tl weighted image showed increased perfusion in the peripheral rim and decreased perfusion in the central necrosis portion. The low grade gliomas appeared as a low perfusion area with poorly defined margin. In 2 cases of brain abscess, the degree of perfusion was similar to that of the normal white matter in the peripheral enhancing rim and was low in the central portion. All meningiomas showed diffuse homogeneous increased perfusion of moderate or high degree. One each of lymphoma and germinoma showed homogenously decreased perfusion with well defined margin. The central neurocytoma showed multifocal increased perfusion areas of moderate or high degree. A few nodules of the multiple metastasis showed increased perfusion of moderate degree. One radionecrosis revealed multiple foci of increased perfusion within the area of decreased perfusion. Conclusion: The rCBV map appears to correlate well with the perfusion state of brain tumor, and may be helpful in discrimination between low grade and high grade gliomas. The further study is needed to clarify the role of perfusion MR image in the evaluation of brain tumor.

• The Korean Journal of Nuclear Medicine Technology
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• v.12 no.1
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• pp.44-48
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• 2008

Purpose: In FDG-PET/CT of breast cancer, a sensitivity was 80~96% and a specificity was 75~95% commonly. It was valuable to identify a cancer in early stage been difficult in Mammography. Most of the PET/CT scans have been examined on supine position, so, the image of breast has been acquired by reconstructed whole body scan image. However, using prone position with a compensator, a shape of breast was reassembly shown to be real by gravity. Therefore, the purpose of this study was to evaluate diagnostic value of prone position in FDG PET-CT of breast cancer. Materials and Methods: 30 female patients with doubtful or positive breast cancer were examined. The PET-CT whole body scan was acquired at 60 minutes after $^{18}F$-FDG injection on Supine position. Then, regional breast spot scan was progressed on prone position using a compensator. Each image was evaluated by physicians blinded to patient's data, and statistical analysis did through SUVs measured in PET-CT images. Results: In 27 of 30 patients, prone position was shown accurate discrimination and diagnostic value, but in another 3 patients had a lesion 1cm below, PET-CT couldn't detect it, unlike MRI. Consequently, prone position distinguished a lesion better than Supine position, because of low degree of metamorphosis by gravity. The SUVs analysis of each position was significant (p value=0.004). Conclusion: In PET-CT of breast cancer, prone position could detect micrometastasis as well as primary lesion, better than supine position. Therefore, this study proposes that any technical change considered morphological feature like prone position can offer adequate and useful diagnostic information, together with complementary quantitative analysis.