• The Journal of Korean Institute of Communications and Information Sciences
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• v.37C no.9
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• pp.751-756
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• 2012

In this paper, we propose a foreground extraction and depth map generation method using a time-of-flight (TOF) depth camera. Although, the TOF depth camera captures the scene's depth information in real-time, it has a built-in noise and distortion. Therefore, we perform several preprocessing steps such as image enhancement, segmentation, and 3D warping, and then use the TOF depth data to generate the depth-discontinuity regions. Then, we extract the foreground object and generate the depth map as of the color image. The experimental results show that the proposed method efficiently generates the depth map even for the object boundary and textureless regions.

• Korean Journal of Bronchoesophagology
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• v.17 no.1
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• pp.14-18
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• 2011

The diagnosis and treatment of early gastrointestinal cancers is the gastroenterologists' mission because of national cancer screening program in South Korea. The detection of early cancers is emphasized, because these were previously treated with surgical treatment can be currently cured with endoscopic treatment. Gastroenterologists who achieved at least on some level can make an exact diagnosis regardless of what type of endoscopy, but generally, there are some required conditions for an optimal diagnosis. First, clinically important lesions have to be detected easily. Second, the border and morphology of lesions have to be characterized easily. Third, lesions have to be diagnosed exactly. Precancers and early cancers are often subtle and can pose a challenge to gastroenterologists to visualize using standard white light endoscopy. The use of dye solutions aids the diagnosis of early gastrointestinal cancers, however, it is a quite cumbersome to use dye solutions all the time and the solution often bothers the exact observation by pooling into the depression or ulceration of the lesion. To overcome this weakness, newer endoscopes are now developed so called "image enhanced endoscopy" using optical and/or electronic methods such as narrow band imaging (NBI), autofluorescence imaging (AFI), i-scan, flexible spectral imaging color enhancement (FICE) and confocal endomicroscopy (CLE).

• Journal of the Korea Computer Graphics Society
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• v.25 no.3
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• pp.123-131
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• 2019

In this study, we modified CT images of femoral head in consideration of anatomically meaningful structure, proposing the method to augment the training data of convolution Neural network for segmentation of femur mesh model. First, the femur mesh model is obtained from the CT image. Then divide the mesh model into meaningful parts by using cluster analysis on geometric characteristic of mesh surface. Finally, transform the segments by using an appropriate mesh deformation algorithm, then create new CT images by warping CT images accordingly. Deep learning models using the data enhancement methods of this study show better image division performance compared to data augmentation methods which have been commonly used, such as geometric conversion or color conversion.

• Journal of Navigation and Port Research
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• v.36 no.8
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• pp.651-657
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• 2012

In the Busan North port redevelopment project, which is the first large scale port redevelopment project in this country, it adopted the national landscape plan for competitiveness of port city. Furthermore, it includes many efforts on landscape formation in the process of improvement of ocean city image and enhancement of its value, which fits for the situation and characteristics of the original nature, history, and culture resources, together with all facilities, structures, symbols, and eco green belt. Firstly this study attempted to analyze the characteristics of Busan landscape, Busan city design plan(2010), especially coloring plan, together with the case study of landscape color plan for foreign countries. From the basis of this analysis, it was proposed a landscape enhancement strategy in the Busan North port redevelopment project through the coloring plan and it is expected that the result would be used for the basic data for other redevelopment project.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.3
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• pp.180-190
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• 2017

This paper describes the application of Multi-frame based super-resolution method to enhance resolution of image information from the UAV, and the improvement of UAV's ground target recognition ability. To verify this algorithm, we designed a flight/ground control system, and the UAV, and then the algorithm was validated using the UAV system with ground target. As a result of the comparison between the pre-applied image and post-applied one shows that the RMSE is from 0.0677 to 0.0315, NRMSE is from 7.4030% to 3.5726%, PSNR is from 23.3885dB to 30.0036dB, and SSIM is from 0.6996 to 0.8948. Through these results, we validate this study can enhance the resolution of UAV's image using Multi-frame based super-resolution algorithm.

• Journal of Digital Contents Society
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• v.18 no.4
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• pp.777-785
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• 2017

In the modern real world, we can extract and recognize some texts to get a lot of information from the scene containing them, so the techniques for extracting and recognizing text areas from a scene are constantly evolving. They can be largely divided into texture-based method, connected component method, and mixture of both. Texture-based method finds and extracts text based on the fact that text and others have different values such as image color and brightness. Connected component method is determined by using the geometrical properties after making similar pixels adjacent to each pixel to the connection element. In this paper, we propose a method to adaptively change to improve the accuracy of text region extraction, detect and correct the slope of the image using edge and image segmentation. The method only extracts the exact area containing the text by correcting the slope of the image, so that the extracting rate is 15% more accurate than MSER and 10% more accurate than EEMSER.

• 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.

• Journal of Yeungnam Medical Science
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• v.22 no.1
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• pp.43-51
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• 2005

Background: To examine the ultrasonographic and magnetic resonance (MRI) imaging findings of a cervical mass type cervical pregnancy. Materials and Methods: The ultrasonographic and MRI findings of 5 patients pathologically confirmed as having a cervical pregnancy were analyzed retrospectively. On ultrasonography, the size and echo pattern of the uterine cervix, the shape and echo pattern of the lesion, the degree and the pattern of blood flow on the color Doppler study and the spectral Doppler pattern were analyzed. The shape, signal intensity, and degree and pattern of enhancement of the lesion were evaluated on MRI. Results: The uterine cervix was enlarged and the size of the lesion was 6.1 to 7.1 (average, 6.5) cm. The endocervical canal was irregularly dilated and showed heterogeneous echogenicity in all 5 cases. Four of the 5 lesions were heterogeneously hyper- or mixed echoic and remaining one was relatively homogeneous echogenic. Doppler ultrasonography revealed an increased vascularity of the peritrophoblastic flow pattern. In all 4 cases where MRI performed, the lesion was irregular in shape and the margin was not sharply demarcated. The T2-weighed image showed that the lesions were mixed signal intensity. Three of the 4 lesions contained high signal intensity nodular portions and a low signal intensity rim was observed along the margin of the nodular portions. The T1-weighted image revealed multiple signal voids along the periphery of the lesions and high signal intensity portions as a result of hemorrhage were noted. The dynamic enhanced study showed that the high signal intensity portions on the T2-weighted image were strongly enhanced similar to the vessels on the early phase and the contrast enhancement gradually decreased with time. Conclusion: A cervical mass type cervical pregnancy can be correctly diagnosed using the patient's clinical symptom, the elevation in the serum ${\beta}$-HCG level, and characteristic ultrasonographic and MRI findings.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.637-644
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• 2021

In general, in the fields of computer vision, robotics, and augmented reality, the importance of 3D space and 3D object detection and recognition technology has emerged. In particular, since it is possible to acquire RGB images and depth images in real time through an image sensor using Microsoft Kinect method, many changes have been made to object detection, tracking and recognition studies. In this paper, we propose a method to improve the quality of 3D reconstructed images by processing images acquired through a depth-based (RGB-Depth) camera on a multi-view camera system. In this paper, a method of removing noise outside an object by applying a mask acquired from a color image and a method of applying a combined filtering operation to obtain the difference in depth information between pixels inside the object is proposed. Through each experiment result, it was confirmed that the proposed method can effectively remove noise and improve the quality of 3D reconstructed image.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.101-108
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• 2024

AI-OCR (Artificial Intelligence Optical Character Recognition) combines OCR technology with Artificial Intelligence to overcome limitations that required human intervention. To enhance the performance of AI-OCR, training on diverse data sets is essential. However, the recognition rate declines when image colors have similar brightness levels. To solve this issue, this study employs Homomorphic filtering as a preprocessing step to clearly differentiate color levels, thereby increasing text recognition rates. While Homomorphic filtering is ideal for text extraction because of its ability to adjust the high and low frequency components of an image separately using a gamma value, it has the downside of requiring manual adjustments to the gamma value. This research proposes a range for gamma threshold values based on tests involving image contrast, brightness, and entropy. Experimental results using the proposed range of gamma values in Homomorphic filtering suggest a high likelihood for effective AI-OCR performance.