• Journal of Drive and Control
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• v.18 no.4
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• pp.9-18
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• 2021

This study proposes a method for creating learning datasets to recognize obstacles using deep learning algorithms in automated construction machinery or an autonomous vehicle. Recently, many researchers and engineers have developed various recognition algorithms based on deep learning following an increase in computing power. In particular, the image classification technology and image segmentation technology represent deep learning recognition algorithms. They are used to identify obstacles that interfere with the driving situation of an autonomous vehicle. Therefore, various organizations and companies have started distributing open datasets, but there is a remote possibility that they will perfectly match the user's desired environment. In this study, we created an interface of the virtual simulator such that users can easily create their desired training dataset. In addition, the customized dataset was further advanced by using the RDBMS system, and the recognition rate was improved.

• Journal of Broadcast Engineering
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• v.22 no.6
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• pp.850-853
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• 2017

In this paper, we propose an image analysis system based on omnidirectional image and object tracking image display using super wide angle camera. In order to generate spherical images, the projection process of converting from two wide-angle images to the equirectangular panoramic image was performed and the spherical image was expressed by converting rectangular to spherical coordinate system. Object tracking was performed by selecting the desired object initially, and KCF(Kernelized Correlation Filter) algorithm was used so that robust object tracking can be performed even when the object's shape is changed. In the initial dialog, the file and mode are selected, and then the result is displayed in the new dialog. If the object tracking mode is selected, the ROI is set by dragging the desired area in the new window.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.2
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• pp.70-77
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• 2010

This study proposed an auto focusing method for a multi-focus image in assembling lens modules in digital camera phones. A camera module in a camera phone is composed of a lens barrel, an IR glass, a lens mount, a PCB board and aspheric lenses. Alignment among the components is one of the important factors in product quality. Auto-focus is essential to adjust image quality of an IR glass in a lens holder, but there are two focal points in the captured image due to thickness of IR glass. So, sharpness, probability and a scale factor are defined to find desired focus from a multi-focus image. The sharpness is defined as clarity of an image. Probability and a scale factors are calculated using pattern matching with a registered image. The presented algorithm was applied to a lens assembly machine which has 5 axes, two vacuum chucks and an inspection system. The desired focus can be determined on the local maximum of the sharpness, the probability and the scale factor in the experiment.

• The Journal of the Korea Contents Association
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• v.5 no.6
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• pp.97-108
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• 2005

In this paper a method of reconstructing a desired image through the geometrical transformation and the interpolation techniques is presented by comparing different interpolation schemes. Several different interpolation schemes are compared with respect to the amount of error that is the difference between the original and the reverse-projective transformed images. Higher ordered B-spline interpolation turned to be superior to other techniques in reconstructing the image which is desired to be close to the unskewed image as much as possible. In the results, this paper demonstrates that the reverse projection using the higher ordered B-spline interpolation is superior to those conventional interpolation methods, linear, cubic spline for reconstructing image. In experiments, the error decreases as the order of B-spline increases. The proposed technique is useful for various practical and theoretical applications in the area of satellite, medical, and commercial image processing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2366-2395
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• 2018

Multi-view super-resolution (MVSR) refers to the process of reconstructing a high-resolution (HR) image from a set of low-resolution (LR) images captured from different viewpoints typically by different cameras. These multi-view images are usually obtained by a camera array. In our previous work [1], we super-resolved multi-view LR images via image fusion (IF) and blind deblurring (BD). In this paper, we present a new MVSR method that jointly realizes IF and BD based on an integrated energy function optimization. First, we reformulate the MVSR problem into a multi-channel blind deblurring (MCBD) problem which is easier to be solved than the former. Then the depth map of the desired HR image is calculated. Finally, we solve the MCBD problem, in which the optimization problems with respect to the desired HR image and with respect to the unknown blur are efficiently addressed by the alternating direction method of multipliers (ADMM). Experiments on the Multi-view Image Database of the University of Tsukuba and images captured by our own camera array system demonstrate the effectiveness of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.238-243
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• 1998

This paper presents a new approach to visual servoing with the stereo vision. In order to control the position and orientation of a robot with respect to an object, a new technique is proposed using a binocular stereo vision. The stereo vision enables us to calculate an exact image Jacobian not only at around a desired location but also at the other locations. The suggested technique can guide a robot manipulator to the desired location without giving such priori knowledge as the relative distance to the desired location or the model of an object even if the initial positioning error is large. This paper describes a model of stereo vision and how to generate feedback commands. The performance of the proposed visual servoing system is illustrated by the simulation and experimental results and compared with the case of conventional method fur a SCARA robot.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.4
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• pp.61-67
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• 2009

PACS and Medical Image System use only high level metadata in retrieving desired image nowadays. In order to retrieve Medical Image Data more efficiently, it would be needed to retrieve similarity by utilizing low level metadata as well as keyword retrieval by high level metadata. Thus, In this paper presents that it has realized similarity retrieval by low level metadata on the basis of MPEG-7, and keyword retrieval by high level metadata of DICOM base. It would be also available to look into medical image data in various methods and read accurate image promptly for diagnosis and treatment by retrieval with integrating two metadata.

• Journal of Biomedical Engineering Research
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• v.18 no.1
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• pp.9-16
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• 1997

We develop an automatic imaging matching technique for combining portal image and simulator image for improvements in localization of treatment in radiation therapy. Fusion of images from two imaging modalities is treated as follows. We archive images thxough a frame-yabber. The simulator and portal images are edge detected and enhanced with interpolated adaptive histouam equalization and combined using geometrical parameters relating the coordinates of two image data sets which are calculated using Fourier descriptors. We don't use any kind of imaging markers for patient's convenience. clinical use of this image matching technique for treatment planning will result in improvements in localization of treatment volumes and critical structures. These improvements will allow greater sparing of normal tissues and more precise delivery of energy to the desired irradiation volume.

• Korean Journal of Community Nutrition
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• v.2 no.4
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• pp.505-514
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• 1997

Concern about body weight among young females is too much, so distorted body image and restrained eating are serious problems among Korean young females. A society preoccupied with thinness give a psychological burden to women, this burden may have adverse effects on health physically and psychologically. We tried this study to investigate body image, eating problems, and dietary intakes among female college students in urban areas of Korea. We satisfactorily surveyed using their body weight, desired weight dieting, binge eating, and night eating by self-questionnaires with 500 female college students. We evaluated their dietary intakes by semi-quantitative food frequency questionnaires to assess their usual intakes. After removing incomplete data, we analyzed 469 data by SAS 6.10. The 469 women aged 21.1 years. Their mean height, weight, and BMI were 161.4cm, 52.5kg, and 20.2kg/$m^2$ respectively. Though the frequency of obesity was only 2.6$\%$, the frequency of dissatisfaction with their body weight was 84.7$\%$. Female college students desired their hight, weight, and BMI to be 165.8cm, 48.2kg, and 17.5kg/$m^2$, respectively. The frequency of binge eating and night eating were 20.7$\%$and 6.8$\%$. The odds ratios for binge eating and night eating were 10.9(95$\%$ CI : 2.6-15.1) and 6.0(95% CI : 1.8-14.8) in the group satisfied with their body weight compared with the dissatisfied group. The odds ratios for binge eating and night eating were 2.6(95% CI : 1.6-4.4) and 2.4(95% CI : 1.1-5.4) in the dieting group compared with the non-dieting group. The mean caloric intake was 1, 840kacl and percent intakes of carbohydrate, protein, and fat wee 57.2$\%$, 16.3$\%$, and 24.0$\%$, respectively. The dieting group showed deficient intakes of protein and calcium significantly compared with the non-dieting group showed deficient intakes of protein and calcium significantly compared with the non-dieting group. Almost all women college students in urban area had dissatisfaction with their body shape, and desired severe undrerweight. The frequency of binge eating and night eating increased according to dissatisfaction with body weight, diet, and distortion of body image. Dieting has adverse effect on the intakes of essential nutrients. Educations is needed to correct distorted body image, and bad eating habits in young females, and marked preoccupation with thinness among women should then be reduced. (Korean J Community Nutrition2(4) : 505-514, 1997)

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.499-507
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• 2016

Medical imaging techniques such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and Ultrasound (US) produce a large amount of digital medical images. Hence, compression of digital images becomes essential and is very much desired in medical applications to solve both storage and transmission problems. But at the same time, an efficient image compression scheme that reduces the size of medical images without sacrificing diagnostic information is required. This paper proposes a novel threshold-based medical image compression algorithm to reduce the size of the medical image without degradation in the diagnostic information. This algorithm discusses a novel type of thresholding to maximize Compression Ratio (CR) without sacrificing diagnostic information. The compression algorithm is designed to get image with high optimum compression efficiency and also with high fidelity, especially for Peak Signal to Noise Ratio (PSNR) greater than or equal to 36 dB. This value of PSNR is chosen because it has been suggested by previous researchers that medical images, if have PSNR from 30 dB to 50 dB, will retain diagnostic information. The compression algorithm utilizes one-level wavelet decomposition with threshold-based coefficient selection.