• Culinary science and hospitality research
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• v.24 no.2
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• pp.112-118
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• 2018

The purpose of this study is to develop and test a model that explains the effect of experiential marketing factors on: 1) brand image, 2) satisfaction, and 3) loyalty in context of restaurants located in complex shopping mall. In addition, the study clarified how these variables relate to each other. Survey were distributed to customers who have visiting experience(s) in a restaurants in complex shopping mall. Total 360 participants were distributed and 344 questionnaires were used for analysing. The confirmatory factor analysis and structural equation modeling(SEM) have been employed research methods for frequency analysis, reliability analysis and measurement model validation. The findings of this study identified that relation factor of experiential marketing elements was only significant factor on brand image Furthermore, sense and recognition were critical components of customer satisfaction. Last, present study also identified the significant relationship between satisfaction and customer loyalty. These findings may contribute to provide valuable marketing strategic for this business segmentation, and it can be utilized as a fundamental study to establish an efficient business plan to increase revenue especially for restaurant business located in complex shopping mall.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.3
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• pp.261-268
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• 2014

Precision screws have a wide range of industrial applications such as electrical and automotive products. To produce screw threads with high precision, not only high precision manufacturing technology but also reliable measurement technology is required. Machine vision systems have been used in the automatic inspection of screw threads based on backlight illumination, which cannot detect defects on the thread surface. Recently, an omnidirectional inspection system for screw threads was developed to obtain $360^{\circ}$ images of screws, based on front light illumination. In this study, the illumination design for the omnidirectional inspection system was modified by adding a light shield to improve the image uniformity. Optical simulation for various shield designs was performed to analyze image uniformity of the obtained images. The simulation results were analyzed statistically using response surface method, from which optical performance of the omnidirectional inspection system could be optimized in terms of image quality and uniformity.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.203-208
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• 2019

Fast operation of a CNN based object detection is important in many application areas. It is an efficient approach to reduce the size of an input image. However, it is difficult to find an area that includes a target object with minimal computation. This paper proposes a ROI detection method that is fast and robust to noise. The proposed method is not affected by a flicker line noise that is a kind of aliasing between camera and LED light. Fast operation is achieved by using down-sampling efficiently. The accuracy of the proposed ROI detection method is 92.5% and the operation time for a frame with a resolution of 640 × 360 is 0.388msec.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1347-1351
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• 1987

This thesis aims at studying laplacian pyramid vector quantization which keeps a simple compression algorithm and stability against various kinds of image data. To this end, images are devied into two groups according to their statistical characteristics. At 0.860 bits/pixel and 0.360 bits/pixel respectively, laplacian pyramid vector quantization is compared to the existing spatial domain vector quantization and transform coding under the same condition in both objective and subjective value. The laplacian pyramid vector quantization is much more stable against the statistical characteristics of images than the existing vector quantization and transform coding.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.360-362
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• 2003

This paper presents simple feature-based approaches for full- and/or semi-automatic extraction, selection, and localization (center-determination) of ground control points (GCPs) for radargrammetry using airborne synthetic aperture radar (SAR) images. Test results using airborne NASA/JPL TOPSAR images in Taiwan verify that the registration accuracy is about 0.8${\sim}$1.4 pixels. In c.a. 30 minutes, 1500${\sim}$3000 GCPs are extracted and their point centers in a SAR image of about 512 ${\times}$ 512 pixels are determined on a personal computer.

• Journal of Advanced Marine Engineering and Technology
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• v.29 no.4
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• pp.453-460
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• 2005

Experiments were conducted to investigate the effect of the surface roughness on the flat plate turbulent boundary layer. The square rods were installed at the leading edge to make surface roughness. The particle image velocimetry was used to measure the mean velocities and velocity fluctuation component. All measurements were made over a range of w/k=1. 2 5 and $Re_x=80.000{\sim}360,000$. Friction velocity was measured by using Clauser plot method. The level of turbulent intensities on roughness surface appears more strongly than that of turbulent intensities on flat plate. A correlation of boundary layer thickness in term of $Re_x$ and w/k are presented.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.1
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• pp.70-77
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• 2014

This paper proposes a novel localization algorithm based on ego-motion which used Lucas-Kanade Optical Flow and warping image obtained through fish-eye lenses mounted on the robots. The omnidirectional image sensor is a desirable sensor for real-time view-based recognition of a robot because the all information around the robot can be obtained simultaneously. The preprocessing (distortion correction, image merge, etc.) of the omnidirectional image which obtained by camera using reflect in mirror or by connection of multiple camera images is essential because it is difficult to obtain information from the original image. The core of the proposed algorithm may be summarized as follows: First, we capture instantaneous $360^{\circ}$ panoramic images around a robot through fish-eye lenses which are mounted in the bottom direction. Second, we extract motion vectors using Lucas-Kanade Optical Flow in preprocessed image. Third, we estimate the robot position and angle using ego-motion method which used direction of vector and vanishing point obtained by RANSAC. We confirmed the reliability of localization algorithm using ego-motion based on fisheye warping image through comparison between results (position and angle) of the experiment obtained using the proposed algorithm and results of the experiment measured from Global Vision Localization System.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.10
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• pp.1967-1972
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• 2016

This paper defines a distance graph for a hand region in a depth image and proposes an algorithm detecting finger using it. The distance graph is a graph expressing the hand contour with angles and Euclidean distances between the center of palm and the hand contour. Since the distance graph has local maximum at fingertips' position, we can detect finger points and recognize the number of them. The hand contours are always divided into 360 angles and the angles are aligned with the center of the wrist as a starting point. And then the proposed algorithm can well detect fingers without influence of the size and orientation of the hand. Under some limited recognition test conditions, the recognition test's results show that the recognition rate is 100% under 1~3 fingers and 98% under 4~5 fingers and that the failure case can also be recognized by simple conditions to be available to add.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.1004-1012
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• 2020

We present a novel content-preserving seam estimation algorithm for real-time high-resolution video stitching. Seam estimation is one of the fundamental steps in image/video stitching. It is to minimize visual artifacts in the transition areas between images. Typical seam estimation algorithms are based on optimization methods that demand intensive computations and large memory. The algorithms, however, often fail to avoid objects and results in cropped or duplicated objects. They also lack temporal consistency and induce flickering between frames. Hence, we propose an efficient and temporarily-consistent seam estimation algorithm that utilizes a straight line. The proposed method also uses convolutional neural network-based instance segmentation to locate seam at out-of-objects. Experimental results demonstrate that the proposed method produces visually plausible stitched videos with minimal visual artifacts in real-time.

• New Physics: Sae Mulli
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• v.68 no.11
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• pp.1268-1274
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• 2018

Aircraft-warning lights are lights that are used to inform pilots in flight about the presence of buildings or dangerous objects. Currently, the light sources of most aircraft-warning lights have been replaced by light-emitting diodes (LEDs). However, the aircraft-warning lights that are installed do not meet the optical performance standards and may cause airplane collisions. Therefore, the use of such light poses a risk to aviation safety. In order to solve this problem, we designed a Fresnel lens with the same luminous intensity distribution ovef $360^{\circ}$ direction; thus, we collimated the light beam from the LED light source with a narrow beam divergence angle in the form of an array of aspheric pieces. After that, we designed and simulated an aircraft-warning-light optical system with a center luminous intensity of 20,000 cd and a vertical divergence angle of $3^{\circ}$ or more by optimizing the lens' tilt and the distance between the LED and the Fresnel lens.