• Journal of Dental Rehabilitation and Applied Science
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• v.16 no.2
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• pp.93-104
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• 2000

Orthodontic treatment in conjunction with second-molar extraction has been a controversial issue among orthodontists over many decades. The aim of this study was to investigate the treatment effects of upper second molar extraction cases. The sample included 19 upper second molar extraction orthodontic cases(ten Angle's Class I's and nine Class II's, average age=13Y 6M) cared at Kyung-Hee University Department of Orthodontics. Lateral cephalometric radiographs were taken before and immediately after treatment. Seventy-nine points were digitized on each cephalogram and 38 cephalometric parameters were computed comprising 22 angular measurements, 13 linear measurements, and 3 facial proportions. The data obtained from each malocclusion group were analyzed by paired t-test. The statistical results disclosed that there was no significant change in skeletal pattern after treatment except for that accountable by growth while there was statistically significant change in dentoalveolar and soft tissue patterns. There were no significant changes in Bjork sum, posterior facial height /anterior facial height and lower anterior facial height /anterior facial height. No significant changes in anteroposterior position of maxilla and palatal plane were manifested. Although facial axis and lower facial height was slightly increased and the mandible was rotated backward and downward, there was no remarkable change in the mandibular plane. There were statistically significant changes in distal movement of upper first molar, molar key correction and overjet reduction while there was no change in the occlusal plane. The upper lip was slightly retracted simultaneously with slight increase in nasolabial angle. These results signify that distalization of upper dentition with the second molar extraction does change occlusal relationship without gross modifications in the craniofacial skeletal configurationson. Henceforth the second molar extracted would be recommended to treat severe anterior crowding and protrusion with minor skeletal discrepancy.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.264-270
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• 2024

In the construction industry, steel structures are prominent due to their exceptional strength and high bearing capacity, making them resilient against natural calamities. However, the stability and overall structural integrity of these steel structures depend significantly on the precision of the individual steel members used. Presently, the dimensions of these steel members are typically measured manually using mechanical instruments such as steel tape and vernier calipers. This conventional approach is not only time-consuming but also highly vulnerable to human error. Consequently, there is a growing need for more accurate and reliable methods for assessing the dimensions of steel members. This paper aims to measure the dimensions of key checklists of the cross-section surface of the steel H-beams using Terrestrial Laser Scan (TLS) data. This study involves the automatic extraction of scan points associated with the cross-section surface of the H-beam members using RANSAC. By the end, an algorithm was developed to predict the actual edge points belonging to the boundary of the extracted surface and introduced an edge loss compensation model to compensate the losses occurred due to uncertainties. Experimental evaluations were conducted using various scan data collected from steel H-beam and the measured dimensions were subsequently compared with manual measurements and dimensions obtained through the previously proposed method, demonstrating that the measurements meet 1mm accuracy and are within the allowable tolerance range followed in industry. This research underscores the efficiency and reliability of the introduced approach, offering a promising solution to enhance the dimensional quality assessment of steel H-beams in the construction industry.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.268-274
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• 2023

Image mosaicking is one of the basic and important technologies in the field of application using images. The key of image mosaicking is to extract seamlines from a joint image. The method proposed in this paper for image mosaicking is as follows. The feature points of the images to be joined are extracted and the joining form between the two images is identified. A reference position for detection the seamlines were selected according to the joint form, and an image pyramid was created for efficient image processing. The outlines of the image including buildings and roads are extracted from the overlapping area with low resolution, and the seamlines are determined by considering the components of the outlines. Based on this, the seamlines in the high-resolution image was re-searched and finally the seamline for image mosaicking was determined. In addition, in order to minimize color distortion of the image with the determined seamline, a method of improving the quality of the mosaic image by fine correction of the mosaic area was applied. It was confirmed that the quality of the seamline extraction results applying the method proposed was reasonable.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5522-5536
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• 2017

In this paper, a video based smoke detection method using dynamic texture feature extraction with volume local binary patterns is studied. Block based method was used to distinguish smoke frames in high definition videos obtained by experiments firstly. Then we propose a method that directly extracts dynamic texture features based on irregular motion regions to reduce adverse impacts of block size and motion area ratio threshold. Several general volume local binary patterns were used to extract dynamic texture, including LBPTOP, VLBP, CLBPTOP and CVLBP, to study the effect of the number of sample points, frame interval and modes of the operator on smoke detection. Support vector machine was used as the classifier for dynamic texture features. The results show that dynamic texture is a reliable clue for video based smoke detection. It is generally conducive to reducing the false alarm rate by increasing the dimension of the feature vector. However, it does not always contribute to the improvement of the detection rate. Additionally, it is found that the feature computing time is not directly related to the vector dimension in our experiments, which is important for the realization of real-time detection.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.605-616
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• 2020

The construction quality control of hull blocks is of great significance for shipbuilding. The total station device is predominantly employed in traditional applications, but suffers from long measurement time, high labor intensity and scarcity of data points. In this paper, the Terrestrial Laser Scanning (TLS) device is utilized to obtain an efficient and accurate comprehensive construction information of hull blocks. To address the registration problem which is the most important issue in comparing the measurement point cloud and the design model, an automatic registration approach is presented. Furthermore, to compare the data acquired by TLS device and sparse point sets obtained by total station device, a method for key point extraction is introduced. Experimental results indicate that the proposed approach is fast and accurate, and that applying TLS to control the construction quality of hull blocks is reliable and feasible.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.65-71
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• 2024

An indirect visual SLAM takes raw image data and exploits geometric information such as key-points and line edges. Due to various environmental changes, SLAM performance may decrease. The main problem is caused by dynamic objects especially in highly crowded environments. In this paper, we propose a robust feature-based visual SLAM, building on ORB-SLAM, via multi-channel dynamic objects estimation. An optical flow and deep learning-based object detection algorithm each estimate different types of dynamic object information. Proposed method incorporates two dynamic object information and creates multi-channel dynamic masks. In this method, information on actually moving dynamic objects and potential dynamic objects can be obtained. Finally, dynamic objects included in the masks are removed in feature extraction part. As a results, proposed method can obtain more precise camera poses. The superiority of our ORB-SLAM was verified to compared with conventional ORB-SLAM by the experiment using KITTI odometry dataset.

• The Transactions of the Korea Information Processing Society
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• v.6 no.9
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• pp.2540-2547
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• 1999

Chromakey method is one of key technologies for realizing virtual studio, and the blue portions of a captured image in virtual studio, are replaced with a computer generated or real image. The replaced image must be changed according to the camera parameter of studio for natural merging with the non-blue portions of a captured image. This paper proposes a novel method to extract camera parameters using the recognition of pentagonal patterns that are painted on a blue screen. We extract corresponding points between a blue screen. We extract corresponding points between a blue screen and a captured image using the projective invariant features of a pentagon. Then, calculate camera parameters using corresponding points by the modification of Tsai's method. Experimental results indicate that the proposed method is more accurate compared to conventional method and can process about twelve frames of video per a second in Pentium-MMX processor with CPU clock of 166MHz.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.256-257
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• 2022

In this paper, we introduce a method of extracting three-dimensional feature points through the movement of a single mobile device. Using a monocular camera, a 2D image is acquired according to the camera movement and a baseline is estimated. Perform stereo matching based on feature points. A feature point and a descriptor are acquired, and the feature point is matched. Using the matched feature points, the disparity is calculated and a depth value is generated. The 3D feature point is updated according to the camera movement. Finally, the feature point is reset at the time of scene change by using scene change detection. Through the above process, an average of 73.5% of additional storage space can be secured in the key point database. By applying the algorithm proposed to the depth ground truth value of the TUM Dataset and the RGB image, it was confirmed that the\re was an average distance difference of 26.88mm compared with the 3D feature point result.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.5
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• pp.479-493
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• 2016

LiDAR(Light Detection and Ranging) data acquired from ALS(Airborne Laser Scanner) has been intensively utilized to reconstruct object models. Especially, researches for 3D modeling from LiDAR data have been performed to establish high quality spatial information such as precise 3D city models and true orthoimages efficiently. To reconstruct object models from irregularly distributed LiDAR point clouds, sensor calibration, noise removal, filtering to separate objects from ground surfaces are required as pre-processing. Classification and segmentation based on geometric homogeneity of the features, grouping and representation of the segmented surfaces, topological analysis of the surface patches for modeling, and accuracy assessment are accompanied by modeling procedure. While many modeling methods are based on the segmentation process, this paper proposed to extract key points directly for building modeling without segmentation. The method was applied to simulated and real data sets with various roof shapes. The results demonstrate feasibility of the proposed method through the accuracy analysis.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.2 no.1 s.2
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• pp.75-84
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• 2003

Current techniques for road-traffic monitoring rely on sensors which have limited capabilities, are costly and disruptive to install. The use of video cameras coupled with computer vision techniques offers an attractive alternative to current sensors. Video based traffic monitoring systems are now being considered key points of advanced traffic management systems. In this paper, we propose the new method which extract the traffic information using video camera. The proposed method uses an adaptive updating scheme for background in order to reduce the false alarm rate due to various noises in images. also, the proposed extraction method of traffic information calculates the traffic volume ratio of vehicles passing through predefined detection area, which is defined by the length of profile occupied by cars over that of overall detection area. Then the ratio is used to define 8 different states of traffic and to interpret the state of vehicle flows. The proposed method is verified by an experiment using CCTV traffic data from urban area.