• ETRI Journal
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• v.39 no.1
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• pp.97-107
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• 2017

A fast and accurate building-level visual place recognition method built on an image-retrieval scheme using street-view images is proposed. Reference images generated from street-view images usually depict multiple buildings and confusing regions, such as roads, sky, and vehicles, which degrades retrieval accuracy and causes matching ambiguity. The proposed practical database refinement method uses informative reference image and keypoint selection. For database refinement, the method uses a spatial layout of the buildings in the reference image, specifically a building-identification mask image, which is obtained from a prebuilt three-dimensional model of the site. A global-positioning-system-aware retrieval structure is incorporated in it. To evaluate the method, we constructed a dataset over an area of $0.26km^2$. It was comprised of 38,700 reference images and corresponding building-identification mask images. The proposed method removed 25% of the database images using informative reference image selection. It achieved 85.6% recall of the top five candidates in 1.25 s of full processing. The method thus achieved high accuracy at a low computational complexity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.341-348
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• 2020

The ability to extract useful information from an image, such as the human eye, is an interface technology essential for AI computer implementation. The building recognition technology has a lower recognition rate than other image recognition technologies due to the various building shapes, the ambient noise images according to the season, and the distortion by angle and distance. The computer vision based building recognition algorithms presented so far has limitations in discernment and expandability due to manual definition of building characteristics. This paper introduces the deep learning CNN (Convolutional Neural Network) model, and proposes new method to improve the recognition rate even by changes of building images caused by season, illumination, angle and perspective. This paper introduces the partial images that characterize the building, such as windows or wall images, and executes the training with whole building images. Experimental results show that the building recognition rate is improved by about 14% compared to the general CNN model.

• Proceedings of the KSRS Conference
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• v.2
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• pp.610-614
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• 2006

In this paper the advanced method of geodesic active contours was developed for the task of building detection from aerial and satellite images. Automatic extraction of man-made structures including buildings, building blocks or roads from remote sensing data is useful for land use mapping, scene understanding, robotic navigation, image retrieval, surveillance, emergency management procedures, cadastral etc. A level set method based on a region-driven segmentation model was implemented with which building boundaries were detected, through this curve propagation technique. The essence of this approach is to optimize the position and the geometric form of the curve by measuring information along that curve, and within the regions that compose the image partition. To this end, one can consider uniform intensities inside objects and the background. Thus, given an initial position of the curve, one can determine global, region-driven functions and provide a statistical description of the inside and outside object area. The calculus of variations and a gradient descent method was used to optimize the variational functional by an iterative steady state process. Experimental results demonstrate the potential of the proposed processing scheme.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.973-985
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• 2019

In this paper, we propose a method to separate the ground and building areas and generate building models automatically through planarity analysis using UAV (Unmanned Aerial Vehicle) based point cloud. In this study, proposed method includes five steps. In the first step, the planes of the point cloud were extracted by analyzing the planarity of the input point cloud. In the second step, the extracted planes were analyzed to find a plane corresponding to the ground surface. Then, the points corresponding to the plane were removed from the point cloud. In the third step, we generate ortho-projected image from the point cloud ground surface removed. In the fourth step, the outline of each object was extracted from the ortho-projected image. Then, the non-building area was removed using the area, area / length ratio. Finally, the building's outer points were constructed using the building's ground height and the building's height. Then, 3D building models were created. In order to verify the proposed method, we used point clouds made using the UAV images. Through experiments, we confirmed that the 3D models of the building were generated automatically.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.165-168
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• 2007

A crack in concrete structure gives trouble to safety of building and human life. This study gives that development of auto extract system in a structure crack by digital image impersonal method for extract structure crack. This system will be possible to impersonal measurement for old concrete building and structure. For this auto extract system, used geometry of high resolution digital image and crack line extract by relation based image matching method. Now to conclude, this auto extract system gives a method that a quick measurement of building crack, hold objectivity in result, makes standardization for acquirement data, optimization result of measurement.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.3
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• pp.173-179
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• 2015

In this paper, object-based classification of urban areas based on a combination of information from lidar and aerial images is introduced. High resolution images are frequently used in automatic classification, making use of the spectral characteristics of the features under study. However, in urban areas, pixel-based classification can be difficult since building colors differ and the shadows of buildings can obscure building segmentation. Therefore, if the boundaries of buildings can be extracted from lidar, this information could improve the accuracy of urban area classifications. In the data processing stage, lidar data and the aerial image are co-registered into the same coordinate system, and a local maxima filter is used for the building segmentation of lidar data, which are then converted into an image containing only building information. Then, multiresolution segmentation is achieved using a scale parameter, and a color and shape factor; a compactness factor and a layer weight are implemented for the classification using a class hierarchy. Results indicate that lidar can provide useful additional data when combined with high resolution images in the object-oriented hierarchical classification of urban areas.

• Korean Journal of Remote Sensing
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• v.23 no.3
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• pp.211-219
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• 2007

Building extraction is one of the essential issues for 3D city modelling. In recent years, high-resolution satellite imagery has become widely available and it brings new methodology for urban mapping. In this paper, we have developed a semi-automatic algorithm to determine building heights from monoscopic high-resolution satellite data. The algorithm is based on the analysis of the projected shadow and actual shadow of a building. Once two roof comer points are measured manually, the algorithm detects (rectangular) roof boundary automatically. Then it estimates a building height automatically by projecting building shadow onto the image for a given building height, counting overlapping pixels between the projected shadow and actual shadow, and finding the height that maximizes the number of overlapping pixels. Once the height and roof boundary are available, the footprint and a 3D wireframe model of a building can be determined. The proposed algorithm is tested with IKONOS images over Deajeon city and the result is compared with the building height determined by stereo analysis. The accuracy of building height extraction is examined using standard error of estimate.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1067-1087
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• 2018

In a study for LiDAR data based building boundary extraction, usually dense point cloud was used to cluster building rooftop area and extract building outline. However, when we used DSM generated from stereo image matching to extract building boundary, it is not trivial to cluster building roof top area automatically due to outliers and large holes of point cloud. Thus, we propose a technique to extract building boundary semi-automatically from the DSM created from stereo images. The technique consists of watershed segmentation for using user input as markers and recursive MBR algorithm. Since the proposed method only inputs simple marker information that represents building areas within the DSM, it can create building boundary efficiently by minimizing user input.

• Korean Institute of Interior Design Journal
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• v.20 no.1
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• pp.208-219
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• 2011

This study is intended to formulate the issues through the status analysis and image evaluation for each street on the basis of colors for the outdoor advertisements for part of street side in the oldest downtown for its construction year. Analysing the business type of the street side, it displays the identity of the fashion business area and the color status shown on the outdoor advertisement did not consider the harmony on the other business type and building improvements that it displays chaotic street views in overall. Status of color for the outdoor advertisement compared and analyzed for each street-side to be analyzed with high color contrast with the building structure and outdoor advertisements than the Street B side where there are many businesses of fashion sundries and fashion clothes to form more complicated street scenery visually for the Street A side. The color combination principle of building structure and outdoor advertisement was shown to be the factor for the contrasting unity and diversity. In order not to stimulate this visual confusion, the colors of outdoor advertisement has to be applied on the basis of the color guideline based on the color combination principle of outdoor advertisement and building structure to have the aesthetic harmony overall. As a result of analysis using the KJ method, the present image of the old downtown area was shown with the adjective vocabularies of "complicated", "out-dated", "chaotic", "disorganized", "dirty", "suffocating", and "unilateral", and its image to strive for would be in a total of 6 adjective vocabularies of "well-arranged", "young", "dynamic", "sophisticated", "personable" and "neat", and it has presented the basic foundation of color guideline of outdoor advertisement fit for its image.

• The Korean Journal of Health Service Management
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• v.5 no.4
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• pp.149-159
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• 2011

The purpose of this research is to provide an available basis for marketing strategy by defining the cause-effect relation between the service level of staff members in dental clinic and purchasing behavior of the customers. This study was done in 10 days from November 2, 2009 to November 11, 2009 and the total 172 copies were used for the analysis. As a result of the correlation analysis, a significant positive correlation among all the measured variables was found. As a result of the structural model analysis, I found that the fair service among the variables of the service level significantly affects the building of relationship and the hospital image. The building of relationship and the hospital image significantly affect the positive oral spread. The building of relationship significantly affects the repurchase intentions but the hospital image does not affects the repurchase intentions. To sum up the result of this study, I found that the fair service and the building of relationship with patients are important variables in order to attract new patients and maintain existing customers. I recommend that the hospital continues to motivate staffs through internal marketing and conduct regular trainings. It is necessary to have patients satisfied with broadening responsibility and authority and set marketing strategy for the relationship with patients.