• Journal of The Korean Digital Architecture Interior Association
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• v.10 no.3
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• pp.11-18
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• 2010

Cost estimation through fast and correct quantity take offs are crucial in the process of construction project. The existing methods for cost estimation are mainly based on 2D-based drawings and the estimation result tends to be different according to the estimator's experience, the quality and quantity of used information and estimation time. To solve these problems, the domestic construction industry have recently tried to use the data extracted from 3D data modeling based on BIM(Building Information Modeling) in order to achieve more accurate and objective cost estimation. However it tends to increase dramatically the quantity of information that can be used in cost estimation by estimators. Therefore in order to achieve quality information data from 3D data modeling, the characteristics of the project should be reflected on the 3D model and it is most important to extract information only for cost estimation from the whole 3D model fast and accurately. Thus this study aims to propose the 3D modeling method through Data Normalization which maximizes the usability of 3D Data modeling in cost estimation process.

• Journal of IKEEE
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• v.22 no.3
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• pp.810-815
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• 2018

The kernelized correlation filter algorithm yielded meaningful results in accuracy for object tracking. However, because of the use of a fixed size template, we could not cope with the scale change of the tracking object. In this paper, we propose a method to track objects by finding the best scale for each frame using correlation filtering response values in multi-scale using nearest neighbor interpolation and Gaussian normalization. The scale values of the next frame are updated using the optimal scale value of the previous frame and the optimal scale value of the next frame is found again. For the accuracy comparison, the validity of the proposed method is verified by using the VOT2014 data used in the existing kernelized correlation filter algorithm.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.97-103
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• 2001

The speaker recognition method using DTW algorithm has the problem that is reducing the performance of the speaker recognition system as the time variation. So there are many proposed algorithms to solve these problems. This paper proposes the new method If make the reference pattern that is acceptable to intra-speaker variation by reference pattern normalization. And to avoid reducing performance of speaker recognition system, we use the modified reference pattern to recognize the system user. The used methods in this paper are VQ and DTW. As the result of simulation we can obtain the $97.5\%$ of recognition accuracy rate.

• The Journal of the Korea Contents Association
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• v.6 no.12
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• pp.21-28
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• 2006

In a face detection system based on template matching, histogram equalization or log transform is applied to an input image for the intensity normalization and the image improvement. It is known that they are noneffective in improving an image with intensity distortion by illumination variation. In this paper, we propose an efficient image improvement method using a simple regression analysis combined with a bilateral symmetry average for images with intensity distortion by illumination variation. Experimental results show that our method delivers the detection performance better than previous methods and also remarkably reduces the number of face candidates.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2007.02a
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• pp.114-121
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• 2007

In the existing ISP methods, the important' role of enterprise value is usually ignored or not recognized in the information systems planning (ISP). Besides, in some ISP methods, there is a connotative precondition that the main body of value is always the enterprise stakeholder. Thus, in ISP, the enterprise stakeholders‘ value has been recognized while the value of other main bodies has been neglected, which has resulted in boycott and other problems in normalization construction. Based on the existing ISP analysis frame and ways, this article analyzes the enterprise fundamental principle of enterprise value acting on ISP and defines the formation of enterprise value. On the basis of Keeney's analysis way of value focused thinking for decision-making, we induct the factors of enterprise value into the ISP method and set forth such an ISP process: (1) identify the aggregation of enterprise value; (2) conform the objective structure of enterprise levels; (3) determine the appraisal standard for enterprise fundamental objectives; (4) determine the basic structure for information systems ; (5) confirm the data requirements for information systems; (6) give appraisal and comment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.9
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• pp.2312-2325
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• 2013

In this paper, we propose a preprocessing method that it is to correct the distortion of text area in Korean signboard images as a preprocessing step to improve character recognition. Distorted perspective in recognizing of Korean signboard text may cause of the low recognition rate. The proposed method consists of four main steps and eight sub-steps: main step consists of potential vertical components detection, vertical components detection, text-boundary estimation and distortion correction. First, potential vertical line components detection consists of four steps, including edge detection for each connected component, pixel distance normalization in the edge, dominant-point detection in the edge and removal of horizontal components. Second, vertical line components detection is composed of removal of diagonal components and extraction of vertical line components. Third, the outline estimation step is composed of the left and right boundary line detection. Finally, distortion of the text image is corrected by bilinear transformation based on the estimated outline. We compared the changes in recognition rates of OCR before and after applying the proposed algorithm. The recognition rate of the distortion corrected signboard images is 29.63% and 21.9% higher at the character and the text unit than those of the original images.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1873-1880
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• 1997

It is an important factor to distinguish the kind of the character for increasing recognition rate before the character recognition in the document recognition system composed of the multi-font and multi-letters. All the letters of each country have a various unique characteristic in the each composition. In this paper, we used the stroke density as a method to distinguish the letter, and it has been adopted only Korean and English character. Input data is processed by the normalization to adopt multi-font document. Proposed method has been proved by the results of experiment the fact that the distinction probability of the Korean and English is more than 90%.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.310-313
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• 1996

In this study, we developed three dimensional FDG brain PET and MRI coregistration technique. The boundaries of the head in PET and MRI were segmented using sinogram of emission PET scan and T1-weighted MRI. We registered both boundaries by minimizing the mean Euclidean distance of those. To display the registered PET and MRI simultaneously, we used weighted normalization method and interleaving method.

• The KIPS Transactions:PartD
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• v.11D no.2
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• pp.397-406
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• 2004

Usually, design method to use UML by Object-Oriented Modelling is used much. But, generation of Metadata that use UML is not easy by difference of expression about detailed functions that Is Involved language and this in environment that differ. In this paper that solution method use XML Metadata Interchange Format(XMI) for standardization and normalization of Pattern and Class. And, for design of Metamodel select frequency A many 4 element of use among XMI Metamodel and create Metadata. Design DB repository for created Metadata storing and add pattern and Information about each class composition and use query and did so that reusability and extension of Metadata nay be easy.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.6_2
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• pp.1025-1032
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• 2020

In this paper, we propose a method to classify road weather conditions into rain, fog, and sun using a SVM (Support Vector Machine) classifier after extracting weather features from images acquired in real time using an optical sensor installed on a roadside post. A multi-dimensional weather feature vector consisting of factors such as image sharpeness, image entropy, Michelson contrast, MSCN (Mean Subtraction and Contrast Normalization), dark channel prior, image colorfulness, and local binary pattern as global features of weather-related images was extracted from road images, and then a road weather classifier was created by performing machine learning on 700 sun images, 2,000 rain images, and 1,000 fog images. Finally, the classification performance was tested for 140 sun images, 510 rain images, and 240 fog images. Overall classification performance is assessed to be applicable in real road services and can be enhanced further with optimization along with year-round data collection and training.