• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.4
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• pp.397-404
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• 2012

Jeju where concentrates on agriculture and tourism, conversion of outdoor culture into cultivation under structure happens actively for the purpose of increasing profit so continuous examination on house cultivation area is very important for this region. This paper is to suggest the effective image classification method using high resolution satellite image to detect the greenhouse. We carried out classification of greenhouse using the supervised classification and rule-based classification method about Formosat-2 images. Connecting result of two classification try to find accuracy improvement for greenhouse detection. Results about each classification method were calculated the accuracy by comparing with the result of visual detection. As a result, mahalanobis distance among the supervised methods was resulted in the highest detection. Also, it could be checked that detection accuracy was improved by tying with result of supervised method and result of rule-based classification. Therefore, it was expected that effective detection of greenhouse would be feasible if henceforward further study is performed in the process of connecting supervised classification and rule-based classification.

• Journal of Korea Multimedia Society
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• v.14 no.10
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• pp.1311-1322
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• 2011

Gesture is applied in many applications such as smart-phone, tablet-PC, and web-browser since it is a fast and simple way to invoke commands. For gesture applications, a gesture designer needs to consider both user and system during designing gestures. In spite of development of gesture design tools, some difficulties for gesture design still remains as followings; first, a designer must design every gesture manually one by one, and, second, a designer must repeatedly train gestures. In this paper, we propose a gesture proposal system that automates gesture training and gesture generation to provide more simple gesture design environment. Using automation of gesture training, a designer does not need to manually train gestures. Proposed gesture proposal system would decrease difficulties of gesture design by suggesting gestures of high recognition possibility that are generated based on mahalanobis distance calculation among generated and pre-existing gestures.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.783-788
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• 2004

Many reported methods assume that the faces in an image or an image sequence have been identified and localization. Face detection from image is a challenging task because of the variability in scale, location, orientation and pose. The difficulties in visual detection and recognition are caused by the variations in viewpoint, viewing distance, illumination. In this paper, we present an efficient linear discriminant for multi-view face detection and face location. We define the training data by using the Fisher`s linear discriminant in an efficient learning method. Face detection is very difficult because it is influenced by the poses of the human face and changes in illumination. This idea can solve the multi-view and scale face detection problems. In this paper, we extract the face using the Fisher`s linear discriminant that has hierarchical models invariant size and background. The purpose of this paper is to classify face and non-face for efficient Fisher`s linear discriminant.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.35 no.2
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• pp.126-131
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• 1990

Sixty two Job's Tear cultivars were largely classified into ten varietal groups and every varieties. except for Group V forming the Group by one variety. uniformly formed the Groups. From Group I to Group Ⅹ respectively contained three(5%), eighteen(29%), five(8%), thirteen(21%), one(2%), five(8%), seven(11%), four(7%), two(3%) and four(7%) varieties. Group II and Group IV showed considerably large variation whereas Group Ⅷ, IV and Ⅹ showed low variation and inferiority in vigorosity and yield components. Most of the varietal Group were not associated with their geographical origin. Days to flowering and plant height among the nine characters were the largest contributors to the D$^2$ in both inter- and inter groups.

• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.853-862
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• 2002

Face Detection can be defined as follows : Given a digitalized arbitrary or image sequence, the goal of face detection is to determine whether or not there is any human face in the image, and if present, return its location, direction, size, and so on. This technique is based on many applications such face recognition facial expression, head gesture and so on, and is one of important qualify factors. But face in an given image is considerably difficult because facial expression, pose, facial size, light conditions and so on change the overall appearance of faces, thereby making it difficult to detect them rapidly and exactly. Therefore, this paper proposes fast and exact face detection which overcomes some restrictions by using neural network. The proposed system can be face detection irrelevant to facial expression, background and pose rapidily. For this. face detection is performed by neural network and detection response time is shortened by reducing search region and decreasing calculation time of neural network. Reduced search region is accomplished by using skin color segment and frame difference. And neural network calculation time is decreased by reducing input vector sire of neural network. Principle Component Analysis (PCA) can reduce the dimension of data. Also, pose estimates in extracted facial image and eye region is located. This result enables to us more informations about face. The experiment measured success rate and process time using the Squared Mahalanobis distance. Both of still images and sequence images was experimented and in case of skin color segment, the result shows different success rate whether or not camera setting. Pose estimation experiments was carried out under same conditions and existence or nonexistence glasses shows different result in eye region detection. The experiment results show satisfactory detection rate and process time for real time system.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.183-190
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• 2016

This paper proposes algorithm to reduce the computing time in a neural network that reduces transmission of data for tracking mobile objects in surveillance networks in terms of detection and communication load. Object Detection can be defined as follows : Given image sequence, which can forom a digitalized image, the goal of object detection is to determine whether or not there is any object in the image, and if present, returns its location, direction, size, and so on. But object in an given image is considerably difficult because location, size, light conditions, obstacle and so on change the overall appearance of objects, thereby making it difficult to detect them rapidly and exactly. Therefore, this paper proposes fast and exact object detection which overcomes some restrictions by using neural network. Proposed system can be object detection irrelevant to obstacle, background and pose rapidly. And neural network calculation time is decreased by reducing input vector size of neural network. Principle Component Analysis can reduce the dimension of data. In the video input in real time from a CCTV was experimented and in case of color segment, the result shows different success rate depending on camera settings. Experimental results show proposed method attains 30% higher recognition performance than the conventional method.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.6
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• pp.77-84
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• 2020

In this paper, we developed a damage evaluation technique that can determine the damage location of a long-sized structure such as a cable-stayed bridge, and verified the performance of the developed technique through experiments. The damage assessment method aims to extract data that can evaluate the damage of the structure without the undamage data and can determine the damage location only by analyzing the response data of the structure. To complete this goal, we developed a damage assessment technique that considers variability based on the IMD theory, which is a statistical pattern recognition technique, to identify the damage location. To complete this goal, we developed a damage assessment technique that considers variability based on the IMD theory, which is a statistical pattern recognition technique, to identify the damage location. To evaluate the performance of the developed technique experimentally, cable damage experiments were conducted on model cable-stayed bridges. As a result, the damage assessment method considering variability automatically outputs the damageless data according to external force, and it is confirmed that the performance of extracting information that can determine the damage location of the cable through the analysis of the outputted damageless data and the measured damage data is shown.

• Knowledge Management Research
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• v.24 no.1
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• pp.245-271
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• 2023

The change to the data economy requires a new analysis beyond ordinary research in the management field. Data matching refers to a technique or processing method that combines data sets collected from different samples with the same population. In this study, statistical matching was performed using random hotdeck and Mahalanobis distance functions using 2020 Survey of Korea Venture Firms and 2020 Korea Innovation Survey datas. Among the variables used for statistical matching simulation, the industry and the number of workers were set to be completely consistent, and region, business power, listed market, and sales were set as common variables. Simulation verification was confirmed by mean test and kernel density. As a result of the analysis, it was confirmed that statistical matching was appropriate because there was a difference in the average test, but a similar pattern was shown in the kernel density. This result attempted to expand the spectrum of the research method by experimenting with a data matching research methodology that has not been sufficiently attempted in the management field, and suggests implications in terms of data utilization and diversity.