• Journal of Environmental Impact Assessment
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• v.30 no.3
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• pp.155-163
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• 2021

The number of trees to be removed trees (ART) in the environmental impact assessment is an environmental indicator used in various parts such as greenhouse gas emissions and waste of forest trees calculation. Until now, the ART has depended on the forest tree density of the vegetation survey, and the uncertainty of estimating the amount of removed trees has increased due to the sampling bias. A full-scale survey can be offered as an alternative to improve the accuracy of ART, but the reality is that it is impossible. As an alternative, there is an individual tree detection using aerial image (ITD), and in this study, we compared the ARTs estimated by full-scale survey, sample survey, and ITD. According to the research results, compared to the result of full-scale survey, the result of ITD was overestimated by 25. While 58 were overestimated by the sample survey (average). However, as the sample survey is an estimate based on random samples, ART will be overestimated or underestimated depending on the number and size of quadrats.

• Journal of information and communication convergence engineering
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• v.4 no.4
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• pp.174-179
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• 2006

In this paper we present a text detection method inspired by wavelet packet analysis and improved fuzzy clustering algorithm(IAFC).This approach assumes that the text and non-text regions are considered as two different texture regions. The text detection is achieved by using wavelet packet analysis as a feature analysis. The wavelet packet analysis is a method of wavelet decomposition that offers a richer range of possibilities for document image. From these multi scale features, we adapt the improved fuzzy clustering algorithm based on the unsupervised learning rule. The results show that our text detection method is effective for document images scanned from newspapers and journals.

• Proceedings of the KIEE Conference
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• 2007.04c
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• pp.46-48
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• 2007

This paper presents research about improvement of fault detection algorithm in FRTU on the feeder of distribution system. FRTU(Feeder Remote Terminal Unit) is applied to fault detection schemes for phase fault, ground fault, and cold load pickup and Inrush restraint functions distinguish the fault current and the normal load current. FRTU is occurred FI(Fault Indicator) when current is over pick-up value also inrush current is occurred FRTU indicate FI. Discrete wavelet transform(DWT) analysis gives the frequency and time-scale information. The neural network system as a fault detector was trained to discriminate inrush current from the fault status by a gradient descent method. In this paper, fault detection is improved using voltage monitoring system with DWT and neural network. These data were measured in actual 22.9kV distribution system.

• Journal of Korea Multimedia Society
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• v.6 no.4
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• pp.594-600
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• 2003

In this paper, we propose a detection method for facial features in color images with various backgrounds and face poses. To begin with, the proposed method extracts face candidacy region from images with various backgrounds, which have skin-tone color and complex objects, via the color and edge information of face. And then, by using the elliptical shape property of face, we correct a rotation, scale, and tilt of face region caused by various poses of head. Finally, we verify the face using features of face and detect facial features. In our experimental results, it is shown that accuracy of detection is high and the proposed method can be used in pose-invariant face recognition system effectively

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.96.3-96
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• 2002

A scheme of clarifying fault feature in the residual is given for model-based fault detection and diagnosis of control systems. It is based on the residual generation using a robust filter and the noise suppresion in test statistics of the residual by multi-scale discrete wavelet transform. By clarifying the fault feature in the residual, the difficulties of existing model based approaches via adopting a threshold can be overcomed and it has advantage of taking the false alarm and missed detection into acount at the same time, which can make the fault detection and diagnosis easy and correct. To show the effectiveness of our approach, the simulation results are illustrated for a linear syste...

• Journal of Korea Multimedia Society
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• v.21 no.3
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• pp.333-341
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• 2018

In this paper, we propose an algorithm to automatically detect gait's joints. The proposed method classifies gait's types into front gait and flank gait so as to automatically detect gait's joints. And then according to classified types, the proposed applies joint extracting algorithm to input images. Firstly, we split input images into foreground image using difference images of Hue and gray-scale image of input and background one and extract gait's object. The proposed method classifies gaits into front gait and flank gait using ratio of Face's width to torso's width. Then classified gait's type, joints are detected 10 at front gait and detected 7~8 at flank gait. The proposed method is applied to the camera's input and the result shows that the proposed method automatically extracts joints.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.680-683
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• 2011

This study deals with damage detection in beam structure by using modal strain energy-based technique with three different sensor types: accelerometer, lead zirconate titanate (PZT) piezoelectric sensor and electrical strain gage. First, the use of direct piezoelectric effect of PZT sensor for dynamic strain response are presented. Next, a modal strain energy-based damage detection method is outlined. For validation, forced vibration tests are carried out on lab-scale aluminum cantilever beam. The dynamic responses are measured for several damage scenarios. Based on damage localization results, the performance of three different sensor types is evaluated.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.69-72
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• 2002

Human face detection has many applications such as face recognition, face or facial feature tracking, pose estimation, and expression recognition. We present a new method for automatically segmentation and face detection in color images. Skin color alone is usually not sufficient to detect face, so we combine the color segmentation and shape analysis. The algorithm consists of two stages. First, skin color regions are segmented based on the chrominance component of the input image. Then regions with elliptical shape are selected as face hypotheses. They are certificated to searching for the facial features in their interior, Experimental results demonstrate successful detection over a wide variety of facial variations in scale, rotation, pose, lighting conditions.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.733-734
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• 2006

The Face Area Detection has an extensive error range of abstraction probabilities by image illuminations and background conditions. In this paper, to reduce error ranges of abstraction probabilities by factors such as illuminations and backgrounds, we made use of Retinex and the Face Area Detection algorithm together. In comparison with other previous methods[4], our proposed algorithm showed stabler and elevated detection rate.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.883-885
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• 1995

Edges are one of the most important features used in various computer vision applications. Most of the known edge detection techniques are categorized into three gropus: First two approaches are to find gray level changes using first-order or second-order differentiation. The third method uses intrinsic propoeties of edges such as the result shown during scale space filtering. In this paper, we study various kind of edge detection techniques. Two images (Lenna image and a certain image which is composed of step, ramp, roof, and other artificial edge patterns) are used to compare different edge detection techniques and to verify the advantages and disadvantage of each techniques.