• Title/Summary/Keyword: 검출 모델

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Skin Color Region Segmentation using classified 3D skin (계층화된 3차원 피부색 모델을 이용한 피부색 분할)

  • Park, Gyeong-Mi;Yoon, Ga-Rim;Kim, Young-Bong
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.14 no.8
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    • pp.1809-1818
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    • 2010
  • In order to detect the skin color area from input images, many prior researches have divided an image into the pixels having a skin color and the other pixels. In a still image or videos, it is very difficult to exactly extract the skin pixels because lighting condition and makeup generate a various variations of skin color. In this thesis, we propose a method that improves its performance using hierarchical merging of 3D skin color model and context informations for the images having various difficulties. We first make 3D color histogram distributions using skin color pixels from many YCbCr color images and then divide the color space into 3 layers including skin color region(Skin), non-skin color region(Non-skin), skin color candidate region (Skinness). When we segment the skin color region from an image, skin color pixel and non-skin color pixels are determined to skin region and non-skin region respectively. If a pixel is belong to Skinness color region, the pixels are divided into skin region or non-skin region according to the context information of its neighbors. Our proposed method can help to efficiently segment the skin color regions from images having many distorted skin colors and similar skin colors.

Automatic Detection of Usability Issues on Mobile Applications (모바일 앱에서의 사용자 행동 모델 기반 GUI 사용성 저해요소 검출 기법)

  • Ma, Kyeong Wook;Park, Sooyong;Park, Soojin
    • KIPS Transactions on Software and Data Engineering
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    • v.5 no.7
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    • pp.319-326
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    • 2016
  • Given the attributes of mobile apps that shorten the time to make purchase decisions while enabling easy purchase cancellations, usability can be regarded to be a highly prioritized quality attribute among the diverse quality attributes that must be provided by mobile apps. With that backdrop, mobile app developers have been making great effort to minimize usability hampering elements that degrade the merchantability of apps in many ways. Most elements that hamper the convenience in use of mobile apps stem from those potential errors that occur when GUIs are designed. In our previous study, we have proposed a technique to analyze the usability of mobile apps using user behavior logs. We proposes a technique to detect usability hampering elements lying dormant in mobile apps' GUI models by expressing user behavior logs with finite state models, combining user behavior models extracted from multiple users, and comparing the combined user behavior model with the expected behavior model on which the designer's intention is reflected. In addition, to reduce the burden of the repeated test operations that have been conducted by existing developers to detect usability errors, the present paper also proposes a mobile usability error detection automation tool that enables automatic application of the proposed technique. The utility of the proposed technique and tool is being discussed through comparison between the GUI issue reports presented by actual open source app developers and the symptoms detected by the proposed technique.

Modeling of Left Ventricular Assist Device and Suction Detection Using Fuzzy Subtractive Clustering Method (퍼지 subtractive 클러스터링 기법을 이용한 좌심실보조장치 모델링 및 흡입현상 검출)

  • Park, Seung-Kyu;Choi, Seong-Jin
    • Journal of the Korean Institute of Intelligent Systems
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    • v.22 no.4
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    • pp.500-506
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    • 2012
  • A method to model left ventricular assist device (LVAD) and detect suction occurrence for safe LVAD operation is presented. An axial flow blood pump as a LVAD has been used to assist patient with heart problems. While an axial flow blood pump, a kind of a non-pulsatile pump, has relative advantages of small size and efficiency compared to pulsatile devices, it has a difficulty in determining a safe pump operating condition. It can show different pump operating statuses such as a normal status and a suction status whether suction occurs in left ventricle or not. A fuzzy subtractive clustering method is used to determine a model of the axial flow blood pump with this pump operating characteristic and the developed pump model can provide blood flow estimates before and after suction occurrence in left ventricle. Also, a fuzzy subtractive clustering method is utilized to develop a suction detection model which can identify whether suction occurs in left ventricle or not.

Face and Its Components Extraction of Animation Characters Based on Dominant Colors (주색상 기반의 애니메이션 캐릭터 얼굴과 구성요소 검출)

  • Jang, Seok-Woo;Shin, Hyun-Min;Kim, Gye-Young
    • Journal of the Korea Society of Computer and Information
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    • v.16 no.10
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    • pp.93-100
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    • 2011
  • The necessity of research on extracting information of face and facial components in animation characters have been increasing since they can effectively express the emotion and personality of characters. In this paper, we introduce a method to extract face and facial components of animation characters by defining a mesh model adequate for characters and by using dominant colors. The suggested algorithm first generates a mesh model for animation characters, and extracts dominant colors for face and facial components by adapting the mesh model to the face of a model character. Then, using the dominant colors, we extract candidate areas of the face and facial components from input images and verify if the extracted areas are real face or facial components by means of color similarity measure. The experimental results show that our method can reliably detect face and facial components of animation characters.

A study on training DenseNet-Recurrent Neural Network for sound event detection (음향 이벤트 검출을 위한 DenseNet-Recurrent Neural Network 학습 방법에 관한 연구)

  • Hyeonjin Cha;Sangwook Park
    • The Journal of the Acoustical Society of Korea
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    • v.42 no.5
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    • pp.395-401
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    • 2023
  • Sound Event Detection (SED) aims to identify not only sound category but also time interval for target sounds in an audio waveform. It is a critical technique in field of acoustic surveillance system and monitoring system. Recently, various models have introduced through Detection and Classification of Acoustic Scenes and Events (DCASE) Task 4. This paper explored how to design optimal parameters of DenseNet based model, which has led to outstanding performance in other recognition system. In experiment, DenseRNN as an SED model consists of DensNet-BC and bi-directional Gated Recurrent Units (GRU). This model is trained with Mean teacher model. With an event-based f-score, evaluation is performed depending on parameters, related to model architecture as well as model training, under the assessment protocol of DCASE task4. Experimental result shows that the performance goes up and has been saturated to near the best. Also, DenseRNN would be trained more effectively without dropout technique.

Deep Learning Algorithm Training and Performance Analysis for Corridor Monitoring (회랑 감시를 위한 딥러닝 알고리즘 학습 및 성능분석)

  • Woo-Jin Jung;Seok-Min Hong;Won-Hyuck Choi
    • Journal of Advanced Navigation Technology
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    • v.27 no.6
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    • pp.776-781
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    • 2023
  • K-UAM will be commercialized through maturity after 2035. Since the Urban Air Mobility (UAM) corridor will be used vertically separating the existing helicopter corridor, the corridor usage is expected to increase. Therefore, a system for monitoring corridors is also needed. In recent years, object detection algorithms have developed significantly. Object detection algorithms are largely divided into one-stage model and two-stage model. In real-time detection, the two-stage model is not suitable for being too slow. One-stage models also had problems with accuracy, but they have improved performance through version upgrades. Among them, YOLO-V5 improved small image object detection performance through Mosaic. Therefore, YOLO-V5 is the most suitable algorithm for systems that require real-time monitoring of wide corridors. Therefore, this paper trains YOLO-V5 and analyzes whether it is ultimately suitable for corridor monitoring.K-uam will be commercialized through maturity after 2035.

Moving Object Detection using Clausius Entropy and Adaptive Gaussian Mixture Model (클라우지우스 엔트로피와 적응적 가우시안 혼합 모델을 이용한 움직임 객체 검출)

  • Park, Jong-Hyun;Lee, Gee-Sang;Toan, Nguyen Dinh;Cho, Wan-Hyun;Park, Soon-Young
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.47 no.1
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    • pp.22-29
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    • 2010
  • A real-time detection and tracking of moving objects in video sequences is very important for smart surveillance systems. In this paper, we propose a novel algorithm for the detection of moving objects that is the entropy-based adaptive Gaussian mixture model (AGMM). First, the increment of entropy generally means the increment of complexity, and objects in unstable conditions cause higher entropy variations. Hence, if we apply these properties to the motion segmentation, pixels with large changes in entropy in moments have a higher chance in belonging to moving objects. Therefore, we apply the Clausius entropy theory to convert the pixel value in an image domain into the amount of energy change in an entropy domain. Second, we use an adaptive background subtraction method to detect moving objects. This models entropy variations from backgrounds as a mixture of Gaussians. Experiment results demonstrate that our method can detect motion object effectively and reliably.

Histogram Analysis in Separated Region for Face Contour Extraction under Various Environmental Condition (다양한 환경 조건에서의 얼굴 윤곽선 영역 검출을 위한 분할 영역 히스토그램 분석)

  • Do, Jun-Hyeong;Kim, Keun-Ho;Kim, Jong-Yeol
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.47 no.1
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    • pp.1-8
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    • 2010
  • Some methods employing the Active Contour Model have been widely used to extract face contour. Their performance, however, depends on the initial position of the model and the coefficients of the energy function which should be reconsidered whenever illumination and environmental condition of an input image is changed. Additionally, the number of points in the contour model should increase drastically in order to extract a fine contour. In this paper, we thus propose a novel approach which extracts face contour by segmenting the face region with threshold values obtained by a histogram analysis technique in the separated region of input image. The proposed method shows good performance under various illumination and environmental condition since it extracts face contour by considering the characteristics of the input image.

New Angular Velocity Pick-off Method for Dynamically Tuned Gyroscope (동조자이로스코프의 새로운 각속도 검출 방법)

  • Ma, Jin-Suk;Lee, Kwang-Il;Kim, Woo-Hyun;Kwon, Woo-Hyen;Im, Sung-Woon;Byun, Seung-Whan;Cheon, Ho-Jeong
    • Journal of Sensor Science and Technology
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    • v.8 no.2
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    • pp.139-147
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    • 1999
  • In this paper, we present the new angular velocity pick-off method for DTG (dynamically tuned gyroscope) which is widely used in various inertial navigation systems and motion control systems. In case of the external angular velocity input, the proposed scheme can make a smaller tilt-angle rather than that of conventional PI method in the transient and steady state because it has an additional inner rebalance loop with a mathematical model of the real gyroscope. So, without any mechanical redesign of the DTG, its dynamic range can be enlarged by the proposed method. The theoretical analysis and simulation model of DTG with the proposed scheme are given. Finally, the proposed scheme is verified.

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Crack Detection on the Road in Aerial Image using Mask R-CNN (Mask R-CNN을 이용한 항공 영상에서의 도로 균열 검출)

  • Lee, Min Hye;Nam, Kwang Woo;Lee, Chang Woo
    • Journal of Korea Society of Industrial Information Systems
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    • v.24 no.3
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    • pp.23-29
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    • 2019
  • Conventional crack detection methods have a problem of consuming a lot of labor, time and cost. To solve these problems, an automatic detection system is needed to detect cracks in images obtained by using vehicles or UAVs(unmanned aerial vehicles). In this paper, we have studied road crack detection with unmanned aerial photographs. Aerial images are generated through preprocessing and labeling to generate morphological information data sets of cracks. The generated data set was applied to the mask R-CNN model to obtain a new model in which various crack information was learned. Experimental results show that the cracks in the proposed aerial image were detected with an accuracy of 73.5% and some of them were predicted in a certain type of crack region.