• 한국과학교육학회지
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• 제38권5호
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• pp.705-720
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• 2018

이 연구에서는 과학교육에서 재미의 의미와 가치에 대해 문헌분석을 통해 이론적으로 고찰해보고자 하였다. 문헌분석은 재미와 관련된 국어학, 심리학, 철학 등 다양한 분야의 문헌을 대상으로 이루어졌다. 구체적으로 이 연구에서는 재미의 의미, 재미를 경험하는 맥락의 특성, 과학교육에서 재미의 가치, 재미의 가치를 실현하기 위한 과학교육의 방향성에 대해 논의하고자 하였다. 연구 결과는 다음과 같다. 첫째, 재미는 특정한 대상과의 상호작용을 통해 느껴지는 정서적 고양 상태를 의미하는 표현으로서, 감각, 관계, 자아, 대상 중심의 다양한 요소들의 복합적 작용으로 유발되는 정서적 경험임을 확인하였다. 둘째, 재미 경험의 맥락에 대해 이해하기 위하여 재미와 관련된 선행 문헌들에 대해 토픽모델링이라는 분석을 수행하였다. 분석 결과 재미와 관련 경험은 유희적 특성을 지니고 있음을 확인하였다. 이러한 유희적 특성은 과학에도 존재함을 논증하였다. 셋째, 과학교육의 재미의 교육적 가치와 지향점에 대해 논의하였다. 과학교육에서 재미는 단순히 학습행동을 유도하는 도구로서의 가치뿐만 아니라, 학습자가 과학 교수-학습 과정에서 생동감을 얻을 수 있는 보편적인 경험 중 하나이며, 학생 개개인이 과학과 관련된 정서적 발달의 원동력이 되며, 재미를 추구하는 태도와 행동은 창의적인 사고와 행동으로 이어 진다는 점에서 교육적 가치가 있음을 논증하였다. 마지막으로, 과학 교육에서 지향해야할 재미는 외부의 자극으로부터 유발되는 수동적인 재미가 아닌 노력하고 시행착오 끝에 경험할 수 있는 능동적인 재미여야 하며 과학을 즐기는 이들 또한 존중할 수 있는 과학교육 문화가 장려되어야 함을 주장했다. 특히 이 연구에서는 철학자 Deleuze(1976)의 철학을 바탕으로 학생 개개인의 고유한 재미 경험의 중요성을 논의하였다.

• 한국멀티미디어학회논문지
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• 제21권8호
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• pp.876-887
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• 2018

In this paper, we propose an robust CAMShift method to track stage objects with a camera. In order to solve the problem of tracking object misdetection in existing CAMShift technique, MBR region is detected to separate the background and the subject, and the subject size of the region of interest is calculated to solve the problem of erroneously detecting a large region having a similar color distribution ratio. Also, by applying the color corelogram and MB-LBP to the part that can not be solved by the color ratio and the size limitation, accurate texture tracking is enabled by reflecting the texture characteristics. Experimental results show that the proposed method has good tracking performance for objects that do not deviate from the size of the subject set in the area of interest and accurately extracts the texture characteristics of different subjects with similar color distribution ratios.

• 제어로봇시스템학회논문지
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• 제13권9호
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• pp.868-874
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• 2007

This paper proposes an efficient partial matching system and representation by using a region-based method for 2D image, and we applied to an extraction of the ROI(Region of Interest) according to its matching score. The matching templates consist of the global pattern and the local one. The global pattern can make it by using region-based relation between center region and its rest regions in an object. And, the local pattern can be obtained appling to the same method as global, except relation between objects. As the templates can be normalized, we use this templates for extraction of ROI with invariant to size and position. And, our system operates only one try to match, due to normalizing of region size. To use our system for searching and examining if it's the ROI by evaluating the matching function, at first, we are searching to find candidate regions with the global template. Then, we try to find the ROI among the candidates, and it works this time by using the local template. We experimented to the binary and the color image respectively, they showed that the proposed system can be used efficiently for representing of the template and the useful applications, such as partially retrievals of 2D image.

• 대한의용생체공학회:의공학회지
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• 제28권1호
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• pp.95-101
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• 2007

Recently, small-animal imaging technology has been rapidly developed for longitudinal screening of laboratory animals such as mice and rats. One of newly developed imaging modalities for small animals is an x-ray micro-CT (computed tomography). We have developed two types of x-ray micro-CT systems for small animal imaging. Both systems use flat-panel x-ray detectors and micro-focus x-ray sources to obtain high spatial resolution of $10{\mu}m$. In spite of the relatively large field-of-view (FOV) of flat-panel detectors, the spatial resolution in the whole-body imaging of rats should be sacrificed down to the order of $100{\mu}m$ due to the limited number of x-ray detector pixels. Though the spatial resolution of cone-beam CTs can be improved by moving an object toward an x-ray source, the FOV should be reduced and the object size is also limited. To overcome the limitation of the object size and resolution, we introduce zoom-in micro-tomography for high-resolution imaging of a local region-of-interest (ROI) inside a large object. For zoom-in imaging, we use two kinds of projection data in combination, one from a full FOV scan of the whole object and the other from a limited FOV scan of the ROI. Both of our micro-CT systems have zoom-in micro-tomography capability. One of both is a micro-CT system with a fixed gantry mounted with an x-ray source and a detector. An imaged object is laid on a rotating table between a source and a detector. The other micro-CT system has a rotating gantry with a fixed object table, which makes whole scans without rotating an object. In this paper, we report the results of in vivo small animal study using the developed micro-CTs.

• 한국멀티미디어학회논문지
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• 제6권3호
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• pp.409-418
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• 2003

동영상에서 객체 추적은 몇 년간 컴퓨터 비전 및 여러 실용적 응용 분야에서 관심을 가지는 주제 중 하나이다. 본 논문에서는 보안 및 감시 시스템 분야에 적용할 수 있는 실시간 객체 추적과 얼굴영역 추출 방법을 제안한다. 이를 위해 카메라가 고정되어 있고 배경영상의 변화가 거의 없는 환경으로 제한하고, 입력영상과 배경영상의 차를 이용하여 객체의 위치를 탐지하고 움직임을 추적한다. 보다 안정적인 객체 추출을 위해 적응적 배경영상을 생성하고, 객체 위치 탐지 시 그물식 탐색방법을 이용하여 객체의 내부점을 추출한다. 추출된 점들을 이용하여 MBR(Minimum Bounding Rectangle)을 설정하여 객체의 실시간 추적을 가능하도록 하였다. 또한 설정된 MBR 내에서 얼굴영역을 추출함으로써 보안 및 감시 시스템의 효용성을 향상시켰다. 그리고 실험을 통하여 제한된 환경 하에서 실시간으로 빠른 객체의 추적을 보인다.

• Journal of Animal Science and Technology
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• 제65권6호
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• pp.1254-1269
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• 2023

This study aims to predict the change in corn share according to the grazing of 20 gestational sows in a mature corn field by taking images with a camera-equipped unmanned air vehicle (UAV). Deep learning based on convolutional neural networks (CNNs) has been verified for its performance in various areas. It has also demonstrated high recognition accuracy and detection time in agricultural applications such as pest and disease diagnosis and prediction. A large amount of data is required to train CNNs effectively. Still, since UAVs capture only a limited number of images, we propose a data augmentation method that can effectively increase data. And most occupancy prediction predicts occupancy by designing a CNN-based object detector for an image and counting the number of recognized objects or calculating the number of pixels occupied by an object. These methods require complex occupancy rate calculations; the accuracy depends on whether the object features of interest are visible in the image. However, in this study, CNN is not approached as a corn object detection and classification problem but as a function approximation and regression problem so that the occupancy rate of corn objects in an image can be represented as the CNN output. The proposed method effectively estimates occupancy for a limited number of cornfield photos, shows excellent prediction accuracy, and confirms the potential and scalability of deep learning.

• 제어로봇시스템학회논문지
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• 제16권1호
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• pp.69-76
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• 2010

The segmentation of moving object in video sequence is a core technique of intelligent image processing system such as video surveillance, traffic monitoring and human tracking. A typical method to segment a moving region from the background is the background subtraction. The steps of background subtraction involve calculating a reference image, subtracting new frame from reference image and then thresholding the subtracted result. One of famous background modeling is Gaussian mixture model (GMM). Even though the method is known efficient and exact, GMM suffers from a problem that includes false pixels in ROI (region of interest), specifically shadow pixels. These false pixels cause fail of the post-processing tasks such as tracking and object recognition. This paper presents a method for removing false pixels included in ROT. First, we subdivide a ROI by using shape characteristics of detected objects. Then, a method is proposed to classify pixels from using histogram characteristic and comparing difference of energy that converts the color value of pixel into grayscale value, in order to estimate whether the pixels belong to moving object area or shadow area. The method is applied to real video sequence and the performance is verified.

• IEIE Transactions on Smart Processing and Computing
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• 제1권2호
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• pp.78-87
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• 2012

In this paper, we address the tracking problem caused by camera motion and rolling shutter effects associated with CMOS sensors in consumer handheld cameras, such as mobile cameras, digital cameras, and digital camcorders. A modified particle filtering method is proposed for simultaneously tracking objects and compensating for the effects of camera motion. The proposed method uses an elastic registration algorithm (ER) that considers the global affine motion as well as the brightness and contrast between images, assuming that camera motion results in an affine transform of the image between two successive frames. By assuming that the camera motion is modeled globally by an affine transform, only the global affine model instead of the local model was considered. Only the brightness parameter was used in intensity variation. The contrast parameters used in the original ER algorithm were ignored because the change in illumination is small enough between temporally adjacent frames. The proposed particle filtering consists of the following four steps: (i) prediction step, (ii) compensating prediction state error based on camera motion estimation, (iii) update step and (iv) re-sampling step. A larger number of particles are needed when camera motion generates a prediction state error of an object at the prediction step. The proposed method robustly tracks the object of interest by compensating for the prediction state error using the affine motion model estimated from ER. Experimental results show that the proposed method outperforms the conventional particle filter, and can track moving objects robustly in consumer handheld imaging devices.

• 응용통계연구
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• 제10권2호
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• pp.261-274
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• 1997

기존에 제안되어온 판별분석 기법이 대상으로 하는 대부분의 자료는 각 개체가 어느 한 특정한 집단에 전적으로 소속되어 있는 것으로 국한되어 왔다. 그러나 오늘날 (0-1)의 이치논리가 퍼지(Fuzzy) 개념과 다치논리로 확장되는 현상은 어느 한 개체를 꼭 한개의 집단에만 국한시키는 관점 역시 변화를 요구하고 있다고 본다. 이에 본 논문에서는 한 개체가 어떤 소속확률을 갖고 여러개의 집단에 소속되어 있는 상황을 고려하여 이러한 개체들로 구성된 학습표본으로부터 판별분석 규칙을 개발하는 것을 목표로 하였다. 방법론으로는 개체들의 특성벡터와 소속상태의 관계를 역추정(calibration) 모형으로 표현하고 판별대상개체의 특성벡터가 주어졌을 때 소속상태를 추정하도록 하며 이때 추정은 베이지안 방법, Metropolis 알고리즘 등을 사용하였다. 또한 제안된 판별규칙의 평가를 위한 기준을 제안하고 두개의 자료를 기존의 다른 규칙들과 함께 분석하여 결과를 비교하였다.

• 제어로봇시스템학회논문지
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• 제17권3호
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• pp.229-235
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• 2011

To make a robust object tracking and identifying system for an intelligent robot and/or home system, heterogeneous sensor fusion between visible ray system and infrared ray system is proposed. The proposed system separates the object by combining the ROI (Region of Interest) estimated from two different images based on a heterogeneous sensor that consolidates the ordinary CCD camera and the IR (Infrared) camera. Human's body and face are detected in both images by using different algorithms, such as histogram, optical-flow, skin-color model and Haar model. Also the pose of human body is estimated from the result of body detection in IR image by using PCA algorithm along with AdaBoost algorithm. Then, the results from each detection algorithm are fused to extract the best detection result. To verify the heterogeneous sensor fusion system, few experiments were done in various environments. From the experimental results, the system seems to have good tracking and identification performance regardless of the environmental changes. The application area of the proposed system is not limited to robot or home system but the surveillance system and military system.