• 천문학회보
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• 제35권1호
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• pp.56.1-56.1
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• 2010

We present JHK photometry of star clusters in the dwarf irregular/dwarf starburst galaxy NGC 1569. Adopting several criteria to exclude other sources like foreground stars, background galaxies, etc., ~150 star cluster candidates are identified in the near-infrared images of NGC 1569, which include very young star clusters. From analysis based on theoretical background, we find ten very young star clusters near the center of this galaxy. The total reddening values toward these clusters are estimated from comparison with the theoretical estimates given by star cluster mode.

• 한국컴퓨터정보학회논문지
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• 제4권4호
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• pp.34-39
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• 1999

본 논문에서는 이진 영상에서 경계에 발생하는 잡영을 효율적으로 제거하고 형상을 단순화시켜 윤곽선을 추출할 수 있는 방법을 제안하였다. 제안된 방법은 이진 영상에서 영역의 윤곽선을 구하는 기존의 $2{times}2$ 마스크 사용 방법을 일부 수정하여 한 픽셀 두께의 잡영을 효율적으로 제거할 수 있도록 하였다. 이를 위해 영역 경계의 잡영에서는 윤곽선 추적 경로가 중복되는 특성과 잡영의 끝점에서의 추적 특성을 분석하여 이용하였다. 또한 흰색 바탕을 윤곽선 추출에 활용함으로써 본래의 형상을 유지하며 효과적으로 단순화된 윤곽선 추출 결과를 얻을 수 있도록 하였다. 제안된 방법은 다양한 실험을 통해 그 효용성을 확인하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.283-284
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• 2006

In this paper, we propose a novel segmentation and classification method using texture features for the document image. First, we extract the local entropy and then segment the document image to separate the background and the foreground using the Otsu's method. Finally, we classify the segmented regions into each component using PCA(principle component analysis) algorithm based on the texture features that are extracted from the co-occurrence matrix for the entropy image. The entropy-based segmentation is robust to not only noise and the change of light, but also skew and rotation. Texture features are not restricted from any form of the document image and have a superior discrimination for each component. In addition, PCA algorithm used for the classifier can classify the components more robustly than neural network.

• Journal of Ubiquitous Convergence Technology
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• 제1권1호
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• pp.3-8
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• 2007

This paper presents a set of techniques used in a real-time visual surveillance system. The system is implemented on a low-cost embedded DSP platform that is designed to work with stationary video sources. It consists of detection, a tracking and a classification module. The detector uses a statistical method to establish the background model and extract the foreground pixels. These pixels are grouped into blobs which are classified into single person, people in a group and other objects by the dynamic periodicity analysis. The tracking module uses mean shift algorithm to locate the target position. The system aims to control the human density in the surveilled scene and detect what happens abnormally. The major advantage of this system is the real-time capability and it only requires a video stream without other additional sensors. We evaluate the system in the real application, for example monitoring the subway entrance and the building hall, and the results prove the system's superior performance.

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.131-139
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• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.

• 한국전자통신학회논문지
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• 제9권10호
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• pp.1095-1102
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• 2014

본 논문에서는 실시간 감시 시스템에서 인간의 활동성을 분석하기 위하여 움직임 벡터를 사용하며, 고속연산에 GPU를 활용한다. 먼저 가장 중요한 부분인 전경으로부터 적응적 가우시안 혼합기법, 두드러진 움직임을 위한 가중치 차영상 기법, 움직임 벡터를 이용하여 인간이라고 판단되는 블랍을 검출하고, 추출된 움직임 벡터를 이용하여 사람의 활동성을 분석한다. 본 논문에서는 사람의 행동을 크게 {Active, Inactive}, {Position Moving, Fixed Moving}, {Walking, Running}의 세 가지 메타 클래스로 분류하고 인식하였다. 실험을 위해서 약 300개의 상황을 연출하였으며, 약 86%~98% 의 인식률을 보였다. 또한 $1920{\times}1080$ 크기 영상에서 CPU 기반은 4.2초 정도 걸렸는데, GPU 기반에서는 0.4초 이내로 빨라진 결과를 얻었다.

• 반도체디스플레이기술학회지
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• 제10권2호
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• pp.31-38
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• 2011

This paper introduces a video based traffic monitoring system for detecting vehicles and obstacles on the road. To segment moving objects from image sequence, we adopt the background subtraction algorithm based on the local binary patterns (LBP). Recently, LBP based texture analysis techniques are becoming popular tools for various machine vision applications such as face recognition, object classification and so on. In this paper, we adopt an extension of LBP, called the Diagonal LBP (DLBP), to handle the background subtraction problem arise in vision-based autonomous parking systems. It reduces the code length of LBP by half and improves the computation complexity drastically. An edge based shadow removal and blob merging procedure are also applied to the foreground blobs, and a pose estimation technique is utilized for calculating the position and heading angle of the moving object precisely. Experimental results revealed that our system works well for real-time vehicle localization and tracking applications.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2011년도 추계학술대회
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• pp.157-160
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• 2011

본 논문에서는 실시간 감시 시스템에서 녹화 도중에 검출된 움직임 벡터를 이용하여 사람의 활동성을 인식하고 분석하고자 한다. 전경에서 블랍(사람)을 검출하는 방법은 기존에 연구했던 차 영상을 이용하였고, MPEG-4 동영상 녹화 시 EPZS(Enhanced Predictive Zonal Search)에서 검출되는 움직임 벡터의 값을 이용하였다. 본 논문에서는 사람의 행동을 크게 세 가지의 {Active, Inactive}, {Moving, Non-moving}, {Walking, Running} 메타 클래스로 분류하고 인식하였다. 각 단계에서는 단계별 임계값을 이용하여 구분하였다. 실험을 위해서 약 150개의 상황을 연출하였으며, 실험 영상에서 각 단계를 구분하는데 약 86% ~ 98% 까지의 높은 인식률을 보였다.

• 한국컴퓨터정보학회논문지
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• 제23권11호
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• pp.51-57
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• 2018

In this paper, we studied about the extraction of the parameter and implementation of speechreading system to recognize the Korean 8 vowel. Face features are detected by amplifying, reducing the image value and making a comparison between the image value which is represented for various value in various color space. The eyes position, the nose position, the inner boundary of lip, the outer boundary of upper lip and the outer line of the tooth is found to the feature and using the analysis the area of inner lip, the hight and width of inner lip, the outer line length of the tooth rate about a inner mouth area and the distance between the nose and outer boundary of upper lip are used for the parameter. 2400 data are gathered and analyzed. Based on this analysis, the neural net is constructed and the recognition experiments are performed. In the experiment, 5 normal persons were sampled. The observational error between samples was corrected using normalization method. The experiment show very encouraging result about the usefulness of the parameter.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.912-919
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• 2022

The study is an image-based assessment that uses image processing techniques to determine the condition of concrete with surface cracks. The preparations of the dataset include resizing and image filtering to ensure statistical homogeneity and noise reduction. The image dataset is then segmented, making it more suited for extracting important features and easier to evaluate. The image is transformed into grayscale which removes the hue and saturation but retains the luminance. To create a clean edge map, the edge detection process is utilized to extract the major edge features of the image. The Otsu method is used to minimize intraclass variation between black and white pixels. Additionally, the median filter was employed to reduce noise while keeping the borders of the image. Image processing techniques are used to enhance the significant features of the concrete image, especially the defects. In this study, the tonal zones of the histogram and its properties are used to analyze the condition of the concrete. By examining the histogram, the viewer will be able to determine the information on the image through the number of pixels associated and each tonal characteristic on a graph. The features of the five tonal zones of the histogram which implies the qualities of the concrete image may be evaluated based on the quality of the contrast, brightness, highlights, shadow spikes, or the condition of the shadow region that corresponds to the foreground.