• Communications for Statistical Applications and Methods
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• v.21 no.3
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• pp.225-234
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• 2014

We propose a novel hierarchical clustering for distribution valued dissimilarities. Analysis of large and complex data has attracted significant interest. Symbolic Data Analysis (SDA) was proposed by Diday in 1980's, which provides a new framework for statistical analysis. In SDA, we analyze an object with internal variation, including an interval, a histogram and a distribution, called a symbolic object. In the study, we focus on a cluster analysis for distribution valued dissimilarities, one of the symbolic objects. A hierarchical clustering has two steps in general: find out step and update step. In the find out step, we find the nearest pair of clusters. We extend it for distribution valued dissimilarities, introducing a measure on their order relations. In the update step, dissimilarities between clusters are redefined by mixture of distributions with a mixing ratio. We show an actual example of the proposed method and a simulation study.

• Journal of Environmental Science International
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• v.24 no.11
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• pp.1473-1483
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• 2015

In this study, a digital terrain analysis had been performed for a mountainous watershed having wetlands. In order to consider the impact for wetland in the flow determination algorithm, the Laplace equation is implemented into the upslope accounting algorithm of wetness computation scheme. The computational algorithm of wetland to spatial contribution of downslope area and wetness was also developed to evaluate spatially distributed runoff due to the presence of wetland. Developed schemes were applied to Wangpichun watershed located Chuncuk mountain at Ulzingun, South Korea. Both spatial distribution of wetness and its histogram indicate that the developed scheme provides feasible consideration of wetland impact in spatial hydrologic analysis. The impact of wetland to downslope propagation pattern is also useful to evaluate spatially distributed runoff distribution.

• Radiation Oncology Journal
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• v.20 no.3
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• pp.283-293
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• 2002

Purpose : A PC based brachytherapy planning system was developed to display dose distributions on simulation images by 2D isodose curve including the dose profiles, dose-volume histogram and 30 dose distributions. Materials and Methods : Brachytherapy dose planning software was developed especially for the Ir-192 source, which had been developed by KAERI as a substitute for the Co-60 source. The dose computation was achieved by searching for a pre-computed dose matrix which was tabulated as a function of radial and axial distance from a source. In the computation process, the effects of the tissue scattering correction factor and anisotropic dose distributions were included. The computed dose distributions were displayed in 2D film image including the profile dose, 3D isodose curves with wire frame forms and dosevolume histogram. Results : The brachytherapy dose plan was initiated by obtaining source positions on the principal plane of the source axis. The dose distributions in tissue were computed on a $200\times200\;(mm^2)$ plane on which the source axis was located at the center of the plane. The point doses along the longitudinal axis of the source were $4.5\~9.0\%$ smaller than those on the radial axis of the plane, due to the anisotropy created by the cylindrical shape of the source. When compared to manual calculation, the point doses showed $1\~5\%$ discrepancies from the benchmarking plan. The 2D dose distributions of different planes were matched to the same administered isodose level in order to analyze the shape of the optimized dose level. The accumulated dose-volume histogram, displayed as a function of the percentage volume of administered minimum dose level, was used to guide the volume analysis. Conclusion : This study evaluated the developed computerized dose planning system of brachytherapy. The dose distribution was displayed on the coronal, sagittal and axial planes with the dose histogram. The accumulated DVH and 3D dose distributions provided by the developed system may be useful tools for dose analysis in comparison with orthogonal dose planning.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.11-22
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• 2010

This paper is a study on implementation of intelligent image surveillance system using context information and supplements temporal-spatial constraint, the weak point in which it is hard to process it in real time. In this paper, we propose scene analysis algorithm which can be processed in real time in various environments at low resolution video(320*240) comprised of 30 frames per second. The proposed algorithm gets rid of background and meaningless frame among continuous frames. And, this paper uses wavelet transform and edge histogram to detect shot boundary. Next, representative key-frame in shot boundary is selected by key-frame selection parameter and edge histogram, mathematical morphology are used to detect only motion region. We define each four basic contexts in accordance with angles of feature points by applying vertical and horizontal ratio for the motion region of detected object. These are standing, laying, seating and walking. Finally, we carry out scene analysis by defining simple context model composed with general context and emergency context through estimating each context's connection status and configure a system in order to check real time processing possibility. The proposed system shows the performance of 92.5% in terms of recognition rate for a video of low resolution and processing speed is 0.74 second in average per frame, so that we can check real time processing is possible.

• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.67-76
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• 2010

This paper proposes a image-based water level measurement method, which adapt to the ruler's surface condition. When the surface of a ruler is deteriorated by mud, drifts, or strong light reflection, the proposed method judges the pollution of ruler by comparing distance between two levels: the first one is the end position of horizontal edge region which keeps the pattern of ruler's marking, and the second one is the position where the sharpest drop occurs in the histogram which is construct using image density based on the axis of image height. If the ruler is polluted, the water level is a position of local valley of the section having a maximum difference between the local peak and valley around the second level. If the ruler is not polluted, the water level is detected as the position having horizontal edges more than 30% of histogram's maximum value around the first level. The detected water level is converted to the actual water level by using the mapping table which is construct based on the making of ruler in the image. The proposed method is compared to the ultrasonic based method to evaluate its accuracy and efficiency on the real situation.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.875-883
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• 2023

In this letter, we used PlanetScope imagery to conduct experiments on the color characteristics for oil type classification of marine oil spills through Red-Green-Blue (RGB) histogram analysis. The histograms of marine oil spills can be divided into three categories (dark black tones, light silver tones, and light rainbow tones) according to the distribution of pixel values in each band. Thick oil layers with dark black tones can be classified as heavy oil, while thin oil layers with light silver and rainbow tones can be classified as light oil. As more images are analyzed in the future, these oil spill detection and classification methods will become more generalized and reliable.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.41-51
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• 2024

In this paper, we explore the enhancement of target detection accuracy in the guided weapon using deep learning object detection on infrared (IR) images. Due to the characteristics of IR images being influenced by factors such as time and temperature, it's crucial to ensure a consistent representation of object features in various environments when training the model. A simple way to address this is by emphasizing the features of target objects and reducing noise within the infrared images through appropriate pre-processing techniques. However, in previous studies, there has not been sufficient discussion on pre-processing methods in learning deep learning models based on infrared images. In this paper, we aim to investigate the impact of image pre-processing techniques on infrared image-based training for object detection. To achieve this, we analyze the pre-processing results on infrared images that utilized global or local information from the video and the image. In addition, in order to confirm the impact of images converted by each pre-processing technique on object detector training, we learn the YOLOX target detector for images processed by various pre-processing methods and analyze them. In particular, the results of the experiments using the CLAHE (Contrast Limited Adaptive Histogram Equalization) shows the highest detection accuracy with a mean average precision (mAP) of 81.9%.

• Journal of the Korea Computer Industry Society
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• v.9 no.5
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• pp.223-228
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• 2008

A case study to show MS Excel's convenient and powerful functions was conducted to test hypotheses with subway data. Quantitative variables were described using descriptive menu, and qualitative variables were described using histogram menu of a MS Excel software. Relationships were tested using regression menu, differences were tested using t-test menu, and factors were tested using variance-layout menu of a Excel software. Data input, management, and statistical analysis were done successfully with only a MS Excel software.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.1
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• pp.33-39
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• 2017

In this paper, an effective feature which is capable of classifying targets among the detections obtained from 2D range-bearing maps generated in active sonar environments is proposed. Most conventional approaches for target classification with the 2D maps have considered magnitude of peak and statistical features of the area surrounding the peak. To improve the classification performance, HOG(Histogram of Gradient) feature, which is popular for their robustness in the image textures analysis is applied. In order to classify the target signal, SVM(Support Vector Machine) method with reduced HOG feature by the PCA(Principal Component Analysis) algorithm is incorporated. The various simulations are conducted with the real clutter signal data and the synthesized target signal data. According to the simulated results, the proposed method considering HOG feature is claimed to be effective when classifying the active sonar target compared to the conventional methods.

• Science of Emotion and Sensibility
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• v.7 no.4
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• pp.57-65
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• 2004

In this paper, we present a hybrid approach which incorporates color, texture and shape in content-based image retrieval. Colors in each image are clustered into a small number of representative colors. The feature descriptor consists of the representative colors and their percentages in the image. A similarity measure similar to the cumulative color histogram distance measure is defined for this descriptor. The co-occurrence matrix as a statistical method is used for texture analysis. An optimal set of five statistical functions are extracted from the co-occurrence matrix of each image, in order to render the feature vector for eachimage maximally informative. The edge information captured within edge histograms is extracted after a pre-processing phase that performs color transformation, quantization, and filtering. The features where thus extracted and stored within feature vectors and were later compared with an intersection-based method. The content-based retrieval system is tested to be effective in terms of retrieval and scalability through experimental results and precision-recall analysis.