• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.5
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• pp.325-333
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• 2015

Vision based night-time vehicle detection has been an emerging research field in various advanced driver assistance systems(ADAS) and automotive vehicle as well as automatic head-lamp control. In this paper, we propose night-time vehicle detection method based on multi-class support vector machine(SVM) that consists of thresholding, labeling, feature extraction, and multi-class SVM. Vehicle light candidate blobs are extracted by local mean based thresholding following by labeling process. Seven geometric and stochastic features are extracted from each candidate through the feature extraction step. Each candidate blob is classified into vehicle light or not by multi-class SVM. Four different multi-class SVM including one-against-all(OAA), one-against-one(OAO), top-down tree structured and bottom-up tree structured SVM classifiers are implemented and evaluated in terms of vehicle detection performances. Through the simulations tested on road video sequences, we prove that top-down tree structured and bottom-up tree structured SVM have relatively better performances than the others.

• The Research Journal of the Costume Culture
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• v.19 no.4
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• pp.805-819
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• 2011

In contemporary society, heterotopia is the law dominates thoughts and is the concept reconstituted spaces calls order in chaos. And that is the place which refuses the paradoxical and social custom and sometimes poses a danger and rise in rebel. The purpose of this study is to study how forms of clothing fluid form images are expressed in modern fashion develop body around in the spatial relationship between the body and its environment. The study method consider changed characterastics of fluid space through the heterotopia thinking system of Foucault Michel. Based on this method, the heterotopia space that appeared in the plastic arts in aspects of artistic significance and aesthetic value was examined. Based on the above discussion on modern fashion Fluid Form were expressed in any formative characteristics were considered. The results of this study are as follows. Fluidity is the transformed interaction. It expanded external representation of organic body structure and reconstructed flexible forms of dynamic structures continuously. Transformation is the new space structure. It constructed invisible transformation and developed convertible dress space by combining a variety of functional overlap and fold. Deconstruction was expressed structural forms, expanding the existing forms in the open structure which have ambiguous boundaries.

• Journal of the Semiconductor & Display Technology
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• v.10 no.2
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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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.157-160
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• 2011

In this paper, We proposed the method of recognition and analysis of human activites using Motion vector in real-time surveillance system. We employs subtraction image techniques to detect blob(human) in the foreground. When MPEG-4 video recording EPZS(Enhanced Predicted Zonal Search) is detected the values of motion vectors were used. In this paper, the activities of human recognize and classified such as meta-classes like this {Active, Inactive}, {Moving, Non-moving}, {Walking, Running}. Each step was separated using a step-by-step threshold values. We created approximately 150 conditions for the simulation. As a result, We showed a high success rate about 86~98% to distinguish each steps in simulation image.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.1
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• pp.173-178
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• 2014

In this study, the new color image processing algorithms with an android-based mobile device are developed to detect the abnormal color densities in a skin image and interpret them as the vitiligo lesions. Our proposed method is firstly based on transforming RGB data into HSI domain and segmenting the imag into the vitiligo-skin candidates by applying Otsu's threshold algorithm. The structure elements for morphological image processing are suggested to delete the spurious regions in vitiligo regions and the image blob labeling algorithm is applied to compare RGB color densities of the abnormal skin region with them of a region of interest. Our suggested color image processing algorithms are implemented with an android-platform smartphone and thus a mobile device can be utilized to diagnose or monitor the patient's skin conditions under the environments of pervasive healthcare services.

• ETRI Journal
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• v.43 no.1
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• pp.95-108
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• 2021

Application (app) ratings are feedback provided voluntarily by users and serve as important evaluation criteria for apps. However, these ratings can often be biased owing to insufficient or missing votes. Additionally, significant differences have been observed between numeric ratings and user reviews. This study aims to predict the numeric ratings of Google apps using machine learning classifiers. It exploits numeric app ratings provided by users as training data and returns authentic mobile app ratings by analyzing user reviews. An ensemble learning model is proposed for this purpose that considers term frequency/inverse document frequency (TF/IDF) features. Three TF/IDF features, including unigrams, bigrams, and trigrams, were used. The dataset was scraped from the Google Play store, extracting data from 14 different app categories. Biased and unbiased user ratings were discriminated using TextBlob analysis to formulate the ground truth, from which the classifier prediction accuracy was then evaluated. The results demonstrate the high potential for machine learning-based classifiers to predict authentic numeric ratings based on actual user reviews.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.132-138
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• 2022

Due to the impacts of the current pandemic COVID-19 and the continuation of studying online. There is an urgent need for an effective and efficient education platform to help with the continuity of studying online. Therefore, the question bank system (QB) is introduced. The QB system is designed as a website to create a single platform used by faculty members in universities to generate questions and store them in a bank of questions. In addition to allowing them to add two types of questions, to help the lecturer create exams and present the results of the students to them. For the implementation, two languages were combined which are PHP and Python to generate questions by using Artificial Intelligence (AI). These questions are stored in a single database, and then these questions could be viewed and included in exams smoothly and without complexity. This paper aims to help the faculty members to reduce time and efforts by using the Question Bank System by using AI and Natural Language Processing (NLP) to extract and generate questions from given text. In addition to the tools used to create this function such as NLTK and TextBlob.

• Journal of Digital Contents Society
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• v.14 no.4
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• pp.455-462
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• 2013

This paper proposes a detection system that, by determining whether a dangerous act is being carried out among other pedestrians in the images captured using CCTV, provides pre-warnings and establishes emergency measures. To determine the presence of a dangerous act, after setting zones of interest and danger zones within those zones of interest, the danger level is determined in accordance with the range of encroachment upon detecting an object. Especially, this research aims at detecting a suicide jump from the bridge and extends to detecting a dangerous act among pedestrians from detecting a dangerous act of only one person with no one in the previous research. This system classifies the status into 3 levels as safe, alert, and danger according to the amount of part being over the bridge railing. If a situation is deemed as warning-worthy and emergency, the integrated control center is immediately alerted to facilitate prevention in advance.

• Journal of the Korean Society of Clothing and Textiles
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• v.32 no.12
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• pp.1971-1980
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• 2008

The purpose of this study is to explore the meaning and depth of traditional knitwears, and to develop knitwear designs by using Aran motif, one of major motifs of traditional knitwears and on the basis of the 'continuance' theory of Henri Bergson and Jill Deleuze. Connectivity, the sense of space and deconstructive fluidity-basic concepts of the continuance theory-are felt in such forms as pleats, origami, and air pumping systems, blob and twisting used in the modem fashion. The motif of Aran knitwear which has a long historical tradition can be reinterpreted in terms of those concepts of the continuance theory. In this study, we designed five pieces of knitwear while applying cable motif, an important motif of Aran knitwear, and the concepts of the continuance theory to them. This study will make a contribution to the designing of knit wears through reinterpretation of a traditional motif in terms of a modem philosophical thought.

• Journal of Korea Multimedia Society
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• v.10 no.10
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• pp.1271-1283
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• 2007

To detect defect of FPD(flat panel displays) is very difficult due to uneven illumination on FPD panel image. This paper presents a method to detect various types of defects using the approximated image of the uneven illumination by B-spline. To construct a approximated surface, corresponding to uneven illumination background intensity, while reducing random noises and small defect signal, only the lowest smooth subband is used by wavelet decomposition, resulting in reducing the computation time of taking B-spline approximation and enhancing detection accuracy. The approximated image in lowest LL subband is expanded as the same size as original one by wavelet reconstruction, and the difference between original image and reconstructed one becomes a flat image of compensating the uneven illumination background. A simple binary thresholding is then used to separate the defective regions from the subtracted image. Finally, blob analysis as post-processing is carried out to get rid of false defects. For applying in-line system, the wavelet transform by lifting based fast algorithm is implemented to deal with a huge size data such as film and the processing time is highly reduced.