• Journal of the Korea Society of Computer and Information
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• v.8 no.2
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• pp.31-36
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• 2003

This paper proposes an acceleration method of PCA(Principal Component Analysis) based feature detection. The feature detection method makes decision whether the target feature is included in a given image, and if included, calculates the position and extent of the target feature. The position and scale of the target feature or face is not known previously, all the possible locations should be tested for various scales to detect the target. This is a search Problem in huge search space. This Paper proposes a fast face and feature detection method by reducing the search space using the multi-stage prediction map and contour Prediction map. A Proposed method compared to the existing whole search way, and it was able to reduce a computational complexity below 10％ by experiment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.608-612
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• 2002

An automatic face and facial feature points(FFPs) detection system is proposed. A face is represented as a graph where the nodes are placed at facial feature points(FFPs) labeled by their Gabor features md the edges are describes their spatial relations. An innovative flexible feature matching is proposed to perform features correspondence between models and the input image. This matching model works likes random diffusion process in the image spare by employing the locally competitive and globally corporative mechanism. The system works nicely on the face images under complicated background, pose variations and distorted by facial accessories. We demonstrate the benefits of our approach by its implementation on the fare identification system.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.148-151
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• 2010

분류 기법은 데이터 마이닝 기술 중 가장 잘 알려진 방법으로서, Decision tree, SVM(Support Vector Machine), ANN(Artificial Neural Network) 등 기법을 포함한다. 분류 기법은 이미 알려진 상호 배반적인 몇 개 그룹에 속하는 다변량 관측치로부터 각각의 그룹이 어떤 특징을 가지고 있는지 분류 모델을 만들고, 소속 그룹이 알려지지 않은 새로운 관측치가 어떤 그룹에 분류될 것인가를 결정하는 분석 방법이다. 분류기법을 수행할 때에 기본적으로 특징 공간이 잘 표현되어 있다고 가정한다. 그러나 실제 응용에서는 단일 특징으로 구성된 특징공간이 분명하지 않기 때문에 분류를 잘 수행하지 못하는 문제점이 있다. 본 논문에서는 이 문제에 대한 해결방안으로써 많은 정보를 포함하면서 빈발패턴에 대한 정보의 순실이 없는 닫힌 빈발패턴 기반 분류에 대한 연구를 진행하였다. 본 실험에서는 ${\chi}^2$(Chi-square)과 정보이득(Information Gain) 속성 선택 척도를 사용하여 의미있는 특징 선택을 수행하였다. 그 결과, 이 연구에서 제시한 척도를 사용하여 특징 선택을 수행한 경우, C4.5, SVM 과 같은 분류기법보다 더 향상된 분류 성능을 보였다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.710-712
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• 2003

본 논문에서는 시각적. 공간적 정보로 멀티미디어 분야에서 다양한 응용이 가능한 이미지검색을 위해 색상특징정보와 모양특징정보를 멀티인덱스키로 구성하여 질의 이미지의 입력 시 자동으로 색상특징정보와 모양특징정보를 동시에 추출하여 유사한 이미지를 검색할 수 있는 내용기반 이미지 검색시스템을 제안하였다. 제안된 시스템은 기존의 단일 특징정보를 이용한 방법이나 2가지 이상의 특징정보를 단계적으로 검색하는 방법에 비해 향상된 효율성과 신속성을 보이고 있다.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.2
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• pp.35-45
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• 2024

In this paper, we propose an intra prediction method for depth picture using CNN and Attention mechanism. The proposed method allows each pixel in a block to predict to select pixels among reference area. Spatial features in the vertical and horizontal directions for reference pixels are extracted from the top and left areas adjacent to the block, respectively, through a CNN layer. The two spatial features are merged into the feature direction and the spatial direction to predict features for the prediction block and reference pixels, respectively. the correlation between the prediction block and the reference pixel is predicted through attention mechanism. The predicted correlations are restored to the pixel domain through CNN layers to predict the pixels in the block. The average prediction error of intra prediction is reduced by 5.8% when the proposed method is added to VVC intra modes.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.4 s.304
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• pp.43-50
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• 2005

This paper proposes a new pattern recognition system combining the new adaptive feature weighting based on the genetic algorithm and the modified KNN(K Nearest-Neighbor) rules. The new feature weighting proposed herein avoids the overfitting and finds the Proper feature weighting value by determining the middle value of weights using GA. New GA operators are introduced to obtain the high performance of the system. Moreover, a class dependent feature weighting strategy is employed. Whilst the classical methods use the same feature space for all classes, the Proposed method uses a different feature space for each class. The KNN rule is modified to estimate the class of test pattern using adaptive feature space. Experiments were performed with the unconstrained handwritten numeral database of Concordia University in Canada to show the performance of the proposed method.

• Spatial Information Research
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• v.20 no.6
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• pp.69-76
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• 2012

In this study, we derived the characteristics of the geo-spatial information industry by using input-output analysis. For this analysis, we classified the geo-spatial information industry and reorganized the input-output table. And we derived the production inducement coefficient, index of the power of dispersion and index of the sensitivity of dispersion in the geo-spatial information industry. We confirmed that geo-spatial information industry has a small production inducement coefficient and a great forward linkage effect. Based on these facts, we suggested the strategy direction as follows: 1) building the industrial eco-system, 2) managing both advance and applicability enhancement, 3) Establishing from a long-term point of view.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.6
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• pp.692-704
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• 2002

In this paper, we present am image retrieval method based on color-spatial features using quadtree and texture information extracted from object MBRs in an image. Tile proposed method consists of creating a DC image from an original image, changing a color coordinate system, and decomposing regions using quadtree. As such, conditions are present to decompose the DC image, then the system extracts representative colors from each region. And, image segmentation is used to search for object MBRs, including object themselves, object included in the background, or certain background region, then the wavelet coefficients are calculated to provide texture information. Experiments were conducted using the proposed similarity method based on color-spatial and texture features. Our method was able to refute the amount of feature vector storage by about 53%, but was similar to the original image as regards precision and recall. Furthermore, to make up for the deficiency in using only color-spatial features, texture information was added and the results showed images that included objects from the query images.

• Journal of KIISE:Software and Applications
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• v.30 no.5_6
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• pp.430-443
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• 2003

The objective of this paper is to evaluate most useful feature vector space using the angle, velocity and location features from gesture trajectory which extracted hand regions from consecutive input images and track them by connecting their positions. For this purpose, the gesture tracking algorithm using color and motion information is developed. The recognition module is a HMM model to adaptive time various data. The proposed algorithm was applied to a database containing 4,800 alphabetical handwriting gestures of 20 persons who was asked to draw his/her handwriting gestures five times for each of the 48 characters.

• Journal of Elementary Mathematics Education in Korea
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• v.21 no.1
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• pp.161-194
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• 2017

The aim of this study is to look into sub-factors of spatial sense that can be contained in spatial sense of solid figure of mathematics curriculum and offer suggestions to improve teaching spatial sense of solid figures in the future. In order to attain these purposes, this study examined the meaning and sub-factors of spatial sense and the relations between spatial sense of solid figure and sub-factors of spatial sense through a theoretical consideration regarding various studies on spatial sense. Based on such examination, this study compared and analyzed textbooks used in South Korea, Finland and the Netherlands with respect to contents of mathematics curriculum and textbooks in grades, sub-factors of spatial sense, and realistic contexts for spatial sense of solid figure. In the light of such theoretical consideration and analytical results, this study provided suggestions for improving teaching spatial sense of solid figures in elementary schools in Korea as follows: extending contents regarding spatial sense of solid figures in mathematics curriculum and considering continuity between grades in textbooks, emphasizing spatial orientation as well as spatial visualization, underlining not only construction with blocks but also mental activities in mental rotation and mental transformation, comparing strength and weakness of diverse plane representations of three dimensional objects, and utilizing various realistic situations and objects in space.