• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.193-195
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• 2001

이동체는 끊임 없이 위치 정보를 변경하는 특징을 가지고 있다. 빈번한 데이터 변경이 발생하는 이동체에 대한 효율적인 색인 기법의 대한 연구가 필요하다. 이 논문에서는 이동체의 공간 데이터 표현 방법과 색인 변경 정책을 제안한다. 그리고 제안한 색인 변경 정책에 따른 공간 색인별 성능 평가와 구축된 공간 색인의 영역 질의 처리에 대한 성능 평가를 위한 실험 평가기를 설계한다. 실험을 통해 이동체 데이터베이스에서 효율적으로 사용 가능한 색인을 도출해 낼 수 있으며, 실험 결과물의 하나인 성능 평가기를 사용하여 향후 개발할 이동체를 위한 새로운 색인에 대한 성능 평가도 수행할 수 있다.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.515-522
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• 2001

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the highspeed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Map(SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space. A topological feature map preserves the mutual relations (similarity) in feature spaces of input data, and clusters mutually similar feature vectors in a neighboring nodes. Each node of the topological feature map holds a node vector and similar images that is closest to each node vector. We implemented about k-NN search for similar image classification as to (1) access to topological feature map, and (2) apply to pruning strategy of high speed search. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.5
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• pp.7-12
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• 2011

In this papwer, we propose a new feature extraction method, named as Class-augmented Kernel Principal Component Analysis (CA-KPCA), which can extract nonlinear features for classification. Among the subspace method that was being widely used for feature extraction, Class-augmented Principal Component Analysis (CA-PCA) is a recently one that can extract features for a accurate classification without computational difficulties of other methods such as Linear Discriminant Analysis (LDA). However, the features extracted by CA-PCA is still restricted to be in a linear subspace of the original data space, which limites the use of this method for various problems requiring nonlinear features. To resolve this limitation, we apply a kernel trick to develop a new version of CA-PCA to extract nonlinear features, and evaluate its performance by experiments using data sets in the UCI Machine Learning Repository.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.437-443
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• 2015

This paper presents face recognition based on the fusion of visible image and thermal infrared (IR) texture estimated from the face image in the visible spectrum. The proposed face recognition scheme uses a multi- layer neural network to estimate thermal texture from visible imagery. In the training process, a set of visible and thermal IR image pairs are used to determine the parameters of the neural network to learn a complex mapping from a visible image to its thermal texture in the low-dimensional feature space. The trained neural network estimates the principal components of the thermal texture corresponding to the input visible image. Extensive experiments on face recognition were performed using two popular face recognition algorithms, Eigenfaces and Fisherfaces for NIST/Equinox database for benchmarking. The fusion of visible image and thermal IR texture demonstrated improved face recognition accuracies over conventional face recognition in terms of receiver operating characteristics (ROC) as well as first matching performances.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.8
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• pp.1596-1603
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• 2007

In this paper, we experimented by three kind of method for feature parameter, training method and recognition algorithm of most suitable for speech recognition system and considered. We decided speech recognition system of most suitable through two kind of experiment after we make speech recognizer. First, we did an experiment about three kind of feature parameter to evaluate recognition performance of it in speech recognizer using existent MFCC and MFCC new feature parameter that change characteristic space using PCA and ICA. Second, we experimented recognition performance or HMM, SVM and RVM by studying data number. By an experiment until now, feature parameter by ICA showed performance improvement of average 1.5% than MFCC by high linear discrimination from characteristic space. RVM showed performance improvement of maximum 3.25% than HMM in an experiment by decrease of studying data. As such result, effective method for speech recognition system to propose in this paper derives feature parameters using ICA and un recognition using RVM.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.1
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• pp.102-107
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• 2004

In this paper, we proposed a content-based image retrieval system using the multi-Index key. The multi-index ky combines the color distribution considering the spatial characteristic and the shape features of an image using the edge detection. Consequently, the evaluation shows that the performance of the proposed technique is better than other techniques.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.11a
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• pp.189-193
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• 2000

표정인식 시스템을 구현하기 위해서는 어떠한 얼굴 특징점이 특정한 내적상태와 밀접한 관련이 있는가를 알아야한다. 이를 위해 MPEG-4 FDP 중 39개의 얼굴 특징점을 사용하여 쾌-불쾌 및 각성-수면의 내적상태와 얼굴 특징요소간의 상관관계를 분석하였다. 연극배우들의 다양한 표정연기 사진 150장으로부터, 5개의 필터 크기와 8개의 필터 방위로 구성된 Gator wavelet을 사용하여 39개의 특징점을 중심으로 영상처리 하였다. 이들 특징점의 필터 반응 값과 내적상태의 상관관계를 분석한 결과, 내적상태의 쾌-불쾌 차원은 주로 입과 눈썹 주변의 특징점과 밀접한 관련이 있었고, 각성-수면 차원은 주로 눈 주변의 특징점과 밀접한 관련이 있었다. 필터의 크기는 주로 저역 공간빈도 필터가 내적상태와 관련이 있었고, 필터의 방위는 주로 비스듬한 사선 방위가 내적상태와 관련이 있었다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.11a
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• pp.9-10
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• 2012

이진 특징 기술자는 실수 벡터 형태의 특징 기술자보다 빠르게 특징점 추출 및 정합이 가능하고 메모리 공간도 적게 차지하는 장점이 있다. 하지만, 특징점의 수가 많아질수록 정합에 많은 시간이 소요되므로 실시간 처리가 중요한 객체 추적에 적용하기 위해서는 정합의 고속화 방법에 대한 연구가 필요하다. 이에 본 논문에서는 이진 특징 기술자의 군집화를 통한 특징점의 고속 정합 방법을 제안한다. 제안된 방법은 k-means 군집화 알고리즘을 기반으로 정합을 위한 기술자 탐색을 효과적으로 수행함으로써 군집화를 사용하지 않는 기존의 정합 방법에 비해 빠르면서도 높은 정확도를 유지한다.

• Journal of the Economic Geographical Society of Korea
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• v.14 no.3
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• pp.377-393
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• 2011

This study aims to indicate the spatial characteristics of the changes in the Korean farm land. In particular, we analyze the spatial diffusion patterns of organic farms increasing rapidly with the growth in the agricultural product markets as well as the demand for safe food and sustainable growth. For the purpose, we examine the changes in the distribution patterns of organic farms between year 2000 and 2005. We analyze the agglomeration pattern by Location Quotient (LQ) and Local indicator of spatial association (LISA). Organic farms have been spread out from the outscuirts of Seoul, the capital city, to the traditional agriculture spetilized area in the southern parts of the nation. In order to analyze the relationships between organic farm distribution and the geographical variables affecting the organic farming, we develop multivariate regression models. Our findings indicate that organic farming is related with the number of agriculture-based business and information technique adaptation as well as the level of education and farmers age.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.2
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• pp.41-50
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• 2004

Since the MPEG-4 visual standard enables content-based functionalities, it is necessary to extract video objects from video sequences. Segmentation algorithms can largely be classified into two different categories: automatic segmentation and user-assisted segmentation. In this paper, we propose a new user-assisted image segmentation method based on the active contour. If we define a shape space as a set of all possible variations from the initial curve and we assume that the shape space is linear, it can be decomposed into the column space and the left null space of the shape matrix. In the proposed method, the shape space vector in the column space describes changes from the initial curve to the imaginary feature curve, and a dynamic graph search algorithm describes the detailed shape of the object in the left null space. Since we employ the shape matrix and the SUSAN operator to outline object boundaries, the proposed algorithm can ignore unwanted feature points generated by low-level image processing operations and is, therefore, applicable to images of complex background. We can also compensate for limitations of the shape matrix with a dynamic graph search algorithm.