• Journal of the Korean Society of Costume
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• v.51 no.1
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• pp.97-104
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• 2001

본 연구의 목적은 양탄자 시대 Navajo 직물의 특징을 살펴보고, 특히 시간의 흐름에 따른 양탄자의 발달을 고찰하는데 있다. 연구방법은 양탄자 스타일의 특징 및 변화에 대한 문헌을 중심으로 조사하였고, 아리조나 주립박물관과 역사박물관을 견학하였으며 박물관 안에 있는 전문가들의 조언을 듣고 연구의 자료를 보완하였다. 또한 Navajo 직물 전시회에서 실제로 직물을 관찰하였고 주요직물들을 시각적 자료로 제시하였다. 양탄자시대는 20세기 초에 서구인들의 요구에 따라 새로운 형태의 Navajo 직물이 출현하면서 시작되었고 초기, 부흥기, 지역적 스타일시대로 구분되며 1940년대 이후 지금까지 지역적 스타일 양탄자시대에 해당한다. 즉 현재 Navajo인들은 거주 지역에 따라 스타일, 색상, 염색 방법, 디자인이 서로 다른 양탄자를 생산하며 대표적인 것으로는 Crystal, Chinle. Wide Ruin, Two Grey Hills, Tees Nos Pos. Ganado, Storm Pattern 양탄자의 일곱 가지를 들 수 있다.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.1-7
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• 2021

In this paper, we propose a feature selection technique for multi-label classification. Many existing feature selection techniques have selected features by calculating the relation between features and labels such as a mutual information scale. However, since the mutual information measure requires a joint probability, it is difficult to calculate the joint probability from an actual premise feature set. Therefore, it has the disadvantage that only a few features can be calculated and only local optimization is possible. Away from this regional optimization problem, we propose a feature selection technique that constructs a low-rank space in the entire given feature space and selects features with sparsity. To this end, we designed a regression-based objective function using Nuclear norm, and proposed an algorithm of gradient descent method to solve the optimization problem of this objective function. Based on the results of multi-label classification experiments on four data and three multi-label classification performance, the proposed methodology showed better performance than the existing feature selection technique. In addition, it was showed by experimental results that the performance change is insensitive even to the parameter value change of the proposed objective function.

• Journal of Digital Contents Society
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• v.11 no.1
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• pp.17-24
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• 2010

The purpose of this study is to design and implement a kernel for the support vector machine(SVM) to improve the performance of face recognition. Local feature analysis(LFA) has been well known for its good performance. SVM kernel plays a limited role of mapping low dimensional face features to high dimensional feature space but the proposed kernel using LFA is designed for face recognition purpose. Because of the novel method that local face information is extracted from training set and combined into the kernel, this method is expected to apply to various object recognition/detection tasks. The experimental results shows its improved performance.

• Cross-Cultural Studies
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• v.53
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• pp.51-72
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• 2018

Camilo $Jos{\acute{e}}$ Cela's hometown Galicia has frequently appeared as the spatial background in his early and later works, revealing various factors related to the area in detail. It is in "Mazurca para dos muertos" that Galicia's characteristic elements appear most strikingly among his works. Several distinctive elements of Galicia are revealed in this work. First, the author shows some of Galicia's features by placing his characters in a Galician rural village and giving them the opportunity to speak local dialects. Second, Galician characteristic nature is specifically embodied through the dozens of depictions of nonstop rain. Third, the author has made the link between his work and Galicia by mentioning names of many Galician cities, villages, rivers and local writers and their works. Fourth, various Galician characteristic features, such as numerous myths, legends, and superstitions surrounding around this region are mentioned through the work. As such, almost all the sub-themes and materials of this work center on things associated with Galicia. This analysis provides for the realization that Cela reveals his identity as a Galician-born writer through this work.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.201-203
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• 2013

본 논문에서는 SIFT 와 particle 특징 궤적을 이용한 새로운 행동 인식 시스템을 제안한다. 먼저, 영상에서 중요한 지역적 특징 정보를 얻기 위하여 SIFT 특징 점들을 탐지하고, 탐지한 특징 점들을 SIFT descriptor matching 기법을 이용하여 그 궤적을 추출한다. 또한, SIFT 특징 궤적들의 수량이 적은점과 영상내의 조명변화, 부분적 가려짐 등의 변화로 인해 SIFT 특징 궤적이 종종 없어지는 단점을 보완하기 위하여, SIFT 특징 궤적 주위에 particle 점들을 탐지하고, dense optical flow 기법을 기반으로 그 특징 궤적을 추출한다. 그리고 SIFT 와 particle 궤적의 중요도를 조절하기 위해 가중치를 부여한다. 제안한 행동 인식 시스템의 효율성을 범용 데이터 셋을 이용한 실험을 통해 증명하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.742-744
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• 2004

본 논문은 복잡한 배경에서의 얼굴 검출에 있어서 다중 특징 추출 데이터로 학습한 계단식 분류기에 의한 방법을 제안한다 얼굴 검출에서 얼굴의 패턴은 상당히 다양한 영상 표현으로 나타나기 때문에 하나의 특징 추출 방법은 사람의 얼굴을 모델링 하기에는 부족하다. 따라서 여기서는 얼굴의 전체적인 지역적인 특징을 나타내는 Subregion과, 얼굴의 주파수 특성에 따라 좀 더 세밀하고 다양한 속성들을 나타내는 Haar 웨이블릿 변환을 이용하여 다중으로 특징을 추출하여 효과적인 모델링을 시도하였다. 특징을 추출한 얼굴과 비얼굴의 패턴(pattern)을 구분하기 위해서 패턴들의 통계적인 특성을 이용하여 각 추출방법에 맞게 학습된 Bayesian 분류기를 직렬로 연결하여 사용하였으며 비얼굴은 얼굴과 유사한 비얼굴(face-like nonface) 패턴들을 사용하여 모델링 하였다. 제안한 얼굴 검출 방식의 성능은 MIT-CMU 시험 영상들을 이용하여 평가하였다. 그 결과 한 가지 특징 추출을 사용하는 것 보다 두 가지 특징 추출을 병행한 계단식 구성이 더 정확한 검출 결과를 나타내었다.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.42-51
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• 2017

Snowfall is an important natural hazard in Korea. In recent years, the socioeconomic importance of impact-based forecasts of meteorological phenomena have been highlighted. To further develop forecasts, we first need to analyze the climatic characteristics of each region. In this study, homogeneous regions for snowfall analysis were classified using a self-organizing map for impact-based forecast and warning services. Homogeneous regions of snowfall were analyzed into seven clusters and the characteristics of each group were investigated using snowfall, observation days, and maximum snowfall. Daegwallyeong, Gangneung-si, and Jeongeup-si were classified as areas with high snowfall and Gyeongsangdo was classified as an area with low snowfall. Comparison with previous studies showed that representative areas were well distinguished, but snowfall characteristics were found to be different. The results of this study are of relevance to future policy decisions that use impact-based forecasting in each region.

• Proceedings of the KGS Conference
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• 2003.11a
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• pp.222-226
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• 2003

본 연구는 지리교육에서 이루어지는 지역에 대한 학습 내용을 구성하기 위한 연구이다. 본 연구의 출발점은 우리나라 교육과정에 따라 진행되는, 지역에 대한 학습이 가지는 문제점과 지역에 대한 개념 형성과정에 작용하는 학생들의 지역 인식 특징에 대한 관심이었다. 학생들의 지역에 대해 인식 조사 과정에서 중간적 수준의 지역을 설정할 필요성을 발견하였다. 따라서 본 연구에서 제안하는 지역에 대한 학습의 틀은 지역의 본질적 특성을 바탕으로 교육의 주체인 학생과 교사의 인식이 반영된 내용으로 구성되었다. (중략)

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.5
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• pp.477-481
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• 2008

Place recognition is necessary for a mobile user to be provided with place-dependent information. This paper proposes real-time video based place recognition system that identifies users' current place while moving in the building. As for the feature extraction of a scene, there have been existing methods based on global feature analysis that has drawback of sensitive-ness for the case of partial occlusion and noises. There have also been local feature based methods that usually attempted object recognition which seemed hard to be applied in real-time system because of high computational cost. On the other hand, researches using statistical methods such as HMM(hidden Markov models) or bayesian networks have been used to derive place recognition result from the feature data. The former is, however, not practical because it requires huge amounts of efforts to gather the training data while the latter usually depends on object recognition only. This paper proposes a combined approach of global and local feature analysis for feature extraction to complement both approaches' drawbacks. The proposed method is applied to a mobile information system and shows real-time performance with competitive recognition result.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.9
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• pp.889-897
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• 2010

Recently, a cutting-edge large-scale image database system has demanded these attributes: search with alarming speed, performs with high accuracy, archives efficiently and much more. An image identifier (descriptor) is for measuring the similarity of two images which plays an important role in this system. The extraction method of an image identifier can be roughly classified into two methods: a local and global method. In this paper, the proposed image identifier, LFH(Local Feature's Histogram), is obtained by a histogram of robust and distinctive local descriptors (features) constrained by a district sub-division of a local region. Furthermore, LFH has not only the properties of a local and global descriptor, but also can perform calculations at a magnificent clip to determine distance with pinpoint accuracy. Additionally, we suggested a way to extract LFH via GPU (OpenGL and GLSL). In this experiment, we have compared the LFH with SIFT (local method) and EHD (global method) via storage capacity, extraction and retrieval time along with accuracy.