• 로봇학회논문지
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• 제11권3호
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• pp.115-126
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• 2016

This paper suggests the method of the spherical signature description of 3D point clouds taken from the laser range scanner on the ground vehicle. Based on the spherical signature description of each point, the extractor of significant environmental features is learned by the Deep Belief Nets for the urban structure classification. Arbitrary point among the 3D point cloud can represents its signature in its sky surface by using several neighborhood points. The unit spherical surface centered on that point can be considered to accumulate the evidence of each angular tessellation. According to a kind of point area such as wall, ground, tree, car, and so on, the results of spherical signature description look so different each other. These data can be applied into the Deep Belief Nets, which is one of the Deep Neural Networks, for learning the environmental feature extractor. With this learned feature extractor, 3D points can be classified due to its urban structures well. Experimental results prove that the proposed method based on the spherical signature description and the Deep Belief Nets is suitable for the mobile robots in terms of the classification accuracy.

• 한국의류산업학회지
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• 제12권5호
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• pp.582-590
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• 2010

Garment design needs an iterative manipulation of 2D patterns to generate a final sloper. Traditionally there have been two kinds of design methodologies such as the flat pattern method and the pattern draping method. But today, it is possible to combine the advantages from the two methods due to the realistic cloth simulation techniques. We devised a new garment design system which starts from 3D initial drape simulation result and then modifies the garment by editing the 2D flat patterns synchronously. With this interactive methodology using real-time pattern deformation technique, the designer can freely change a pattern shape by watching its 3D outlook in real-time. Also the final garment data were given relative coordinates with respect to the human anthropometric feature points detected by an automatic body feature detection algorithm. Using the relative human body coordinate system, the final garments can be re-used to an arbitrary body data without repositioning in the drape simulation. A female shirt was used for an example and a 3D body scan data was used for an illustration of the feature point detection algorithm.

• 정보처리학회논문지B
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• 제10B권7호
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• pp.823-828
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• 2003

이 논문의 주요 목적은 내용을 기반으로 하는 이미지 검색에서 이미지 객체의 외형특징을 추출하는 방법을 제시하는 것이다. 대부분의 실질적인 객체들의 외형은 불규칙적이고, 이러한 객체를 수치화하기위한 일반적인 방법은 없다. 특히 전자 카타로그들은 상품들을 나타내는 많은 이미지를 포함하고 있다. 이 논문에서는 이미지 전체가 아닌 이미지내의 개별 객체들을 기반으로 특징을 추출하는 방법을 제시한다. 왜냐하면 제시된 방법은 한 이미지내에서 RLC lines을 사용하여 각 객체들의 외형을 기반으로하는 방법을 사용하기 때문이다. 실험결과는 일반적으로 가장 많이 사용하는 특징인 Texture와 비교를 했고 제시된 외형을 나타내는 변수들이 전자카타로그의 이미지 객체들을 뚜렷하게 나타냈고, 보다 정확하게 객체들을 분류하고 구별하였다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제35권9호
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• pp.572-587
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• 2008

SOFM(Self-organizing Feature Map)은 고차원의 데이타를 군집화(clustering)하거나 시각화(visualization)하기 위해 많이 사용되고 있는 비교사 학습 신경망(unsupervised neural network)의 한 종류이며, 컴퓨터비전이나 패턴인식 분야에서 다양하게 활용되고 있다. 최근 SOFM이 실제 응용분야에 다양하게 활용되고 좋은 결과를 보이고 있지만, 학습된 SOFM의 뉴론(neuron)을 다시 군집화해야 하는 후처리가 필요하며, 대부분의 경우 수동으로 이루어지고 있다. 후처리를 자동으로 하기 위해 k-means와 같은 기존의 군집화 알고리즘을 많이 이용하지만, 이 방법은 특히 다양한 모양의 클래스를 가진 고차원의 데이타에서 만족스럽지 못한 결과를 보인다. 다양한 모양의 클래스에서 좋은 성능을 보이기 위해, 본 논문에서는 그래프 컷(graph cut)을 이용하여 학습된 SOFM을 자동으로 군집화하는 방법을 제안한다. 그래프 컷을 이용할 때 터미널(terminal)이라는 두 개의 추가적인 정점(vertex)이 필요하며, 터미널과 각 정점 사이의 가중치는 대부분 사용자에 의해 입력받은 사전정보를 기반으로 설정된다. 제안된 방법은 SOFM의 거리 매트릭스(distance matrix)를 기반으로 한 모드 탐색(mode-seeking)과 모드의 군집화를 통하여 자동으로 사전정보를 설정하며, 학습된 SOFM의 군집화를 자동으로 수행한다. 실험에서 효율성을 검증하기 위해 제안된 방법을 텍스처 분할(texture segmentation)에 적용하였다. 실험 결과에서 제안된 방법은 기존의 군집화 알고리즘을 이용한 방법보다 높은 정확도를 보였으며, 이는 그래프기반의 군집화를 통해 다양한 모양의 클러스터를 처리할 수 있기 때문이다.

• 패션비즈니스
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• 제15권1호
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• pp.92-102
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• 2011

The purpose of this research is to study the types of name spectrum and their characteristics of domestic men's wear brands focusing on formal and casual wear and also examine the differences based on the type of brands' product and brand style. Total 184 of men's wear brands, which were consisted of 66 formal wear brands and 84 casual wear brands, were selected from '2009 Korea Fashion Yearbook'. For data analysis, quantitatively evaluated the frequency and qualitatively evaluated the image of brand product and the meaning of brand name. The result as follows; 1. The domestic fashion brands for men's wear appeared to have four types of name spectrum. The descriptive name was the most frequently showed, and followed by arbitrary, suggestive, and coined name. For formal wear brands, four types of name spectrum were appeared in the order of descriptive, suggestive, coined, and arbitrary name. In casual wear brands, three types of name spectrum were appeared in the order of descriptive, arbitrary, and suggestive name. 2. The characteristics of men's brand name according to their name spectrum was as follows.; In the descriptive brand names, person's name was used the most and some ascribed the characteristics, feature or geographic location of the product. The suggestive brand names contained images and symbols of the product and also implied the relevant benefit information in a particular product context. In the arbitrary brand name, they imply the various meanings according to the product and are made up of either coined or natural. For the coined name, some bear the ideology or symbolized the characteristics of product itself. 3. The descriptive name spectrum showed the most in domestic menswear brands, regardless of the brand type. Except this, there were differences in the type and the frequency of name spectrum depending on the brand type.

• Genomics & Informatics
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• 제17권4호
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• pp.39.1-39.20
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• 2019

Analyzing patterns in data points embedded in linear and non-linear feature spaces is considered as one of the common research problems among different research areas, for example: data mining, machine learning, pattern recognition, and multivariate analysis. In this paper, data points are heterogeneous sets of biosequences (composite data points). A composite data point is a set of ordinary data points (e.g., set of feature vectors). We theoretically extend the derivation of the largest generalized eigenvalue-based distance metric D_ij(γ₁) in any linear and non-linear feature spaces. We prove that D_ij(γ₁) is a metric under any linear and non-linear feature transformation function. We show the sufficiency and efficiency of using the decision rule $\bar{{\delta}}_{{\Xi}i}$(i.e., mean of D_ij(γ₁)) in classification of heterogeneous sets of biosequences compared with the decision rules min_𝚵iand median_𝚵i. We analyze the impact of linear and non-linear transformation functions on classifying/clustering collections of heterogeneous sets of biosequences. The impact of the length of a sequence in a heterogeneous sequence-set generated by simulation on the classification and clustering results in linear and non-linear feature spaces is empirically shown in this paper. We propose a new concept: the limiting dispersion map of the existing clusters in heterogeneous sets of biosequences embedded in linear and nonlinear feature spaces, which is based on the limiting distribution of nucleotide compositions estimated from real data sets. Finally, the empirical conclusions and the scientific evidences are deduced from the experiments to support the theoretical side stated in this paper.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.42.6-42
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• 2002

Kohonen's self organizing feature map (SOFM) converts arbitrary dimensional patterns into one or two dimensional arrays of nodes. Among the many competitive learning algorithms, SOFM proposed by Kohonen is considered to be powerful in the sense that it not only clusters the input pattern adaptively but also organize the output node topologically. SOFM is usually used for a preprocessor or cluster. It can perform dimensional reduction of input patterns and obtain a topology-preserving map that preserves neighborhood relations of the input patterns. The traditional SOFM algorithm[1] is a competitive learning neural network that maps inputs to discrete points that are called nodes on a lattice...

• 대한전기학회논문지:시스템및제어부문D
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• 제49권6호
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• pp.307-315
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• 2000

For the autonomy of a mobile robot it is first needed to know its position and orientation. Various methods of estimating the position of a robot have been developed. However, it is still difficult to localize the robot without any initial position or orientation. In this paper we present the method how to make the colored map and how to calculate the position and direction of a robot using the angle data of an omnidirectional image. The wall of the map is rendered with the corresponding color images and the color histograms of images and the coordinates of feature points are stored in the map. Then a mobile robot gets the color omnidirectional image at arbitrary position and orientation, segments it and recognizes objects by multiple color indexing. Using the information of recognized objects robot can have enough feature points and localize itself.

• 복식
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• 제62권6호
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• pp.200-217
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• 2012

The purpose of this research is to examine the specificity of grids to define the characteristics of clothes styles in the Joseon Dynasty period. The significance of examining of the specificity of grids is to find out arbitrary types of the features of grids involved in structuring the Jeogori in the Joseon Dynasty period one by one. The Visual Linguistic Theory was introduced as a methodological tool to exquisitely analyze the characteristics of grids in deep structures of Jeogori in the Joseon Dynasty period. This theory strives to examine sample distribution, the distribution of samples by quality and the distribution of the types of ploidy features. Through the examination, the results are as follows. The grid systems of the Jeogori consisted of diverse proportion systems reaching 86 cases, that is, sequence systems composed of multi-functional, multi-combined bodies. Most ornamental grids had feature angles distributed in a range of $2-20^{\circ}$ that showed a common preference for low sloped diagonal lines or small curvature. Although the preference for certain feature angles were prominent, the feature angles that were used were generally distributed evenly among diverse feature angles to show the characteristics of separation. Therefore, Jeogori makers in the Joseon Dynasty period can be considered as having experimented with many proportion systems to show their aesthetics. In conclusion, based on the results of the examination of feature distributions and related methods to allocate ploidy features, O-type accounted for 66% and thus it was identified that the Jeogori was characterized by O-type. Therefore, it was identified that the characteristic of the Jeogori in the Joseon Dynasty period consisted of O-type fractal structures which are formative structures unique to our nation.

• 디지털콘텐츠학회 논문지
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• 제15권6호
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• pp.701-710
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• 2014

본 논문에서 중심적으로 논의하고 있는 일본 장편 애니메이션의 의미작용 체계에 대한 접근은 궁극적으로 일본 장편 애니메이션에서 보이는 표현과 소통의 특징을 분석하는 것이다. 구체적인 스토리를 중심으로 하는 대부분의 캐릭터 애니메이션 작품은 의미작용 체계에 있어서 외시의미와 함축의미를 동시에 담고 있지만, 각 작품에서 추구하는 의미작용 체계의 특징에 따라서 미학적 표현과 소통의 경향이 다르게 나타난다. 본 논문은 방법론적으로 일본의 문화사회적 배경과 특징을 토대로 하여 일본 애니메이션에 나타나는 의미작용을 살펴보고 있다. 일본의 장편 애니메이션은 대체로 함축의미적인 의미작용 체계와 미학적 표현을 강조하며, 외시의미와 몰입을 강화하는 디즈니의 장편 애니메이션들과는 달리 관객 스스로가 작품에 담겨진 함축의미를 고찰하고 해석하도록 하는 경향이 두드러지게 나타난다. 그러므로 관객의 문화사회적 배경과 경험에 따라서 일본 장편 애니메이션이 전달하고자 하는 주제와 메시지, 그리고 추구하는 신화에 대한 임의적 해석과 수용이 발생할 수도 있다. 의미작용에 있어서 그러한 해석과 수용의 폭넓은 가능성은 문제점이라기보다는 일본 장편 애니메이션이 가지고 있는 차별적인 표현과 소통의 특징으로 보는 것이 바람직할 것이다.