• The Journal of Korea Robotics Society
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• v.11 no.3
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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.

• Fashion & Textile Research Journal
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• v.12 no.5
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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.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.823-828
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• 2003

The main objective of this paper is to provide a methodology of feature extraction using shape of image objects for content-based image retrieval. The shape of most real-life objects is irregular, and hence there is no universal approach to quantify the shape of an arbitrary object. In particular. electronic catalogs contain many image objects for their products. In this paper, we perform feature extraction based on individual objects in images rather than on the whole image itself, since our method uses a shape-based approach of objects using RLC lines within an image. Experiments show that shape parameters distinctly represented image objects and provided better classification and discrimination among image objects in an image database compared to Texture.

• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.572-587
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• 2008

The Self-organizing Feature Map(SOFM) that is one of unsupervised neural networks is a very powerful tool for data clustering and visualization in high-dimensional data sets. Although the SOFM has been applied in many engineering problems, it needs to cluster similar weights into one class on the trained SOFM as a post-processing, which is manually performed in many cases. The traditional clustering algorithms, such as t-means, on the trained SOFM however do not yield satisfactory results, especially when clusters have arbitrary shapes. This paper proposes automatic clustering on trained SOFM, which can deal with arbitrary cluster shapes and be globally optimized by graph cuts. When using the graph cuts, the graph must have two additional vertices, called terminals, and weights between the terminals and vertices of the graph are generally set based on data manually obtained by users. The Proposed method automatically sets the weights based on mode-seeking on a distance matrix. Experimental results demonstrated the effectiveness of the proposed method in texture segmentation. In the experimental results, the proposed method improved precision rates compared with previous traditional clustering algorithm, as the method can deal with arbitrary cluster shapes based on the graph-theoretic clustering.

• Journal of Fashion Business
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• v.15 no.1
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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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• v.17 no.4
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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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• 2002.10a
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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...

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.6
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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.

• Journal of the Korean Society of Costume
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• v.62 no.6
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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.

• Journal of Digital Contents Society
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• v.15 no.6
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• pp.701-710
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• 2014

The analysis on the characteristics of the signification system of Japanese feature-length animations that this essay centrally deals with eventually examines the characteristics of representation and communication in Japanese feature-length animations. In general, most animation works focusing on characters and stories contain the signification systems related to 'denotation' and connotation.' However, tendency of the aesthetic representation and communication that appears differently, depending on the characteristics of the signification system that each animation work pursues. From this point of view, Japanese feature-length animation emphasizes connotative signification system and aesthetic representation, unlike Disney animation that strongly shows the tendency that makes the audience directly immersed in the theme and message of the work conveyed further in the myths by pursuing denotative signification system. And, in the case of Japanese feature-length animation, the 'dissenting and arbitrary interpretation' of the theme, the message that the animation work intends to convey and myths pursued is bound to appear diversely, depending on the audience's experiences and cultural and social backgrounds.