• Journal of KIISE
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• v.43 no.7
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• pp.812-819
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• 2016

This paper introduces and analyzes the theoretical basis and method of the conventional initial-value assignment problem and feasibility of image segmentation. The paper presents topological evidence and a method of appropriate initial-value assignment based on topology theory. Subsequently, the paper shows minimum conditions for feasibility of image segmentation based on separation axiom theory of topology and a validation method of effectiveness for image modeling. As a summary, this paper shows image segmentation with its mathematical validity based on topological analysis rather than statistical analysis. Finally, the paper applies the theory and methods to conventional Gaussian random field model and examines effectiveness of GRF modeling.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.3_4
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• pp.402-411
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• 2002

The objective of this study was to investigate sensibility images of Korean traditional motifs cognized by college students in U.S.A. The subjects consisted of 217 male and 351 female undergraduate students. The experimental materials used in this study were 48 stimuli and questionnaires, composed of 7-point semantic differential scales of 15 bipolar adjectives. Twelve motifs selected from 3 groups of Korean motifs were used as motif stimuli. Twelve repeated patterns were constructed from them to be applied on a CAD-simulated dress. The data were analyzed by factor analysis, ANOVA, Duncan's multiple range test. The major finding were as follows: 1. Four dimensions were emerged accounting for the dimensional structure of the images of Korean traditional motifs. These dimensions were ‘quality’, ‘simplicity’, ‘cheerfulnees’, and ‘modernity’. Among them, ‘quality’and ‘simplicity’were the major dimensions. 2. Category, interpretation type, composition type, and application object had significant effects on the images of above-mentioned dimensions. The interpretation type had a significant effect on ‘quality’image, the composition type on ‘cheerful’image, and the application object on ‘modernity’image.

• Korean Journal of Optics and Photonics
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• v.2 no.1
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• pp.1-6
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• 1991

This paper describes the theoretical analysis of a diffraction pattern analyzer for the characterization of microscale object fields and a method for obtaining size and size distribution from the measured diffraction pattern of the object fields. For the experimental verification, a typical optical Fourier transform system was set up and calibrated with 2 5$\mu \textrm m$ and 50$\mu \textrm m$ pinholes. The system responses to distilled water droplets, alcohol, glycerin and silicon oil were imaged with vidicon, and the image was processed to determine the size distribution of each liquid particle field. The energy distribution function which is defined as the total intensity of a circular ring in the diffraction pattern was used to determine the dominant particle size of each liquid particle field.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.2
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• pp.109-118
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• 2022

GPR (Ground Penetrating Radar), developed as a technology for geotechnical investigations such as sinkhole exploration, was used limitedly as a method to resolve undetectable lines in underground facility exploration. To improve the accuracy of underground facility data, the government made it possible to explore underground facilities using a non-metallic pipeline probe from July 2022. However, GPR has a problem in that the exploration rate is lowered in the soil with high moisture content, such as soft soil, such as clay layer, and there is a lot of variation in long-term accuracy. In this study, as a way to improve the accuracy of exploration considering the characteristics of GPR and the environment of underground facilities, we propose a GPR exploration method for underground facilities using permittivity constant correction and pattern analysis of GPR image data. Through this study, the accuracy of underground facility exploration and high reproducibility were derived as a result of field verification applying GPR frequency band and heterogeneous GPR.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.251-256
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• 2004

The image classification is one of the most important studies in the remote sensing. In general, the MLC(Maximum Likelihood Classification) classification that in consideration of distribution of training information is the most effective way but it produces a bad result when we apply it to actual hyperspectral image with the same classification technique. The purpose of this research is to reveal that which one is the most effective and suitable way of the classification algorithms iii the hyperspectral image classification. To confirm this matter, we apply the MLC classification algorithm which has distribution information and SAM(Spectral Angle Mapper), SFF(Spectral Feature Fitting) algorithm which use average information of the training class to both multispectral image and hyperspectral image. I conclude this result through quantitative and visual analysis using confusion matrix could confirm that SAM and SFF algorithm using of spectral pattern in vector domain is more effective way in the hyperspectral image classification than MLC which considered distribution.

• Journal of IKEEE
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• v.6 no.1 s.10
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• pp.10-20
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• 2002

The purpose of this work is to develop a system fer rapid and automatic identification of persons, with high reliability and confidence levels. The iris of the eye is used as an optical fingerprint, having a highly detailed pattern that is unique for each individual and stable over many years. Image analysis algorithms find the iris in a image, and encode its texture into an iris code. Iris texture is extracted from the image at multiple scales of analysis by wavelet transformation. The features of many different parts of the iris are projected onto the space-frequency space. They are used to determine an abstract iris code which is similar to 2D barcode. Pattern recognition is achieved by using modified CPN.

• International Journal of Automotive Technology
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• v.6 no.1
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• pp.53-63
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• 2005

We propose a fuzzy neural network (FNN) theory capable of deciding the quality of a road image prior to extracting lane-related information. The accuracy of lane-related information obtained by image processing depends on the quality of the raw images, which can be classified as good or bad according to how visible the lane marks on the images are. Enhancing the accuracy of the information by an image-processing algorithm is limited due to noise corruption which makes image processing difficult. The FNN, on the other hand, decides whether road images are good or bad with respect to the degree of noise corruption. A cumulative distribution function (CDF), a function of edge histogram, is utilized to extract input parameters from the FNN according to the fact that the shape of the CDF is deeply correlated to the road image quality. A suitability analysis shows that this deep correlation exists between the parameters and the image quality. The input pattern vector of the FNN consists of nine parameters in which eight parameters are from the CDF and one is from the intensity distribution of raw images. Experimental results showed that the proposed FNN system was quite successful. We carried out simulations with real images taken in various lighting and weather conditions, and obtained successful decision-making about $99\%$ of the time.

• Journal of ILASS-Korea
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• v.20 no.1
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• pp.1-6
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• 2015

Visualization of the mixing pattern in a non-element mixer was carried out using laser induced fluorescence(LIF) to evaluate characteristics of mixer consisting of the main flow pipe and branch flow pipes. The branch flows were injected periodically with the period $T_{in}$ normal to the main flow, and rhodamine B was mixed into the most upstream branch flow to visualize mixing pattern in the main flow pipe by LIF. The length of boundary line L of the LIF image was measured. In this study, a numerical analysis was performed to identify the mixing process of the non-element mixer, and the results were compared with experimental results. Each result was almost the same. When the number of branch flows is increased, the mixing pattern became complicated and was supposed to become chaotic. The length of boundary line L increased exponentially with an increase in the number of branch flows.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.11
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• pp.43-51
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• 2004

In this paper, we propose a high-level optical encryption system, which is tolerant with noises and cropping, by encrypting the phase-encoded CGH pattern of original image with the phase-encoded Fresnel diffraction pattern of random key images. For encryption, the phase-encoded CGH pattern of original image is multiplied by conjugate components which are the phase-encoded Fresnel diffraction patterns of random key images. The original information can be reconstructed by multiplying encrypted image by phase-encoded Fresnel diffraction pattern of random key images and performing Fourier transform of the multiplication result. The proposed system is robust to noises and cropping due to characteristics of CGH pattern and can guarantee high-level encryption by using Fresnel diffraction information. We verified the validity of proposed system by computer simulations, numerical analysis of noises and cropping effect and optical experiment.

• Korean Journal of Human Ecology
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• v.20 no.2
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• pp.413-426
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• 2011

This research was designed to compare the subjective evaluation of texture image and preference according to fiber blending ratio of men's suit fabrics. 110 subjects evaluated the texture image and preference of various fabrics. For statistical analysis, factor analysis, MDS, pearson correlation and ANOVA were used. The results were as follows: Sensory image factors of suit fabrics were 'smoothness', 'bulkiness', 'stiffness', 'elasticity', 'moistness' and 'weight sensation'. Sensibility image factors were 'classic', 'practical', 'characteristic' and 'sophisticated'. 'Bulkiness' and 'elasticity' sensory images showed high correlations with sensibility images. Fabrics with high wool blending ratio showed as 'classic' and 'sophisticated', 'bulkiness' and 'elasticity' texture images and fabrics with low wool blending ratio showed texture images of 'characteristic', 'surface character', 'stiffness', 'moistness' and 'weight sensation'. Wool fiber blending ratio affected on the purchase preference and tactile preference. Using regression analysis, it was shown that sensibility images had more of an effect on preference than sensory images. The thickness and pattern type showed positive effects and fiber blending ratio showed negative effects on the preference.