• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2580-2582
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• 2004

In this paper, a Neural Network(NN) based approach for classification of two dimensions images. The proposed algorithm is able to apply in the actual industry. The described diagnostic algorithm is presented to defect surface failures on tiles. A way to get data for a digital image process is several kinds of it. The tiles are scanned and the digital images are preprocessed and classified using neural networks. It is important to reduce the amount of input data with problem specific preprocessing. The auto-associative neural network is used for feature generation and selection while the probabilistic neural network is used for classification. The proposed algorithm is evaluated experimentally using one hundred of the real tile images. Sample image data to preprocess have histogram. The histogram is used as input value of probabilistic neural network. Auto-associative neural network compress input data and compressed data is classified using probabilistic neural network. Classified sample images are determined by human state. So it is intervened human subjectivity. But digital image processing and neural network are better than human classification ability. Therefore it is very useful of quality control improvement.

• The Journal of Information Technology
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• v.6 no.1
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• pp.71-84
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• 2003

In this paper, it is presented the efficient system for data recognition using the proposed binary code images. The proposed algorithm finds the position of binary image. Through the process of the block region classification, it is classified each block with the edge region using the value of gray level only. Each block region is divided horizontal and vertical edge region. If horizontal edge region blocks are classified over six blocks in any region, the proposed algorithm should search the vertical edge region in the start point of the horizontal edge region. If vertical edge region blocks were found over ten blocks in vertical region, the code image would found. Practical code region is acquired from the rate of the total edge region that is computed from the binary image that is processed with the average value. In case of the wrong rate, it is restarted the code search in the point after start point and the total process is followed. It has a short time than the before process time because it had classified block information. The block processing is faster thant the total process. The proposed system acquires the image from the digital camera and makes binary image from the acquired image. Finally, the proposed system extracts various characters from the binary image.

• Korean Journal of Remote Sensing
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• v.25 no.3
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• pp.233-242
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• 2009

This paper presents two different types of supervised classifiers such as support vector machine (SVM) and spectral angle mapper (SAM). The Compact Airborne Spectrographic Imager (CASI) high resolution aerial image was classified with the above two classifier. The image was classified into eight land use /land cover classes. Accuracy assessment and Kappa statistics were estimated for SVM and SAM separately. The overall classification accuracy and Kappa statistics value of the SAM were 69.0% and 0.62 respectively, which were higher than those of SVM (62.5%, 0.54).

• Journal of the Korea Fashion and Costume Design Association
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• v.19 no.4
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• pp.17-27
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• 2017

This study analyzes the trends of photo image components and forms of expression in global fashion advertising photos. First, photo image components are classified into seven categories: location (indoor-outdoor), the model's movement, pose, facial expression, gender, race and number of models. The forms of expression are classified into six categories: direct expression, sensual expression, symbolic expression, storytelling expression, dramatic expression, and sexual expression. With the aforementioned classifications, the trends were studied for three years from 2013 to 2015. The analysis result indicates the following: for the details of photo image components, the portion of indoor photos, static poses and conscious facial expressions was over 60% of the total for every season of the 3 years, while there was a slight increase in the number of models and the diversity of races. For the forms of expression, the sensual expression showed the largest portion accounting for over 50% of the total, followed by direct expression and storytelling expression. The findings from this study show that the trends of photo image components and forms of expression in global fashion advertisements are changing. Therefore, domestic companies will need to develop photo image components and forms of expression in line with the changing global fashion advertisement trends.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.3
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• pp.85-93
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• 2015

This paper mainly introduces the methods of extracting landslide information using ALOS(Advanced Land Observing Satellite) images and GIS(Geographical Information System) technology. In this study, we classified images using three different methods which are the unsupervised the supervised and the PCA(Principal Components Analysis) for extracting landslide information based on characteristics of ALOS image. From the image classification results, we found out that the quality of classified image extracted with PCA supervised method was superior than the other images extracted with the other methods. But the accuracy of landslide information extracted from this image classification was still very low as the pixels were very similar between the landslide and safety regions. It means that it is really difficult to distinguish those areas with an image classification method alone because the values of pixels between the landslide and other areas were similar, particularly in a region where the landslide and other areas coexist. To solve this problem, we used the LSM(Landslide Susceptibility Map) created with ArcView software through weighted overlay GIS method in the areas. Finally, the developed LSM was applied to the image classification process using the ALOS images. The accuracy of the extracted landslide information was improved after adopting the PCA and LSM methods. Finally, we found that the landslide region in the study area can be calculated and the accuracy can also be improved with the LSM and PCA image classification methods using GIS tools.

• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.1
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• pp.17-27
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• 2021

The pursuit of a healthier life has created life style changes through exercise; in addition, an athleisure look as well as a combination of everyday clothes and sportswear has rapidly spread through sharing based on image-based SNS. Fashion related images shown in an image-based SNS are considered important resources for grasping micro-needs with regard to the sensibility of consumers. Therefore, this study analyzes the design characteristics of an athleisure look shown in image-based SNS. In order to analyze the athleisure look, images of the entire garment were collected and classified to enable content analysis methods that analyzed the design characteristics of each type. As a result, types were classified as sporty-athleisure, modern-athleisure, high-end athleisure, retro-athleisure, and romantic-athleisure. Looking at the characteristics of the athleisure look, it was shown that the design characteristics of each type were well expressed through differences in the direction, material, and details by matching between the items used. This study can be used in design development processes by deriving the characteristics of athleisure looks through an analysis of fashion images that appear in image-based SNS.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.225-228
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• 2003

The purpose of this paper is to develop a semi -automated system for medical image processing with which tissues or organs from medical images can be segmented and classified by people who have basic knowledge of image processing. In addition, the proposed medical image processing system is independent on types of human tissues or images. In this paper, a new semi-automated image processing system with essential image processing functions for medical images is introduced

• Journal of the Korean Society of Clothing and Textiles
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• v.18 no.2
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• pp.170-179
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• 1994

The purpose of this study was to identify the constructing factors and the hierarchy of the female clothing image evaluation made by male university students. 'rho instruments developed by the precedent study of In Hee Chung(1992) was used compare the female clothing image evaluation made by male university students with that by (emale students. The results were 1. 5 factor - modernity, grace, activeness, uniqueness, masculinity were found out as constructors of female clothing image evaluation made by male university student. 2. Eleven clusters were determinted to exist. The clusters classified as the main groups were 'modem and romantic image' and 'classic and straight image.'

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.2
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• pp.193-204
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• 2002

Many fashion forecasting companies propose the fashion colors in every season. Modern fashion consumer respond to fashionable trends with utmost sensitivity. Therefore to satisfy the consumer with an trendy image, the fashion design must be found first, as image matters, followed by an analysis of each design element's effect on the total image composition. In previous studies of fashion image, has been discussed the positive correlation between fashion design elements of color, fabric, and form as the central issue. In this thesis, two of the fashion design elements, color and fabric are simultaneously considered to classify the image of fabric in fashion. For the color variables, 10 hues are selected from Munsell's system of color notation, and 12 tones from PCCS color notation., which are currently used in the domestic fashion industry. Texture variables used in this survey are classified by luster, prominence-depression of surface, thickness, and density of fabric. Graduate students from 20 to 50 years old and the specialists in fashion companies participated in the survey. The results of this survey are as follows: 1. The fashion fabric image is classified as 5 main images: 'elegant', 'comfortable', 'characteristic', 'light'and 'simple'. 2. The influence of hue, tone and texture is significant to the fashion fabric image. Following colors, yellow-red, red hues and light grayish, dark grayish tones convey the elegant image. The texture property for the elegant image is luster, thin and low density. Properties of fabric conveying the comfortable image are yellow-red and green-yellow hue, soft, light tones, matte and high density. Furthermore, hue turned out to be a insignificant variables for the unique image, whereas dark grayish, grayish tone, luster and prominent texture convey a unique image. For light image, properties of fabric are blue-green, purple hues, light, bright tones with thin, low density texture. Properties of fabric conveying the simple image are blue-green, purple-blue, green-yellow hues, and strong, vivid tones, with luster and flat texture.

• Spatial Information Research
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• v.10 no.2
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• pp.331-343
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• 2002

Recently, as various fields of applications using space-borne imagery have been emphasized, interests on integrated analysis or fusion using multi-sources are also increasing. In this study, to investigate applicability of multiple imageries for further regional-scaled application, DN value analysis and multi-resolution classification by using KOMPSAT EOC imagery and Landsat 7 ETM+image data in the Namyangju-city area were performed, and then this classified results were compared to land-use thematic data at the same area. In case of classified results by using muff-resolution image data, it is shown that linear-type features can be easily extracted. furthermore, it is expected that multi-resolution classified image can be effectively utilized to urban environment analysis, according to results of similar pattern by comparative study based on multi-buffered zone analysis or so-called distance analysis along main road features in the study area.