• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.11 s.158
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• pp.1608-1617
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• 2006

Due to the popularization of fashion, it is important for consumers to find out under what kinds of reasons consumers choose and prefer the clothing products as consumers are interested in clothing and have variety of their styles to choose This study is to classify the self-image of the middle-aged women and examine the characteristics of each group and also to inquire into the evaluation of clothing by each group. Data are collected through a self-administered questionnaire survey from 4 to October 31, 2005 from 350 middle-aged women in Daegu; 275 are used for the data analysis. Data analysis is performed using SPSS Package, which included cluster analysis, factor analysis, ANOVA, Duncan's multiple range test, and chi-square test. The results are as follows: 1. As a result of factor analysis of self-image, the five factors which are intelligent image, social image, fashionable image, female image, bold image are extracted. Besides, as a result of cluster analysis, the three types which are female-type, neuter-type, male-type are classified. 2. The middle-aged women regard the classic style as their best style for outgoing and then they like the casual style, elegant style, dramatic style in order. 3. As a result of factor analysis for clothing image, the four factors which are dignity, attraction, simplicity activity are extracted. 4. According to self-image types, there are differences for clothing image and preferring clothing styles. While female-type groups choose the elegant style, neuter-type groups regard the classic style as their best style and male-type groups regard the casual style as their best style. In case of daring style, the preference shows the lowest among all the types unrelated to self-image types.

• Ocean and Polar Research
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• v.35 no.3
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• pp.239-247
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• 2013

Digital image processing is a process to analyze a large volume of information on digital images. In this study, Artemia hatching rate was measured by automatically classifying and counting cysts and larvae based on color imaging data from cyst hatching experiments using an image processing technique. The Artemia hatching rate estimation consists of a series of processes; a step to convert the scanned image data to a binary image data, a process to detect objects and to extract their shape information in the converted image data, an analysis step to choose an optimal discriminant function, and a step to recognize and classify the objects using the function. The function to classify Artemia cysts and larvae is optimally estimated based on the classification performance using the areas and the plan-form factors of the detected objects. The hatching rate using the image data obtained under the different experimental conditions was estimated in the range of 34-48%. It was shown that the maximum difference is about 19.7% and the average root-mean squared difference is about 10.9% as the difference between the results using an automatic counting (this study) and a manual counting were compared. This technique can be applied to biological specimen analysis using similar imaging information.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.212-218
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• 2022

In this paper, the necessity of AI video interview arises when conducting an interview for acquisition of excellent talent in a non-face-to-face situation due to similar situations such as Covid-19. As a matter to be supplemented in general AI interviews, it is difficult to evaluate the reliability and qualitative factors. In addition, the AI interview is conducted not in a two-way Q&A, rather in a one-sided Q&A process. This paper intends to fuse the advantages of existing AI interviews and video interviews. When conducting an interview using AI image analysis technology, it supplements subjective information that evaluates interview management and provides quantitative analysis data and HR expert data. In this paper, image-based multi-modal AI image analysis technology, bioanalysis-based HR analysis technology, and web RTC-based P2P image communication technology are applied. The goal of applying this technology is to propose a method in which biological analysis results (gaze, posture, voice, gesture, landmark) and HR information (opinions or features based on user propensity) can be processed on a single screen to select the right person for the hire.

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.2
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• pp.526-536
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• 2017

This study intended to understand similarity of image between games and position of image attribute perceived by consumers according to the games of marine sports using MDS (Multidimensional Scaling). Through the foregoing, this study aims to provide preliminary data for establishing marketing strategies for games of marine sports by accurately understanding images of marines sports perceived by consumers. For survey targets, this study selected students of K University located in Gyeonggi-do as a population, and extracted samples using convenience sampling out of non-probability sampling methods targeting 200 students who showed intention of participation in this study, and total 188 sheets of questionnaire were used as final data excepting 12 sheets that are filled up unfaithfully or considered unreliable. For data processing, this study conducted Frequency Analysis, Descriptive Statistical Analysis, Reliability Analysis, MDS (Multidimensional Scaling) and Multiple Regression Analysis, and study results show that water ski and wakeboard(.602) have the most similar image attribute, indicating that image attributes of scuba diving and water ski(2.031) are positioned farthest with each other. As for image attribute, image attribute of water ski has appearance and progressiveness, windsurfing has an image attribute of positive, scuba diving has an image attribute of negation, and marin rafting has an image attribute of friendliness.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.35.4-35
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• 2001

Image detection system for road traffic data analysis is a real time detection system using image processing techniques to get the real-time traffic information which is used for traffic control and analysis. One of the most important functions in this system is to match the coordinates of real world and that of image on video camera. When there in no way to know the exact position of camera and it´s height from the object. If some points on the road of real world are known it is possible to calculate the coordinates of real world from image.

• Korean Journal of Remote Sensing
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• v.6 no.1
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• pp.11-24
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• 1990

This is the study about the meteorological satellite cloud image classification by objective methods. For objective cloud classification, linear discriminant analysis was tried. In the linear discriminant analysis 27 cloud characteristic parameters were retrieved from GMS infrared image data. And, linear cloud classification model was developed from major parameters and cloud type coefficients. The model was applied to GMS IR image for weather forecasting operation and cloud image was classified into 5 types such as Sc, Cu, CiT, CiM and Cb. The classification results were reasonably compared with real image.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.1
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• pp.64-75
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• 2003

Medical image segmentation is the process by which an original image is partitioned into some homogeneous regions like bones, soft tissues, etc. This study demonstrates an automatic medical image segmentation technique based on independent component analysis. Independent component analysis is a generalization of principal component analysis which encodes the higher-order dependencies in the input in addition to the correlations. It extracts statistically independent components from input data. Use of automatic medical image segmentation technique using independent component analysis under the assumption that medical image consists of some statistically independent parts leads to a method that allows for more accurate segmentation of bones from CT data. The result of automatic segmentation using independent component analysis with square test data was evaluated using probability of error(PE) and ultimate measurement accuracy(UMA) value. It was also compared to a general segmentation method using threshold based on sensitivity(True Positive Rate), specificity(False Positive Rate) and mislabelling rate. The evaluation result was done statistical Paired-t test. Most of the results show that the automatic segmentation using independent component analysis has better result than general segmentation using threshold.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2273-2286
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• 2018

This paper describes transformations between elemental image arrays and a RGBD image for three-dimensional integral imaging and transmitting systems. Two transformations are introduced and analyzed in the proposed method. Normally, a RGBD image is utilized in efficient 3D data transmission although 3D imaging and display is restricted. Thus, a pixel-to-pixel mapping is required to obtain an elemental image array from a RGBD image. However, transformations and their analysis have little attention in computational integral imaging and transmission. Thus, in this paper, we introduce two different mapping methods that are called as the forward and backward mapping methods. Also, two mappings are analyzed and compared in terms of complexity and visual quality. In addition, a special condition, named as the hole-free condition in this paper, is proposed to understand the methods analytically. To verify our analysis, we carry out experiments for test images and the results indicate that the proposed methods and their analysis work in terms of the computational cost and visual quality.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.191-203
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• 2022

In this paper, we developed a system that intelligently identifies skin image data from big data collected from social media Instagram and extracts standardized skin sample data for skin condition diagnosis and management. The system proposed in this paper consists of big data collection and analysis stage, skin image analysis stage, training data preparation stage, artificial neural network training stage, and skin image identification stage. In the big data collection and analysis stage, big data is collected from Instagram and image information for skin condition diagnosis and management is stored as an analysis result. In the skin image analysis stage, the evaluation and analysis results of the skin image are obtained using a traditional image processing technique. In the training data preparation stage, the training data were prepared by extracting the skin sample data from the skin image analysis result. And in the artificial neural network training stage, an artificial neural network AnnSampleSkin that intelligently predicts the skin image type using this training data was built up, and the model was completed through training. In the skin image identification step, skin samples are extracted from images collected from social media, and the image type prediction results of the trained artificial neural network AnnSampleSkin are integrated to intelligently identify the final skin image type. The skin image identification method proposed in this paper shows explain high skin image identification accuracy of about 92% or more, and can provide standardized skin sample image big data. The extracted skin sample set is expected to be used as standardized skin image data that is very efficient and useful for diagnosing and managing skin conditions.

• Proceedings of the Korea Society of Design Studies Conference
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• 2001.10a
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• pp.60.2-60
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• 2001