• The Research Journal of the Costume Culture
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• v.22 no.3
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• pp.399-410
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• 2014

The purpose of this study was to investigate emotional images and preference of knitwear by tone on tone combination. The subjects were 357 university students in Daejeon and Chungnam province, and the measuring instruments were 6 stimuli manipulated by color and tone combination type of background and pattern in the tone and tone combination, and self-administrated questionnaires consisted of emotional images items, preference items, and subjects' demographics attributions. The data were analyzed by Cronbach's ${\alpha}$, factor analysis, t-test, MANOVA and Duncan's multiple range test, using SPSS program. The results were as follows. First, four factors (attractiveness, conspicuity, mildness, and activity) are emerged on emotional images of knitwear. Second, color had main effects on emotional images and preference. Gray color was perceived as most attractive image and more preferred than others. Third, tone combination type had some effects on emotional images. Vivid tone background/light tone pattern was perceived more attractive image but less conspicuous and mild than light tone background/vivid tone pattern. Forth, subjects' gender had an effects on conspicuous image. Male was perceived more conspicuous image on knitwear stimuli than female. Fifth, color and subjects' gender had interaction effects on attractiveness image and preference. Male perceived that blue is more attractive and preferred than female.

• International Journal of CAD/CAM
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• v.9 no.1
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• pp.37-46
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• 2010

Existing methods for the registration of blurred images are efficient for the artificially blurred images or a planar registration, but not suitable for the naturally blurred images existing in the real image mosaic process. In this paper, we attempt to resolve this problem and propose a method for a distortion-free stitching of naturally blurred images for image mosaic. It adopts a multi-resolution and robust feature based inter-layer mosaic together. In each layer, Harris corner detector is chosen to effectively detect features and RANSAC is used to find reliable matches for further calibration as well as an initial homography as the initial motion of next layer. Simplex and subspace trust region methods are used consequently to estimate the stable focal length and rotation matrix through the transformation property of feature matches. In order to stitch multiple images together, an iterative registration strategy is also adopted to estimate the focal length of each image. Experimental results demonstrate the performance of the proposed method.

• Journal of the Korean Society of Costume
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• v.51 no.7
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• pp.75-92
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• 2001

The purpose of this study was to investigate the grandparents' clothing image perceived by adolescents. Self-administered questionnaires were distributed and collected from 547 middle school and high school students from March to April in 2000. Frequencies, percentages, mean. and standard deviation were calculated. χ²-test, t-test, F-test, and ANOVA were used for group differences, and Duncan's multiple range test was used. The results were as follows : 1. Grandparents' clothing images were classified into four images of elegant, comfortable, fashionable and active. 2. Adolescents in middle school, in high economic status, in higher academic achievement, and who pursuited fashion perceived favorable grandparents' clothing images as more favorable. Adolescents perceived clothing images of younger grandparents with higher economic status, or grandmother as more favorable. Adolescents who had a good affection and a good-cognition to their grandparents perceived their clothing images as more favorable. Adolescents perceived their mother-side-grandparents clothing images as more favorable. 3. Grandparents were segmented into 3 groups of the fashion pursuit, the utility pursuit, and the stagnated according to their adolescents' clothing image perception. The adolescents in higher economic status, who pursuited fashion, and had a favorable cognition to their grandparents tended to perceive their grandparents as the fashion pursuit. Adolescents who had a high affection to their grandparents tended to Perceive their grandparents as the utility pursuit. Adolescents perceived their mother side grandparents as the utility pursuit.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.321-326
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• 2019

As AI technology is recently introduced into various fields, it is being applied to the fashion field. This paper proposes a system for recommending cody clothes suitable for a user's selected clothes. The proposed system consists of user app, cody recommendation module, and server interworking of each module and managing database data. Cody recommendation system classifies clothing images into 80 categories composed of feature combinations, selects multiple representative reference images for each category, and selects 3 full body cordy images for each representative reference image. Cody images of the representative reference image were determined by analyzing the user's preference using Google survey app. The proposed algorithm classifies categories the clothing image selected by the user into a category, recognizes the most similar image among the classification category reference images, and transmits the linked cody images to the user's app. The proposed system uses the ResNet-50 model to categorize the input image and measures similarity using ORB and HOG features to select a reference image in the category. We test the proposed algorithm in the Android app, and the result shows that the recommended system runs well.

• Korean Journal of Remote Sensing
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• v.10 no.2
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• pp.133-160
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• 1994

In this paper, we propose a comprehensive geometric correction algorithm of Along Track Scanning Radiometer(ATSR) images. The procedure consists of two cascaded modules; precorrection and fine co-location. The pre-correction algorithm is based on the navigation model which was derived in mathematical forms. This model was applied for correction raw(un-geolocated) ATSR images. The non-systematic geometric errors are also introduced as the limitation of the geometric correction by this analytical method. A fast and automatic algorithm is also presented in the paper for co-locating nadir and forward views of the ATSR images by using a binary cross-correlation matching technique. It removes small non-systematic errors which cannot be corrected by the analytic method. The proposed algorithm does not require any auxiliary informations, or a priori processing and avoiding the imperfect co-registratio problem observed with multiple channels. Coastlines in images are detected by a ragion segmentation and an automatic thresholding technique. The matching procedure is carried out with binaty coastline images (nadir and forward), and it gives comparable accuracy and faster processing than a patch based matching technique. This technique automatically reduces non-systematic errors between two views to .$\pm$ 1 pixel.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.1
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• pp.12-21
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• 2021

For successful automatic target recognition(ATR) with synthetic aperture radar(SAR) imagery, SAR target images of the database should have the identical or highly similar resolution with those collected from SAR sensors. However, it is time-consuming or infeasible to construct the multiple databases with different resolutions depending on the operating SAR system. In this paper, an approach for resolution conversion of SAR target images is proposed based on conditional generative adversarial network(cGAN). First, a number of pairs consisting of SAR target images with two different resolutions are obtained via SAR simulation and then used to train the cGAN model. Finally, the model generates the SAR target image whose resolution is converted from the original one. The similarity analysis is performed to validate reliability of the generated images. The cGAN model is further applied to measured MSTAR SAR target images in order to estimate its potential for real application.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.143-147
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• 2024

Image noise classification is a classical problem in the field of image processing, machine learning, deep learning and computer vision. In this paper, image noise classification is performed using deep learning. Keras deep learning library of TensorFlow is used for this purpose. 6900 images images are selected from the Kaggle database for the classification purpose. Dataset for labeled noisy images of multiple type was generated with the help of Matlab from a dataset of non-noisy images. Labeled dataset comprised of Salt & Pepper, Gaussian and Sinusoidal noise. Different training and tests sets were partitioned to train and test the model for image classification. In deep neural networks CNN (Convolutional Neural Network) is used due to its in-depth and hidden patterns and features learning in the images to be classified. This deep learning of features and patterns in images make CNN outperform the other classical methods in many classification problems.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1835-1838
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• 2003

Texts in natural images contain significant and detailed informations about the images. Thus, to extract those texts correctly, we suggest a text extraction method using color variance feature. Generally, the texts in images have color variations with the backgrounds. Thus, if we express those variations in 3 dimensional RGB color space, we can emphasize the text regions that can be hard to be captured with a method using intensity variations in the gray-level images. We can even make robust extraction results with the images contaminated by light variations. The color variations are measured by color variance in this paper. First, horizontal and vertical variance images are obtained independently, and we can fine that the text regions have high values of the variances in both directions. Then, the two images are logically ANDed to remove the non-text components with only one directional high variance. We have applied the proposed method to the multiple kinds of the natural images, and we confirmed that the proposed feature can help to find the text regions that can he missed with the following features - intensity variations in the gray-level images and/or color continuity in the color images.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1071-1081
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• 2021

Remote Sensing using unmanned aerial vehicles(UAV) can acquire images with higher time resolution and spatial resolution than aerial and satellite remote sensing. However, UAV images are photographed at low altitude and the area covered by one image isrelatively narrow. Therefore multiple images must be processed to monitor large area. Since UAV images are photographed under different exposure conditions, there is difference in brightness values between adjacent images. When images are mosaicked, unnatural seamlines are generated because of the brightness difference. Therefore, in order to generate seamless mosaic image, a radiometric processing for correcting difference in brightness value between images is essential. This paper proposes a relative radiometric calibration and image blending technique. In order to analyze performance of the proposed method, mosaic images of UAV images in agricultural and mountainous areas were generated. As a result, mosaic images with mean brightness difference of 5 and root mean square difference of 7 were avchieved.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.706-709
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• 2002

This paper describes an effective technique for coding video sequences based on JPEG2000 codec. In the proposed method, multiple frames are combined into one large picture by interleaving each pixel data. A large picture enables images to be coded more efficiently and image quality is improved. A video sequence is efficiently coded by adapting the time correlation of the video sequences to spatial correlation. We demonstrated the effectiveness of this method by encoding video sequences using JPEG2000.