• Journal of the Korean Society of Costume
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• v.51 no.4
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• pp.141-163
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• 2001

This study was to analyse the meanings of the appropriation inherited in the modern costumes from the latter of the 20th century to the present. According to the dictionary, the meanings of a word. "appropriation" is to steal something, used in order to avoid saying this directly. The sorts of the appropriation represented in the works of Art was as follow : First. the reconstruction by the imitation of the works of a great master or partly induction of the works of a great master Second, the introduction by the history, modern art, the image of popular culture Third, the imitation by the works of photograph, etc The appropriation in the modern costumes could be distinguished as two facts : First, the appropriation of the image of popular culture, 1) the way by the induction of popular factors of the extremely routine, commonplace character 2) the way by the citation of critical sentence of society, complaint message of the situation of times. Second, the reinterpretation of the past works : 1) the way by the reinterpretation of a great artist′s works, or popular works. In accordance with its change of a standard of value of the beauty, the products of modern culture, called the artificial second image, that is, popular factor, ready made factor, a signboard, a trademark etc, was appropriated in modern costumes and was reinterpreted by the works of fashion designer′s empathy. We can say that the modern costumes is not only the products of creative, original action of fashion designers but also the mirror of times, having relationship with society.

• Journal of Information Management
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• v.40 no.4
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• pp.49-71
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• 2009

As the importance and proportion of electronic records increases in the public sector, the necessity for variable types of records management has strengthened. Elements of records management metadata standards, which were provided in 2007 by the National Archives of Korea, focused mainly on text-centered records management standards. Therefore an extension of elements which can represent diverse types of electronic records is needed. In this study, metadata elements focusing on image records are suggested. For this, the characteristics of image records are investigated and the Australian government recordkeeping metadata standard and the PREMIS data dictionary, which have been recently modified, are analyzed. Through this, four elements, format, significant properties, environment, and coverage are suggested to fortify the current records management standard.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.425-433
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• 2018

In this paper, we propose a super-resolution reconstruction algorithm for plenoptic images based on the reliability of disparity. The subperture image generated by the Flanoptic camera image is used for disparity estimation and reconstruction of super-resolution image based on TV_L1 algorithm. In particular, the proposed image reconstruction method is effective in the boundary region where disparity may be relatively inaccurate. The determination of reliability of disparity vector is based on the upper, lower, left and right positional relationship of the sub-aperture image. In our method, the unreliable vectors are excluded in reconstruction. The performance of the proposed method was evaluated by comparing to a bicubic interpolation method, a conventional disparity based method and dictionary based method. The experimental results show that the proposed method provides the best performance in terms of PSNR(Peak Signal to noise ratio), SSIM(Structural Similarity).

• Journal of Fashion Business
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• v.13 no.1
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• pp.91-101
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• 2009

The purpose of this study is to develop semantic differential scales which are necessary to evaluate visual image and effect of flare skirt. As a result of the first survey of 362 female college students, the most effective factors for shape of flare skirt are silhouette, volume of flare, and skirt length. Based on this result, we made flare skirt simulation for visual evaluation with using I-Designer program. 4 kinds of volume of flare($90^{\circ},\;180^{\circ},\;270^{\circ},\;360^{\circ}$) and 3 kinds of skirt length(48cm, 58cm, 68cm) are combined as variables for flare skirt of visual evaluation. For the second survey of 362 female college students, we asked to write suggested adjectives freely. As a result, we could draw out 210 adjectives. 'Feminine' was most frequently used word for flare skirt, and then, 'vivid', 'rhythmic', 'cute', 'soft', 'fat', and 'comfortable' in this order. With considering frequently used words in the preceding study, we selected 41 adjectives. Antonyms were selected from the resulted frequency of this study and preceding study, and the rest of words were found from dictionary. From these process, we developed semantic differential scales for visual image and effect of flare skirt.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1877-1885
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• 2015

This paper presents a new Nearest-Neighbors based weighted representation for images and weighted K-Nearest-Neighbors (WKNN) classifier to improve the precision of image classification using the Bag of Visual Words (BOVW) based models. Scale-invariant feature transform (SIFT) features are firstly extracted from images. Then, the K-means++ algorithm is adopted in place of the conventional K-means algorithm to generate a more effective visual dictionary. Furthermore, the histogram of visual words becomes more expressive by utilizing the proposed weighted vector quantization (WVQ). Finally, WKNN classifier is applied to enhance the properties of the classification task between images in which similar levels of background noise are present. Average precision and absolute change degree are calculated to assess the classification performance and the stability of K-means++ algorithm, respectively. Experimental results on three diverse datasets: Caltech-101, Caltech-256 and PASCAL VOC 2011 show that the proposed WVQ method and WKNN method further improve the performance of classification.

• The Journal of the Acoustical Society of Korea
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• v.15 no.6
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• pp.90-98
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• 1996

In this paper we have proposed the sign language transformation system for deaf based on a morpheme analysis. The proposed system extracts phoneme components and connection informations of the input character sequence by using a morpheme analysis. And then the sign image obtained by component analysis is correctly and automatically generated through the sign image database. For the effective sign language transformation, the language description dictionary which consists of a morpheme analysis part for analysis of input character sequence and sign language description part for reference of sign language pattern is costructed. To avoid the duplicating sign language pattern, the pattern is classified a basic, a compound and a similar sign word. The computer simulation shows the usefulness of the proposed system.

• Current Optics and Photonics
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• v.4 no.3
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• pp.210-220
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• 2020

Aiming at the problem that the Local Sparse Difference Index algorithm has low accuracy and low efficiency when detecting target anomalies in a hyperspectral image, this paper proposes a Weighted Collaborative Representation and Sparse Difference-Based Hyperspectral Anomaly Detection algorithm, to improve detection accuracy for a hyperspectral image. First, the band subspace is divided according to the band correlation coefficient, which avoids the situation in which there are multiple solutions of the sparse coefficient vector caused by too many bands. Then, the appropriate double-window model is selected, and the background dictionary constructed and weighted according to Euclidean distance, which reduces the influence of mixing anomalous components of the background on the solution of the sparse coefficient vector. Finally, the sparse coefficient vector is solved by the collaborative representation method, and the sparse difference index is calculated to complete the anomaly detection. To prove the effectiveness, the proposed algorithm is compared with the RX, LRX, and LSD algorithms in simulating and analyzing two AVIRIS hyperspectral images. The results show that the proposed algorithm has higher accuracy and a lower false-alarm rate, and yields better results.

• Journal of IKEEE
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• v.25 no.1
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• pp.83-87
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• 2021

In harsh environments such as fog or fine dust, the cameras' detection ability for object recognition may significantly decrease. In order to accurately obtain important information even in bad weather, fog removal algorithms are necessarily required. Research has been conducted in various ways, such as computer vision/data-based fog removal technology. In those techniques, estimating the amount of fog through the input image's depth information is an important procedure. In this paper, a linear model is presented under the assumption that the image dark channel dictionary, saturation ∗ value, and sharpness characteristics are linearly related to depth information. The proposed method of haze removal through a linear model shows the superiority of algorithm performance in quantitative numerical evaluation.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.8
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• pp.81-88
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• 2017

This paper proposes a method of detecting rain regions from a single image. More specifically, a way of training the deep convolutional neural network based on the collected rain and non-rain patches is presented in a supervised manner. It is also shown that the proposed rain detection method based on deep convolutional neural network can provide better performance than the conventional rain detection method based on dictionary learning. Moreover, it is confirmed that the application of the proposed rain detection for rain removal can lead to some improvement in detail representation on the low-frequency regions of the rain-removed images. Additionally, this paper introduces the rain transfer method that inserts rain patterns into original images, thereby producing rain effects on the resulting images. The proposed rain transfer method could be used to augment rain patterns while constructing rain database.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.727-730
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• 2016

TSC means classifying time series data based on pattern. Time series data is quite common data type and it has high potential in many fields, so data mining and machine learning have paid attention for long time. In traditional approach, distance and dictionary based methods are quite popular. but due to time scale and random noise problems, it has clear limitation. In this paper, we propose a novel approach to deal with these problems with CNN and data mutation. CNN is regarded as proven neural network model in image recognition, and could be applied to time series pattern recognition by extracting pattern. Data mutation is a way to generate mutated data with different methods to make CNN more robust and solid. The proposed method shows better performance than traditional approach.