• Journal of the Korean Society of Costume
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• v.33
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• pp.27-39
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• 1997

The purpose of this study was to classify the attributes of brand image criteria of women's shoes to compose the perceptual map of the brand by factor analysis and to examine the differences in brand preferences and purchase methods of shoes according to demographic variables. 10 brand names were selected for the study Samples were 271 women in Seoul Korea :143 were college students and 128 were career women.The data were analyzed using factor analy-sis multiple regression analysis one-way ANOVA Duncan's multiple range test x2-test t-test. The results of the study were the -followings: 1. Four segments of brand image attributes of women's shoes derived by factor analysis: F. 1. 'utility' F.2'appearance' ; F. 3 'sales promotion' ; F.4 'financial factor'. 2. As the result of draw up the perceptual map 'landrover' was high in utility but low in appearance 'Misope' and 'Mook' was low in utility but high in appearance. 'Fashion Leader' was in the nearest ideal direction to the utility and appearance. 3. The preference level of the shoes brand name was in order of the 'Fashion Leader'. 'Mook' and 'Soda' But consumers possessed 'Landrover' the most 4. There were significant differences among preference level of ' Landrover' and 'Misope' according to the social class. There were sig-nificant differences among possession level of 'Misope' and 'Soda' according to the social class 5. the middle and lower class consumers used an exchange ticket during the bargain sales more than upper class when they pur-chase shoes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3823-3840
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• 2020

Recently, auto-encoder has emerged as the most popular method in convolutional neural network (CNN) based image compression and has achieved impressive performance. In the traditional auto-encoder based image compression model, the encoder simply sends the features of last layer to the decoder, which cannot allocate bits over different spatial regions in an efficient way. Besides, these methods do not fully exploit the contextual information under different receptive fields for better reconstruction performance. In this paper, to solve these issues, a novel auto-encoder model is designed for image compression, which can effectively transmit the hierarchical features of the encoder to the decoder. Specifically, we first propose an adaptive bit-allocation strategy, which can adaptively select an importance channel. Then, we conduct the multiply operation on the generated importance mask and the features of the last layer in our proposed encoder to achieve efficient bit allocation. Moreover, we present an additional novel perceptual loss function for more accurate image details. Extensive experiments demonstrated that the proposed model can achieve significant superiority compared with JPEG and JPEG2000 both in both subjective and objective quality. Besides, our model shows better performance than the state-of-the-art convolutional neural network (CNN)-based image compression methods in terms of PSNR.

• Journal of the Korean Society of Clothing and Textiles
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• v.19 no.6
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• pp.1040-1050
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• 1995

The Purposes of this study were to investigate the choice dimensions in purchasing the medium-low priced casual clothing, the influence of them on the preference of medium-low priced casual clothing, and the brand images of six medium-low priced casual clothing using the perceptual map. The Questionnaires were administered to 540 college students living in Seoul (340) and County of Chungnam(200). The data were analyzed by frequency, factor analysis and multiple regression analysis. The results were summarized as follows: 1) The choice dimensions in purchasing the medium-low price casual clothing were identified as exclusiveness/style, intrinsic characteristics, promotion and price/distance. 2) Exclusiveness/style dimension influenced most on the preference of medium-low priced casual, intrinsic characteristics, price/distance dimension were followed. Promotion dimension appeared to have an insignificant influence. These results were consistent in both Seoul and the County of Chungnam. 3) Perceptual mapping showed Hunt and J-vim had the best brand images, Maypole and Omphalos were followed. Tipi Cosi and I-land appeared to have the worst brand image. The college students living in the County of Chungnam perceived that all six brands of medium low priced casual clothing to be exclusive in their style. In addition, it was perceived less promoted, more expensive and farther than Seoul counterparts.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.60
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• pp.47-55
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• 2000

Two experiments were conducted to examine the relationships between the color structure and the user preference of a CNS (Car Navigation System) digital map in terms of HSE (Human Sensibility Ergonomics). In the first experiment, the user's preference of color structures were investigated from the subjects' self-designed digital maps using a CNS digital map UIMS (User Interface Management System): in the second, statistical relation models between the user's color structure satisfaction level and the color components of CIE (Commission Internationale de ι'Eclairage) of the real products were suggested. For each experiment, CIE L*u*v* and CIE LCH color space were adapted, respectively, because they have their own characteristics of perceptual uniformity which enables the color components to transform a linear function.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.5 no.1
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• pp.37-42
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• 2006

A reversible audio watermarking algorithm is presented in this paper. This algorithm transforms the audio signal with the integer wavelet transform first in order to enhance the correlation between neighbor audio samples. Audio signal has low correlation between neighbor samples, which makes it difficult to apply difference expansion scheme. Second, a novel difference expansion scheme is used to embed more data by reducing the size of location map. Therefore, the difference expansion scheme used in this paper theoretically secures high embedding capacity under low perceptual distortion. Experiments show that this scheme can hide large number of information bits and keeps high perceptual quality.

• Journal of Electrical Engineering and Technology
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• v.3 no.3
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• pp.436-443
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• 2008

This paper presents a new method for building detection and reconstruction from aerial images. In our approach, we extract useful building location information from the generated disparity map to segment the interested objects and consequently reduce unnecessary line segments extracted in the low level feature extraction step. Hypothesis selection is carried out by using an undirected graph, in which close cycles represent complete rooftops hypotheses. We test the proposed method with the synthetic images generated from Avenches dataset of Ascona aerial images. The experiment result shows that the extracted 3D line segments of the reconstructed buildings have an average error of 1.69m and our method can be efficiently used for the task of building detection and reconstruction from aerial images.

• Journal of IKEEE
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• v.12 no.1
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• pp.51-58
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• 2008

This paper presents a new method for building detection and reconstruction from aerial images. In our approach, we extract the useful building location information from the generated disparity map to obtain the segmentation of interested objects and thus reduce significantly unnecessary line segment extracted in low level feature extraction step. Hypothesis selection is carried out by using undirected graph in which close cycles represent complete rooftops hypotheses, and hypothesis are finally tested to contruct building model. We test the proposed method with synthetic images generated from Avenches dataset of Ascona aerial images. The experiment result shows that the extracted 3D line segments of the buildings can be efficiently used for the task of building detection and reconstruction from aerial images.

• The Journal of the Acoustical Society of Korea
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• v.27 no.5
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• pp.256-261
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• 2008

In this paper, we propose a novel approach to improve the performance of minima controlled recursive averaging (MCRA) which is based on the conditional maximum a posteriori criterion. A crucial component of a practical speech enhancement system is the estimation of the noise power spectrum. One state-of-the-art approach is the minima controlled recursive averaging (MCRA) technique. The noise estimate in the MCRA technique is obtained by averaging past spectral power values based on a smoothing parameter that is adjusted by the signal presence probability in frequency subbands. We improve the MCRA using the speech presence probability which is the a posteriori probability conditioned on both the current observation the speech presence or absence of the previous frame. With the performance criteria of the ITU-T P.862 perceptual evaluation of speech quality (PESQ) and subjective evaluation of speech quality, we show that the proposed algorithm yields better results compared to the conventional MCRA-based scheme.

• ETRI Journal
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• v.41 no.6
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• pp.797-810
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• 2019

Image saliency detection is the basis of perceptual image processing, which is significant to subsequent image processing methods. Most saliency detection methods can detect only a single object with a high-contrast background, but they have no effect on the extraction of a salient object from images with complex low-contrast backgrounds. With the prior knowledge, this paper proposes a method for detecting salient objects by combining the boundary contrast map and the geodesics-like maps. This method can highlight the foreground uniformly and extract the salient objects efficiently in images with low-contrast backgrounds. The classical receiver operating characteristics (ROC) curve, which compares the salient map with the ground truth map, does not reflect the human perception. An ROC curve with distance (distance receiver operating characteristic, DROC) is proposed in this paper, which takes the ROC curve closer to the human subjective perception. Experiments on three benchmark datasets and three low-contrast image datasets, with four evaluation methods including DROC, show that on comparing the eight state-of-the-art approaches, the proposed approach performs well.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.544-551
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• 2023

Speech enhancement used to improve the perceptual quality and intelligibility of noise speech has been studied as a method using a complex-valued spectrum that can improve both magnitude and phase in a method using a magnitude spectrum. In this paper, a study was conducted on how to apply attention mechanism to complex-valued spectrum-based speech enhancement systems to further improve the intelligibility and quality of noise speech. The attention is performed based on additive attention and allows the attention weight to be calculated in consideration of the complex-valued spectrum. In addition, the global average pooling was used to consider the importance of the feature map. Complex-valued spectrum-based speech enhancement was performed based on the Deep Complex U-Net (DCUNET) model, and additive attention was conducted based on the proposed method in the Attention U-Net model. The results of the experiments on noise speech in a living room environment showed that the proposed method is improved performance over the baseline model according to evaluation metrics such as Source to Distortion Ratio (SDR), Perceptual Evaluation of Speech Quality (PESQ), and Short Time Object Intelligence (STOI), and consistently improved performance across various background noise environments and low Signal-to-Noise Ratio (SNR) conditions. Through this, the proposed speech enhancement system demonstrated its effectiveness in improving the intelligibility and quality of noisy speech.