• Journal of the Korean Society of School Health
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• v.32 no.1
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• pp.50-58
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• 2019

Purpose: The purpose of this study was to examine the mediating effect of self-perception on the relationship between body image and depressive symptoms in middle school adolescents. Methods: This study performed a pathway analysis with a sample of 284 adolescents recruited from three middle schools. Self-reported questionnaires consisted of items regarding body image, self-perception, and depressive symptoms. Results: Body image, self-perception, and depressive symptoms showed a significant correlation. Self-perception was directly affected by body image and depressive symptoms were not affected by body image. The mediating effect of self-perception on body image's impact on depressive symptoms was confirmed. Self-perception had a significant indirect effect on depressive symptoms, which means that self-perception has a full mediating effect on body image's impact on depressive symptoms. Conclusion: The effect of body image on depressive symptoms was fully mediated by self-perception in early adolescents. It suggests that self-perception needs to be considered when providing nursing interventions for adolescents.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1425-1430
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• 2018

In this paper, we propose a CNN based deep learning algorithm for semantic segmentation of images. In order to improve the accuracy of semantic segmentation, we combined pixel level object classification and image level object classification. The image level object classification is used to accurately detect the characteristics of an image, and the pixel level object classification is used to indicate which object area is included in each pixel. The proposed network structure consists of three parts in total. A part for extracting the features of the image, a part for outputting the final result in the resolution size of the original image, and a part for performing the image level object classification. Loss functions exist for image level and pixel level classification, respectively. Image-level object classification uses KL-Divergence and pixel level object classification uses cross-entropy. In addition, it combines the layer of the resolution of the network extracting the features and the network of the resolution to secure the position information of the lost feature and the information of the boundary of the object due to the pooling operation.

• Journal of the Korean Society of Clothing and Textiles
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• v.33 no.4
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• pp.666-677
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• 2009

The purpose of this study was to analyze market opportunities for Korean image fashion products, focusing on adolescents. In other words, preference, purchase intention and gift intention of adolescents on Korean image fashion products were analyzed. For this study, four stimuli which reflect Korean image were chosen. We measured design evaluation and adolescent fashion innovativeness. The subjects for this study were 219 high school students. The data were analyzed by descriptive statistics, ANOVA, Duncan test, cross-tabulation, correlation analysis, multiple regression. The more innovative the subject is, the higher the clothing purchase frequency and the purchase price are. Also, high-innovative group showed that they like Korean image fashion products more than low one. Product attributes such as prettiness and newness significantly influenced purchase intention for their own use and for others as a gift. In conclusion, when related marketers and scholars provide Korean image products that target adolescents, they should try to make those products more sophisticated and modern.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.3
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• pp.528-533
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• 1997

In the analysis of fatigue crack propagation behavior, the crack length is one of the most important factors. In the test of crack propagation, compliance method is widely used to detect crack length. The measurement of surface crack length is not so easy with compliance method. In this study, the image processing technique was applied to measure the surface crack length. CCD(Charge-coupled device) camera was used to observe the crack image and the computer program to detect crack length from stored crack image was developed. CCT(Center Cracked Tension) specimen was used to compare the compliance method with the image processing technique. The crack length which detected by the image processing techniques was found to be well consistent with that from the optical measurement.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.33 no.1
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• pp.68-75
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• 1997

In this paper, we propose the improved initial image estimation method for a fast fractal image decoding. When the correlation between a domain and a range is given as the linear equation, the value of initial image estimation using the conventional method is the intersection between its linear equation and y=x. If the gradient of linear equation is large, that the difference of the value between each adjacent pixels is large, the conventional method has disadvantage which has the impossibility of exact estimation. The method of the proposed initial image estimation performs well by two steps. he first step can improve the disadvantage of the conventional method. The second step upgrades the range value which was found previous step by referring information of its domain. Though the computational complexity for the initial image estimation increses slightly, the total computational complexity decreases by 30% than that of the conventional method because of diminishing in the number of iterations.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.683-688
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• 2008

The emotional information processing is to simulate and recognize human sensibility, sensuality or emotion, to realize natural and harmonious human-machine interface. This paper proposes an emotion-based image retrieval method. In this method, user can choose a linguistic query among some emotional adjectives. Then the system shows some corresponding representative images that are pre-evaluated by experts. Again the user can select a representative one among the representative images to initiate traditional content-based image retrieval (CBIR). By this proposed method any CBIR can be easily expanded as emotion-based image retrieval. In CBIR of our system, we use several color and texture visual descriptors recommended by MPEG-7. We also propose a fuzzy similarity measure based on Choquet integral in the CBIR system. For the communication between system and user, a relevance feedback mechanism is used to represent human subjectivity in image retrieval. This can improve the performance of image retrieval, and also satisfy the user's individual preference.

• Journal of Korea Multimedia Society
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• v.15 no.7
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• pp.891-902
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• 2012

Reversible data hiding scheme is a form of steganography in which the secret embedding data can be retrieved from a stego image for the purpose of identification, copyright protection and making a covert channel. The reversible data hiding should satisfy that not only are the distortions due to artifacts against the cover image invisible but also it has large embedding capacity as far as possible. In this paper, we propose a robust reversible data hiding scheme by exploiting the differences between a center pixel and its neighboring pixels in each sub-block of the image to embed secret data into extra space. Moreover, our scheme enhances the embedding capacity and can recover the embedded data from the stego image without causing any perceptible distortions to the cover image. Simulation results show that our proposed scheme has lower visible distortions in the stego image and provides robustness to geometrical image manipulations, such as rotation and cropping operations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.985-988
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• 2007

This paper presents techniques to make image enhancement using unsharp masking. It is the technique to make image enhancement by automatically find the three parameters that makes hard to use the unsharp mask technique. To optimize the three parameters(Threshold, Amount, Radius), at first classify the pixels in the image to three groups, and then according to the groups, apply the unsharp mask to the image differently. We experimented and analyzed the rate of image enhancement by comparing images which is enhanced by human and which is enhanced by proposed technique.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.59-62
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• 2009

In general, as a dynamic range of digital still camera is narrower than a real scene‘s, it is hard to represent the shadow region of scene. Thus, multi-scaled retinex algorithm is used to improve detail and local contrast of the shadow region in an image by dividing the image by its local average images through Gaussian filtering. However, if the chromatic distribution of the original image is not uniform and dominated by a certain chromaticity, the chromaticity of the local average image depends on the dominant chromaticity of original image, thereby the colors of the resulting image are shifted to a complement color to the dominant chromaticity. In this paper, a modified multi-scaled retinex method to reduce the influence of the dominant chromaticity is proposed. In multi-scaled retinex process, the local average images obtained by Gaussian filtering are divided by the average chromaticity values of the original image in order to reduce the influence of dominant chromaticity. Next, the chromaticity of illuminant is estimated in highlight region and the local average images are corrected by the estimated chromaticity of illuminant. In experiment, results show that the proposed method improved the local contrast and detail without color distortion.

• Journal of Biosystems Engineering
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• v.25 no.5
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• pp.409-414
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• 2000

A machine vision system was built to obtain single stationary image from an egg. This system includes a CCD camera, an image processing board and a lighting system. A computer program was written to acquire, enhance and get histogram from an image. To minimize the evaluation time, the artificial neural network with the histogram of the image was used for eggshell evaluation. Various artificial neural networks with different parameters were trained and tested. The best network(64-50-1 and 128-10-1) showed an accuracy of 87.5% in evaluating eggshell. The comparison test for the elapsed processing time per an egg spent by this method(image processing and artificial neural network) and by the processing time per an egg spent by this method(image processing and artificial neural network) and by the previous method(image processing only) revealed that it was reduced to about a half(5.5s from 10.6s) in case of cracked eggs and was reduced to about one-fifth(5.5s from 21.1s) in case of normal eggs. This indicates that a fast eggshell evaluation system can be developed by using machine vision and artificial neural network.