• The Journal of the Korea Contents Association
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• v.18 no.9
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• pp.482-497
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• 2018

This study would investigate the impacts of coffee shop companies' mecenat activity on identification, corporate image, love mark and loyalty. For sampling, an investigator who visited the coffee shops in person conducted surveys with customers. 800 copies were distributed for 11 days from May 23 through June 2, 2018, and excluding unreliable questionnaires from the collected questionnaires, 711 copies were used in the final analysis. As a result of the analysis, it turned out that the higher social contribution, purity, public interest and preference, the higher identification became. Also, the higher social contribution, public interest and preference, the higher corporate image became. On the other hand, it turned out that purity had a negative (-) impact on the corporate image. It turned out that purity and preference had positive impacts on the love mark, while did not affect social contribution, while public interest had a negative (-) impact on that. It turned out that identification had a positive impact on the corporate image, and identification and corporate image had positive impacts on the love mark. Also, identification, corporate image and love mark had positive impacts on loyalty. It is expected that the above research result would provide practical implications for coffee shop companies' marketing techniques in the future, and further, it is judged that it would play a positive role in the quality of life of consumers who experience coffee shop companies' mecenat activity.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1624-1631
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• 2021

With the continued development of image processing technology, we live in a time when it is difficult to visually discriminate processed (or tampered) images from real images. However, as the risk of fake images being misused for crime increases, the importance of image forensic science for identifying fake images is emerging. Currently, various deep learning-based identifiers have been studied, but there are still many problems to be used in real situations. Due to the inherent characteristics of deep learning that strongly relies on given training data, it is very vulnerable to evaluating data that has never been viewed. Therefore, we try to find a way to improve generalization ability of deep learning-based fake image identifiers. First, images with various contents were added to the training dataset to resolve the over-fitting problem that the identifier can only classify real and fake images with specific contents but fails for those with other contents. Next, color spaces other than RGB were exploited. That is, fake image identification was attempted on color spaces not considered when creating fake images, such as HSV and YCbCr. Finally, dropout, which is commonly used for generalization of neural networks, was used. Through experimental results, it has been confirmed that the color space conversion to HSV is the best solution and its combination with the approach of increasing the training dataset significantly can greatly improve the accuracy and generalization ability of deep learning-based identifiers in identifying fake images that have never been seen before.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.5
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• pp.390-395
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• 2000

We present a fingerprint identification algorithm using the wavelet transform and correlation. The wavelet transform is used because of its simple operation to extract fingerprint minutiaes features for fingerprint classification. We perform the rowwise 1-D wavelet transform for a $256\times256$ fingerprint image to get a $1\times256$ column vector using the Haar wavelet and repeat 1-D wavelet transform for a 1$\times$256 column vector to get a $1\times4$ feature vector. Using PNN(Probabilistic Neural Network), we select the possible candidates from the stored feature vectors for fingerprint images. For those candidates, we compute the correlation between the input binary image and the target binary image to find the most similar fingerprint image. The proposed algorithm may be the key to a low cost fingerprint identification system that can be operated on a small computer because it does not need a large memory size and much computation.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.247-249
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• 2007

Crown parameters are important roles in tree species identification, because the canopy is the aggregate of all the crowns. However, crown measurements with spaceborne image data have remained more difficult than on aerial photographs since trees show more structural detail at higher resolutions. This recognized problem led to the initiation of the research to determine if high resolution satellite image data could be used to identify and classify single tree species. In this paper, shape parameters derived from pixel-based crown area measurements and texture features derived from GLCM parameters in QuickBird image were tested and compared for individual tree species identification. As expected, initial studies have shown that the crown parameters and the canopy texture parameters provided a differentiating method between coniferous trees and broad-leaved trees within the compartment(less than forest stand) for single extraction from spaceborne high resolution image.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.19-25
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• 2024

This paper proposes an artificial intelligence-based personal information area object detection optimization method in an embedded system to de-identify personal information in video data. As an object detection optimization method, first, in order to increase the detection rate for personal information areas when detecting objects, a gyro sensor is used to collect the shooting angle of the image data when acquiring the image, and the image data is converted into a horizontal image through the collected shooting angle. Based on this, each learning model was created according to changes in the size of the image resolution of the learning data and changes in the learning method of the learning engine, and the effectiveness of the optimal learning model was selected and evaluated through an experimental method. As a de-identification method, a shuffling-based masking method was used, and double-key-based encryption of the masking information was used to prevent restoration by others. In order to reuse the original image, the original image could be restored through a security key. Through this, we were able to secure security for high personal information areas and improve usability through original image restoration. The research results of this paper are expected to contribute to industrial use of data without personal information leakage and to reducing the cost of personal information protection in industrial fields using video through de-identification of personal information areas included in video data.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.10
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• pp.1021-1028
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• 2009

In this paper, a location identification for a cleaning robot using a camera shooting forward a room ceiling which kas three point landmarks is introduced. These three points are made from a laser source which is placed on an auto charger. A fisheye lens covering almost 150 degrees is utilized and the image is transformed to a camera image grabber. The widly shot image has an inevitable distortion even if wide range is coverd. This distortion is flatten using an image warping scheme. Several vision processing techniques such as an intersection extraction erosion, and curve fitting are employed. Next, three point marks are identified and their correspondence is investigated. Through this image processing and image distortion adjustment, a robot location in a wide geometrical coverage is identified.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.163-177
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• 2024

The fingerprint image quality relies on the clearness of separated ridges by valleys and the uniformity of the separation. The condition of skin still dominate the overall quality of the fingerprint. However, the identification performance of such system is very sensitive to the quality of the captured fingerprint image. Fingerprint image quality analysis and enhancement are useful in improving the performance of fingerprint identification systems. A fuzzy technique is introduced in this paper for both fingerprint image quality analysis and enhancement. First, the quality analysis is performed by extracting four features from a fingerprint image which are the local clarity score (LCS), global clarity score (GCS), ridge_valley thickness ratio (RVTR), and the Global Contrast Factor (GCF). A fuzzy logic technique that uses Mamdani fuzzy rule model is designed. The fuzzy inference system is able to analyse and determinate the fingerprint image type (oily, dry or neutral) based on the extracted feature values and the fuzzy inference rules. The percentages of the test fuzzy inference system for each type is as follow: For dry fingerprint the percentage is 81.33, for oily the percentage is 54.75, and for neutral the percentage is 68.48. Secondly, a fuzzy morphology is applied to enhance the dry and oily fingerprint images. The fuzzy morphology method improves the quality of a fingerprint image, thus improving the performance of the fingerprint identification system significantly. All experimental work which was done for both quality analysis and image enhancement was done using the DB_ITS_2009 database which is a private database collected by the department of electrical engineering, institute of technology Sepuluh Nopember Surabaya, Indonesia. The performance evaluation was done using the Feature Similarity index (FSIM). Where the FSIM is an image quality assessment (IQA) metric, which uses computational models to measure the image quality consistently with subjective evaluations. The new proposed system outperformed the classical system by 900% for the dry fingerprint images and 14% for the oily fingerprint images.

• Korean Journal of Food Science and Technology
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• v.30 no.3
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• pp.473-479
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• 1998

Identification of rice species was attempted by three side (top, side and front) images of brown rice. Nine parameters of each image were area, aspect ratio, maximum diameter, minimum diameter, perimeter, roundness and red (R), green (G) and blue (B) pixel values of an image. Forty rice samples consisted of 19 species used for the study and total 27 image characteristics for a kernel were measured. For calibration and confirmation, 105 and 20 brown rice kernels per each sample were used respectively. For best identification of rice species, 24 image characteristics were selected for discriminant analysis. Average percentages for correct identification of rice species were 84.75% and 84.93% for calibration and confirmation data set, respectively. The highest and lowest percentage for correct identification were 99.05% for Nongan and 50.63% for Hwaseung respectively in calibration data. The confirmation data showed that the correct identification of Nongan or Paalgong was 100%, while that of Hwaseung was 47.62%. The result of the study showed that three side (top, side and front) image of brown rice was not suitable for identification of rice species suggesting that additional techniques are required for better discrimination of rice species.

• The Journal of the Korea Contents Association
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• v.15 no.8
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• pp.258-270
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• 2015

The study aims at verifying the effect of the image, trust and responsiveness to social welfare organizations on continuing the sponsorship of private donors, the mediating effect of the organizational identification and the moderating effect of their financial status. As a result of causal analysis of the variables by structural equation models, regardless of donors' financial status, social welfare organizations' image, trust and responsiveness has no direct effect on continuing the sponsorship. Among the donors in relatively good financial status, social welfare organizations' image, trust and responsiveness positively have direct effects on organizational identification, which has a direct effect on continuing the sponsorship. That is, the image, trust and responsiveness influence the continuity of the sponsorship through the full mediating effect of organizational identification. On the other hand, among relatively poor donors, only the organizations' image and trust positively influence the organizational identification, The direct and indirect effects on the continuity of the sponsorship are different according to the private donors' financial status, which means that the moderating effect of the donors' financial status is proved. The result of latent mean analysis regarding 5 main latent variables according to the private donors' financial status, shows the significant difference in the continuity of sponsorship only(Mean= .197, Cohen's D=.779). By emphasizing that Organizational identification is a critical factor in terms of enhancing the continuity of sponsorship, some practical implications are discussed based on the study's findings.

• Journal of Korea Multimedia Society
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• v.19 no.10
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• pp.1782-1791
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• 2016

Various color spaces such as RGB, HSV, log-chromaticity have been used in the field of person re-identification. However, not enough studies have been done to find suitable color space for the re-identification. This paper reviews color invariance of color spaces by diagonal model and explores the suitability of each color space in the application of person re-identification. It also proposes a method for person re-identification based on a histogram refinement technique and some fusion strategies of color spaces. Two public datasets (ALOI and ImageLab) were used for the suitability test on color space and the ImageLab dataset was used for evaluating the feasibility of the proposed method for person re-identification. Experimental results show that RGB and HSV are more suitable for the re-identification problem than other color spaces such as normalized RGB and log-chromaticity. The cumulative recognition rates up to the third rank under RGB and HSV were 79.3% and 83.6% respectively. Furthermore, the fusion strategy using max score showed performance improvement of 16% or more. These results show that the proposed method is more effective than some other methods that use single color space in person re-identification.