• 한국콘텐츠학회논문지
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• 제18권9호
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• pp.482-497
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• 2018

본 연구는 커피전문점 기업의 메세나 활동이 동일시, 기업이미지, 러브마크, 충성도에 미치는 영향을 살펴보고자 하였다. 표본추출을 위하여 커피전문점에 직접 방문한 조사원이 고객들에게 설문조사를 실시하였고, 2018년 5월 23일부터 6월 2일까지 11일간 800부를 배포하여 회수된 설문지중 불성실한 설문지를 제외한 711부를 최종분석에 사용하였다. 분석결과, 사회공헌, 순수성, 공익성, 호감도가 높아질수록 동일시가 높아지는 것으로 나타났고, 사회공헌, 공익성, 호감도가 높아질수록 기업이미지도 높아지는 것으로 나타났다. 반면, 순수성은 기업이미지에 부(-)의 영향을 미치는 것으로 나타났다. 순수성과 호감도는 러브마크에 긍정적 영향을 미치는 것으로 나타났지만, 사회공헌은 영향을 미치지 않았고, 공익성은 부(-)의 영향을 미치는 것으로 나타났다. 동일시는 기업이미지에 긍정적 영향을 미치는 것으로 나타났고, 동일시와 기업이미지는 러브마크에 긍정적 영향을 미치는 것으로 나타났으며, 동일시, 기업이미지, 러브마크는 충성도에 긍정적 영향을 미치는 것으로 나타났다. 이러한 연구결과는 향후 커피전문점 기업의 마케팅 기법에 실무적 시사점을 제공할 것으로 기대되며, 나아가 커피전문점 기업의 메세나 활동을 경험하는 소비자들의 삶의 질에 긍정적 역할을 하게 될 것으로 사료된다.

• 한국멀티미디어학회논문지
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• 제24권12호
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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.

• 제어로봇시스템학회논문지
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• 제6권5호
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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.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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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.

• 대한임베디드공학회논문지
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• 제19권1호
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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.

• 제어로봇시스템학회논문지
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• 제15권10호
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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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• 제24권1호
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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.

• 한국식품과학회지
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• 제30권3호
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• pp.473-479
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• 1998

국산 19품종의 현미(40점)를 대상으로 현미 1알로부터 3개의 화상(윗면, 측면, 앞면)의 9개 화상특성(면적, 장단축비, 장축길이, 단축길이, 둘레길이, 원형도, 적색도, 녹색도, 청색도), 도합 27개 화상특성을 측정하여 품종 판별을 시도하였다. 판별식의 도출과 검증을 위하여 현미 시료 1점당 각각 105알과 20알이 사용되었다. 현미의 화상특성 중 윗면 화상, 측면 화상에서의 면적, 장단축비, 장축길이, 단축길이, 둘레, 원형도, 앞면 화상에서의 면적, 둘레는 품종 간 차이가 큰 특성이었다. 27개 화상 특성중 판별에 기여율이 가장 낮은 앞면 화상의 둘레길이, 측면 화상의 원형도, 윗면의 둘레길이를 제외한 24개 화상특성을 사용하였을 때 84.75%로 가장 판별율이 높았다. 품종 판별율은 품종 농안의 경우 최고 99.05%, 화성의 경우 최저 50.63%로 품종 간 차이가 많았다. 품종 판별식을 검증한 결과 평균 84.93%의 품종 판별율을 보였으며, 농안과 팔공의 경우 최고 100%, 화성의 경우 최저 47.62%의 품종 판별율을 보였다. 이 결과, 단지 화상분석에 의한 품종 판별율은 품종에 따라 커다란 차이가 있어 전체 품종 판별에는 적합하지 않은 것으로 사료되었다. 좀더 신뢰성 있는 품종 판별을 위해서는 화상분석 이외에 품종이 지닌 고유의 특성을 분석하는 다른 판별분석법도 병행하여 사용되어야 할 것이다.

• 한국콘텐츠학회논문지
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• 제15권8호
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• pp.258-270
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• 2015

본 연구는 사회복지조직에 대한 이미지, 신뢰성, 반응성이 개인후원자들의 후원지속성에 미치는 영향과, 조직동일시의 매개효과, 그리고 경제형편 수준의 조절효과를 실증적으로 검증하였다. 구조방정식 모형에 기초한 주요 변수들의 인과관계 분석결과, 개인 후원자들의 경제수준과 상관없이 사회복지조직에 대한 이미지, 신뢰성, 반응성은 후원지속성에 직접적으로 영향을 미치지 않은 것으로 밝혀졌다. 경제형편이 좋은 집단의 경우, 조직 이미지, 신뢰성, 반응성은, 조직동일시에 각각 유의한 정적 직접효과를 미쳤고, 조직동일시는 후원지속성에 정적 직접효과를 미쳤다. 조직동일시를 완전매개로 하여, 조직이미지, 신뢰성, 반응성 등이 각각 후원지속성에 유의한 영향을 미칠 수 있음이 입증되었다. 반면, 경제형편이 좋지 않은 경우는, 조직이미지와 신뢰성만 조직동일시에 유의한 영향을 미치는 것으로 밝혀졌다. 개인 후원자들의 경제형편 수준에 따라 후원지속성에 영향을 미치는 요인들의 직 간접효과가 상이한 양상을 보인다는 사실이 확인되어, 경제형편의 조절효과가 실증적으로 입증되었다. 잠재평균 분석결과, 경제형편 수준에 따른 주요변인들의 차이는 후원지속성에서만 유의한 차이를 보였다(평균값, .197, Cohen's D=.779). 개인 기부자들의 후원기관에 대한 동일시가 후원지속 가능성을 높일 수 있는 결정적으로 중요한 요인임을 강조하면서, 주요 발견점을 토대로 실천적 함의를 논의하였다.

• 한국멀티미디어학회논문지
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• 제19권10호
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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.