• 패션비즈니스
• /
• 제18권1호
• /
• pp.37-49
• /
• 2014

By investigating structural relationships between personal image and self-efficacy, this experimental study purposes to suggest a direction and the meaning of effective education on personal image. Based on scholars' studies on personal image and self-efficacy, this study extracts a revised questionnaire on personal image. The experimental study proved the relationship between the variables of personal image and self-efficacy by using personal image questionnaires which are extracted from the literature study. For this purpose, we have conducted a questionnaire survey including 234 students from women's university in Seoul. The results of this study are as follows. First, for cognitions on personal image, which are components of the internal image, both the visual image and social image impacting on self-efficacy have a significant efficacy in the self-regulation factor. Second, the satisfaction rates of the components for personal image impacting all the factors of self-efficacy showed a significant effect. Third, the significant results are being obtained from the analysis of differences in self-efficacy according to the levels of satisfaction rates on internal image and social image, which are expected to have effects on the self-efficacy between the groups for all factors. However, according to the analysis of differences in self-efficacy in relation to the levels of satisfaction for visual images, only the self-confidence factor in the self-efficacy is different between the groups.

• 한국컴퓨터정보학회논문지
• /
• 제23권4호
• /
• pp.147-153
• /
• 2018

In this paper, we propose an efficient WBC 14-Diff classification which performs using the WBC-ResNet-152, a type of CNN model. The main point of view is to use Super-pixel for the segmentation of the image of WBC, and to use ResNet for the classification of WBC. A total of 136,164 blood image samples (224x224) were grouped for image segmentation, training, training verification, and final test performance analysis. Image segmentation using super-pixels have different number of images for each classes, so weighted average was applied and therefore image segmentation error was low at 7.23%. Using the training data-set for training 50 times, and using soft-max classifier, TPR average of 80.3% for the training set of 8,827 images was achieved. Based on this, using verification data-set of 21,437 images, 14-Diff classification TPR average of normal WBCs were at 93.4% and TPR average of abnormal WBCs were at 83.3%. The result and methodology of this research demonstrates the usefulness of artificial intelligence technology in the blood cell image classification field. WBC-ResNet-152 based morphology approach is shown to be meaningful and worthwhile method. And based on stored medical data, in-depth diagnosis and early detection of curable diseases is expected to improve the quality of treatment.

• 한국정보처리학회논문지
• /
• 제7권11호
• /
• pp.3592-3602
• /
• 2000

영상 처리는 크게 영상에 내재된 특성값을 얻어내는 영상분석과, 동일한 성질의 영상을 분류하는 영상분류의 두단계로 이루어진다. 본 논문에서는 텍스쳐에 내재된 일반적인 속성인 거침과 부드러움의 특성 추출을 통해서 영상에 포함된 다양한 텍스쳐를 자동적으로 인식하고 분류하는 방법을 제안한다. 특성추출은 텍스쳐 영상이 지닌 그레이 레벨의 공간적인 의존성을 이용한 통계적 분석에 기반한 것으로 모멘트와 동차성의 조합을 통해서 일반적인 텍스쳐의 속성을 검출하기 때문에 텍스쳐의 구조형태에 크게 영향을 받지 않는 이점을 가지고 있다. 거친 텍스쳐일수록 강하게 반응하는 모멘트와 부드러운 텍스쳐일수록 강하게 반응하는 동차성의 차를 이용하기 때문에 보다 뚜렷한 텍스쳐 분할이 가능하다. 제안한 시스템의 성능 평가를 위해서 다양한 텍스쳐 영상에 제안한 방법을 적용하고, 성공적인 결과를 보인다.

• 한국컴퓨터정보학회논문지
• /
• 제27권1호
• /
• pp.77-82
• /
• 2022

본 연구는 장소 이미지를 수집하고 학습하여 사용자가 관심이 있어 하는 이미지의 장소를 예측하여 알려주는 서비스 개발을 목적으로 한다. 이미지 학습을 위한 이미지 데이터들은 크롤링 부분을 통해 수집되도록 설계되었다. 이미지 수집 이후 수집된 이미지들은 장소별로 라벨링 되어 CNN의 다양한 층을 통하여 학습된다. 각 층을 거칠 때마다 입력받은 학습 데이터는 최적화하여 특징 맵과의 비교를 반복하며 특정 장소 이미지의 특징 정보를 뽑아낸다. 충분한 학습 데이터가 쌓이면 다양한 장소 이미지들에 대해 예측이 가능하다. 학습 결과 모델의 정확도는 79.2로 높은 학습 정확도를 보였다.

• 한국방송∙미디어공학회:학술대회논문집
• /
• 한국방송공학회 2009년도 IWAIT
• /
• pp.396-399
• /
• 2009

In this paper, we present a method to define a color similarity between color images using Octree-based quantization and similar color integration. The proposed method defines major colors from each image using Octree-based quantization. Two color palettes to consist of major colors are compared based on Euclidean distance and similar color bins between palettes are matched. Multiple matched color bins are integrated and major colors are adjusted. Color histogram based on the color palette is constructed for each image and the difference between two histograms is computed by the weighted Euclidean distance between the matched color bins in consideration of the frequency of each bin. As an experiment to validate the usefulness, we discriminated the same clothing from CCD camera images based on the proposed color similarity analysis. We retrieved the same clothing images with the success rate of 88 % using only color analysis without texture analysis.

• 로봇학회논문지
• /
• 제6권1호
• /
• pp.18-26
• /
• 2011

This paper presents a method, Exposure Controlled Temporal Filtering (ECF), applied to visual motion tracking, that can cancel the temporal aliasing of periodic vibrations of cameras and fluctuations in illumination through the control of exposure time. We first present a theoretical analysis of the exposure induced image time integration process and how it samples sensor impingent light that is periodically fluctuating. Based on this analysis we develop a simple method to cancel high frequency vibrations that are temporally aliased onto sampled image sequences and thus to subsequent motion tracking measurements. Simulations and experiments using the 'Center of Gravity' and Normalized Cross-Correlation motion tracking methods were performed on a microscopic motion tracking system to validate the analytical predictions.

• 한국멀티미디어학회논문지
• /
• 제19권9호
• /
• pp.1738-1746
• /
• 2016

This study is on moving images among the various media created by the screen-based image media in the multimedia age. Moving images shown in the image media are reinterpreted in various forms and are necessary to have a conceptual definition as expression media to meet the changes. Therefore, the study identified the conceptual definition of moving images from the perspective of objects, and subdivided them into the visual cognitive area to satisfy the recent characteristics of visual media. It also suggested the guidelines required for analyzing the image media based on the results of the case analysis of 8 different works.

• 한국방송∙미디어공학회:학술대회논문집
• /
• 한국방송∙미디어공학회 2022년도 추계학술대회
• /
• pp.112-115
• /
• 2022

The process of deep learning usually needs to deal with massive data which has greatly limited the development of deep learning technologies today. Convolutional Neural Network (CNN) structure is often used to solve image classification problems. However, a large number of images may be required in order to train an image in CNN, which is a heavy burden for existing computer systems to handle. If the image data can be compressed under the premise that the computer hardware system remains unchanged, it is possible to train more datasets in deep learning. However, image compression usually adopts the form of lossy compression, which will lose part of the image information. If the lost information is key information, it may affect learning performance. In this paper, we will analyze the effect of image compression on deep learning performance on CNN-based cat and dog classification. Through the experiment results, we conclude that the compression of images does not have a significant impact on the accuracy of deep learning.

• 유통과학연구
• /
• 제16권8호
• /
• pp.51-61
• /
• 2018

Purpose - To active the Korea kimchi distribution, this study aims to investigate the effect of Korea national image and kimchi image on kimchi purchase intention. this study suggest the strategies for kimchi export to China by examining how the Chinese perceptions of Korean kimchi. Research design, data, and methodology - For this study, empirical analysis was conducted based on survey results. A questionnaire was distributed to a total of 400 Chinese consumers. Of these, 280 were collected and 278 were used for statistical processing, excluding 2 that were found to be unsuitable for analysis. This study was performed by the regression analysis using the spss24 statistical program. Results - As a results, It was not significant that the Chinese consumers' familiarity on the Korea image will have a positive effect on their kimchi purchase intention. On the other hand, the kimchi/Korean food image of Chinese consumers' will increase their kimchi consumption experience. Conclusions - The Chinese consumers' positive image on kimchi/Korean food in terms of the unique characteristics of kimchi, health aspects and preference of kimchi is positively influenced when they have higher image on Korea related to its national characteristics, and that of the higher image for Korea has a positive effect on kimchi purchase intention.

• Journal of Information Processing Systems
• /
• 제13권2호
• /
• pp.370-384
• /
• 2017

The image segmentation is the most important operation in an image processing system. It is located at the joint between the processing and analysis of the images. Unsupervised segmentation aims to automatically separate the image into natural clusters. However, because of its complexity several methods have been proposed, specifically methods of optimization. In our work we are interested to the technique SFLA (Shuffled Frog-Leaping Algorithm). It's a memetic meta-heuristic algorithm that is based on frog populations in nature searching for food. This paper proposes a new approach of unsupervised image segmentation based on SFLA method. It is implemented and applied to different types of images. To validate the performances of our approach, we performed experiments which were compared to the method of K-means.