• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.943-946
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• 1998

In this paper, we applied FFT to PCB Images, cutting unnecessary singals and noise, moving the starting point to center of image and used rotaion transform. from the detected edge Hough Transform identify the length, but not the angle, so we matched PCB images with using rotation transform to identify length and angle. After rotation transform we employ Least Squared Method to exact stereo matching.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.812-819
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• 1996

This study was performed to quantify microscopically morphological characteristics of cotton fiber to identify parameters for quality evaluation using image analysis . The image of each fiber was captured by a Pc-based color imaging system using a conventional microscope. Ends of individual cotton fibers were glued on a microscope slide without any tension or straightening. A modified watershed technique was implemented to identify individual convolution segments, which were defined as sections of the fiber bordered by two neighboring convolutions. Length, area and perimeter of each convolution segment were measured directly from the image . Average width, shape factor and number of convolution segments in mm were calculated from the measured parameters. The performance of the image analysis algorithm was compared with visual varieties of cotton . The image analysis results agreed with visual inspection in 89.6% of the tested images.

• Journal of the Korean Society of Clothing and Textiles
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• v.25 no.5
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• pp.833-844
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• 2001

The purposes of this study were 1) to identify the effects of store image and stores entertainment environment image on attitude toward store and store patronage intention through emotions toward store, and 2) to identify the effects of sensation-seeking tendency, hedonic shopping orientation and shopping motives on store image, stores entertainment environment image, attitude toward store and store patronage intention. The data were collected from 416 female adolescents who visited stores located in Dongdaemoon, Seoul, via self-administered questionnaires, and were analyzed by frequency, factor analysis, multiple regression and path analysis. The results of this study were as follows : (1) According to path analysis, store image and stores entertainment environment image affected emotions toward store and attitude toward store, and affected store patronage intention through a mediator, emotions toward store. (2) Among the factors related to store images, stores entertainment environment image had the greatest effect on store patronage intention. (3) Sensation-seeking tendency, hudonic shopping orientation and shopping motives affected store image, stores entertainment environment image, emotions toward store and attitude toward store directly and indirectly through store image and stores entertainment environment image. (4) The most significant factor in explaining all these relations was hedonic shopping orientation.

• Journal of Biosystems Engineering
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• v.24 no.1
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• pp.51-58
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• 1999

A color image processing algorithm was developed to assist the robotic plug-seedling transplanter. The algorithm was designed to identify and locate empty cells in the seedling tray. The image of pepper seedling tray was segmented into regions of plant, frame and soil using thresholding technique which utilized HSI or RGB color characteristics of each region. The detection algorithm was able to successfully identify empty cells and locate their two-dimensional location. The overall success rate of the algorithm was about 88%.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.2
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• pp.359-367
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• 2016

As taking pictures by using smartphones has become more common in society, there are many incidents which are unexpected manipulation of images and leak of confidential information. Because of those incidents, demands that identify forgery/alteration of image file and proves of the original copy is constantly increasing. In general, smartphone saves image file as JPEG form and it has DQT which determines a compression rate of image in a header part of image. There is also DQT in Thumbnail image which inside of JPEG. In previous research, it identified a smartphone which take image by only using DQT, However, the research has low accuracy to identify the devices. There are two main purposes in this research. First, this research will analogize a smartphone and an application that takes a picture, edits and save an image file by testing not only about a DQT information but also a information of Thumbnail image. Second, the research will build a database of DQT and Thumbnail information in JPEG file to find more accurate image file's origin.

• Journal of Korea Trade
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• v.25 no.2
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• pp.178-195
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• 2021

Purpose - This study aims to analyze consumer in the multidimensional aspect of a combination of corporate image and brand personality in order to identify the structural causal relationship between consumer choice and corporate image and brand personality. Design/methodology - This study combined theoretical literature studies with empirical field studies using questionnaire survey methods. To achieve this objective, a hypothetical causal model consisting of three potential variables and nine measurement variables was created based on prior research, and a structural equation model was used to identify the suitability of the model. Findings - The hypothetical model established by this study was judged to be generally appropriate. In particular, corporate image was shown to have significant static direct effects on consumer choice and brand personality. It was also shown that brand personality had a direct static effect on consumer choice, and that corporate image has an indirect significant impact on consumer choice by moderating brand personality. Originality/value - Previous papers have mainly focused on one-dimensional studies of various images, such as companies and brands. However, this paper used a model that analyzed consumer choice through multi-clue information rather than corporate images as the only clue to consumer choice.

• Journal of Fashion Business
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• v.12 no.1
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• pp.129-146
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• 2008

The purpose of this study is to identify components of CEO image and to examine predictors to affect company's market value. To explore the social construction of the CEO Image depicted in the popular business newspaper, the Wall Street Journal and daily newspaper of Korea, was analyzed. Then, the reconstructed image of the CEO was compared with the firm's stock price change to see their relationship, if any. This paper focused on the case of Carly Fiorina as previous chief of Hewlett-Packard, who was the Fortune's ranking of the 50 most powerful women in business is presented. The period for the analysis was five years and eight months from her inauguration(July, 1999) to the release(February, 2005). The results, four predictors such as nature, management ability, leadership style, appearance character had statistically significant relationship with both company's market value and the image of CEO. In addition to revealed that media coverage of Carly Filoina was commensurate with the financial performance, particularly stock price change of the Hewlett-Packard. In general, the best image of the CEO is highly transcends to the image of the company as well. Therefore it is need to manage effectively components of CEO image to enhance brand image and its brand value, which are further expected to enhance company's market value.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.33-38
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• 2020

Anomaly detection of Machine Learning such as PCA anomaly detection and CNN image classification has been focused on cross-sectional data. In this paper, two approaches has been suggested to apply ML techniques for identifying the failure time of big time series data. PCA anomaly detection to identify time rows as normal or abnormal was suggested by converting subjects identification problem to time domain. CNN image classification was suggested to identify the failure time by re-structuring of time series data, which computed the correlation matrix of one minute data and converted to tiff image format. Also, LASSO, one of feature selection methods, was applied to select the most affecting variables which could identify the failure status. For the empirical study, time series data was collected in seconds from a power generator of 214 components for 25 minutes including 20 minutes before the failure time. The failure time was predicted and detected 9 minutes 17 seconds before the failure time by PCA anomaly detection, but was not detected by the combination of LASSO and PCA because the target variable was binary variable which was assigned on the base of the failure time. CNN image classification with the train data of 10 normal status image and 5 failure status images detected just one minute before.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.3
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• pp.140-151
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• 2020

It is important to identify the location of ship hull blocks with exact block identification number when scheduling the shipbuilding process. The wrong information on the location and identification number of some hull block can cause low productivity by spending time to find where the exact hull block is. In order to solve this problem, it is necessary to equip the system to track the location of the blocks and to identify the identification numbers of the blocks automatically. There were a lot of researches of location tracking system for the hull blocks on the stockyard. However there has been no research to identify the hull blocks on the stockyard. This study compares the performance of 5 Convolutional Neural Network (CNN) models with multi-view image set on the classification of the hull blocks to identify the blocks on the stockyard. The CNN models are open algorithms of ImageNet Large-Scale Visual Recognition Competition (ILSVRC). Four scaled hull block models are used to acquire the images of ship hull blocks. Learning and transfer learning of the CNN models with original training data and augmented data of the original training data were done. 20 tests and predictions in consideration of five CNN models and four cases of training conditions are performed. In order to compare the classification performance of the CNN models, accuracy and average F1-Score from confusion matrix are adopted as the performance measures. As a result of the comparison, Resnet-152v2 model shows the highest accuracy and average F1-Score with full block prediction image set and with cropped block prediction image set.

• Proceedings of the Korean Institute of Interior Design Conference
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• 2006.05a
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• pp.135-139
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• 2006

Today, brands are transforming into a strategic structure which emphasize brand image more so that the product itself, and the brand identify that factors in personal preference and taste assume important positions. In particular, space where brand identify is factored in is increasingly in importance these days, which means that it is no longer about selling products themselves, but selling brand image. Accordingly, it is necessary to approach in a strategic manner when it comes to the space that factor in brand value in terms of selling brand image. Accordingly, this research examines the relationship between brand identity and space components through the space in coffee shops where brand identity is applied, to identify how indoor expression trend is utilized.