• The Korean Journal of Food And Nutrition
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• v.32 no.3
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• pp.202-207
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• 2019

The objective of this study was to analyze labeling improvements and evaluate willingness to pay for health functional foods with a focus on the caution for intake. For this study, we conducted a survey on health functional food intake behavior, confirmation and improvements of cautions for intake. We assessed the willingness to pay for improvement of the caution for intake. Consumers anticipate improved immune function, and fatigue improvement after consumption of health functional foods. They mainly checked the function components related to efficacy and effectiveness, ingredients and their contents, ingestion amount and method, expiry date and best mode of storage, product name, and cautions associated with ingestion of health functional foods. They has been difficulties in obtaining sufficient caution information for intake from the current labeling method. Therefore, it is necessary to improve the labeling of caution for intake. The analysis indicated that about 5.14% of the respondents were willing to pay more if new labeling was introduces. However, there is still controversy over their safety, which is damaging to the consumers. Therefore, by providing consumers with accurate and detailed information on cautions for intake, it can contribute to securing safety and improving the quality of health functional foods.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.545-550
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• 2022

In Deep Learning method, it is well known that it requires large amount of data to train the deep neural network. And it also requires the labeling of each data to fully train the neural network, which means that experts should spend lots of time to provide the labeling. To alleviate the problem of time-consuming labeling process, some methods have been suggested such as weak-supervised method, one-shot learning, self-supervised, suggestive learning, and so on. In this manuscript, those methods are analyzed and its possible future direction of the research is suggested.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.23-29
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• 2009

Blob detection is an essential ingredient process in some computer applications such as intelligent visual surveillance. However, previous blob detection algorithms are still computationally heavy so that supporting real-time multi-channel intelligent visual surveillance in a workstation or even one-channel real-time visual surveillance in a embedded system using them turns out prohibitively difficult. In this paper, we propose a fast and precise blob detection algorithm for visual surveillance. Blob detection in visual surveillance goes through several processing steps: foreground mask extraction, foreground mask correction, and connected component labeling. Foreground mask correction necessary for a precise detection is usually accomplished using morphological operations like opening and closing. Morphological operations are computationally expensive and moreover, they are difficult to run in parallel with connected component labeling routine since they need much different processing from what connected component labeling does. In this paper, we first develop a fast and precise foreground mask correction method utilizing on neighbor pixel checking which is also employed in connected component labeling so that the developed foreground mask correction method can be incorporated into connected component labeling routine. Through experiments, it is verified that our proposed blob detection algorithm based on the foreground mask correction method developed in this paper shows better processing speed and more precise blob detection.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.3
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• pp.16-24
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• 2019

Recently, research on emotion recognition based on EEG has attracted great interest from human-robot interaction field. In this paper, we propose a method of labeling using image-based EEG topography instead of evaluating emotions through self-assessment and annotation labeling methods used in MAHNOB HCI. The proposed method evaluates the emotion by machine learning model that learned EEG signal transformed into topographical image. In the experiments using MAHNOB-HCI database, we compared the performance of training EEG topography labeling models of SVM and kNN. The accuracy of the proposed method was 54.2% in SVM and 57.7% in kNN.

• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.661-665
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• 2011

This paper presents the method for the extraction of the lungs part from the other parts for the diagnostic of the lungs part. The proposed method is based on the calculation of the connected component and the centroid of the image. Connected Component labeling is used to label the each objects in the binarized image. After the labeling is done, centroid value is calculated for each object. The filing operation is applied which helps to extract the lungs part from the image retaining all the parts of the original lungs image. The whole process is explained in the following steps and experimental results shows it's significant.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.267-270
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• 2010

In filtering of urban lidar data, low outliers or opening underground areas may cause errors that some ground points are labelled as non-ground objects. To solve such a problem, this paper proposes an automated method which consists of RANSAC algorithm, one-dimensional labeling, and morphology filter. All processes are conducted along the lidar scan line profile for efficient computation. Lidar data over Dajeon, Korea is used and the final results are evaluated visually. It is shown that the proposed method is quite promising in urban dem generation.

• Journal of information and communication convergence engineering
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• v.15 no.1
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• pp.49-52
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• 2017

Ganglion cysts are benign soft tissues usually encountered in the wrist. In this paper, we propose a method to extract a ganglion cyst region from ultrasonography images by using image segmentation. The proposed method using the possibilistic c-means (PCM) clustering method is applicable to ganglion cyst extraction. The methods considered in this thesis are fuzzy stretching, median filter, PCM clustering, and connected component labeling. Fuzzy stretching performs well on ultrasonography images and improves the original image. Median filter reduces the speckle noise without decreasing the image sharpness. PCM clustering is used for categorizing pixels into the given cluster centers. Connected component labeling is used for labeling the objects in an image and extracting the cyst region. Further, PCM clustering is more robust in the case of noisy data, and the proposed method can extract a ganglion cyst area with an accuracy of 80% (16 out of 20 images).

• Current Optics and Photonics
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• v.3 no.3
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• pp.220-226
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• 2019

A novel speckle-shifting ghost imaging (SSGI) technique is proposed in this paper. This method can effectively extract the edge of an unknown object without achieving its clear ghost image beforehand. However, owing to the imaging mechanism of SSGI, the imaging result generally contains serious noise. To solve the problem, we further propose a simple and effective method to remove noise from the speckle-shifting ghost image with a connected-region labeling (CRL) algorithm. In this method, two ghost images of an object are first generated according to SSGI. A threshold and the CRL are then used to remove noise from the imaging results in turn. This method can retrieve a high-quality image of an object with fewer measurements. Numerical simulations are carried out to verify the feasibility and effectiveness.

• International Journal of Oral Biology
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• v.35 no.3
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• pp.107-111
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• 2010

Treponema denticola is a gram-negative anaerobe that can cause periodontal disease. The adhesion of this bacterium to host tissues is considered to be the primary event in the colonization and infection of a host. Fibrinogen is generally found in damaged tissues resulting from periodontitis. The binding ability of T. denticola to fibrinogen may therefore be an important virulence factor in inducing periodontal diseases. It has been reported recently that oral spirochetes can be labeled with fluorescent fatty acids and we speculated that this labeling method could be used in an oral spirochete binding assay. The binding of several different strains of T. denticola to immobilized human fibrinogen was therefore tested using the fluorescent fatty acid labeling method. In the case of immobilized fibrinogen, the T. denticola ATCC 35405 strain showed saturable binding to immobilized fibrinogen. Indeed, all four different T. denticola strains tested in this experiment, T. denticola ATCC 35405, T. denticola ATCC 33520, T. denticola ATCC 35404 and T. denticola OTK showed binding to fibrinogen. The fluorescent fatty acid labeling method thus shows utility in binding assays for T. denticola, different strains of which can generally bind to immobilized fibrinogen.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.10
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• pp.421-426
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• 2013

As a greater amount of traffic have been generated on the Internet, it becomes more important to know the size of each flow. Many research studies have been conducted on the traffic measurement, and mostly they have focused on how to increase the measurement accuracy with a limited amount of memory. In this paper, we propose an explicit flow labeling technique that can be used to find out the names of the top flows and to increase the counting upper bound of the existing scheme. The labeling technique is applied to CSM (Counter Sharing Method), the most recent traffic measurement algorithm, and the performance is evaluated using the CAIDA dataset.