• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.511-521
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• 2011

As the use of image input and output devices increases, the importance of extracting text area in color images is also increasing. In this paper, in order to extract text area of the images efficiently, we present a text area extraction method for color images based on labeling and gradient difference method. The proposed method first eliminates non-text area using the processes of labeling and filtering. After generating the candidates of text area by using the property that is high gradient difference in text area, text area is extracted using the post-processing of noise removal and text area merging. The benefits of the proposed method are its simplicity and high accuracy that is better than the conventional methods. Experimental results show that precision, recall and inverse ratio of non-text extraction (IRNTE) of the proposed method are 99.59%, 98.65% and 82.30%, respectively.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2006.08a
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• pp.36-36
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• 2006

• Journal of the Korea Society of Computer and Information
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• v.13 no.6
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• pp.243-251
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• 2008

This study is to suggest the extraction algorithms of fingerprint corepoint and region separation using the labeling for gate security in order that it might be applied to the fingerprint recognition effectively. The gate security technology is entrance control, attendance management, computer security, electronic commerce authentication, information protection and so on. This study is to extract the directional image by dividing the original image in $128{\times}128$ size into the size of $4{\times}4$ pixel. This study is to separate the region of directional smoothing image extracted by each directional by using the labeling, and extract the block that appeared more than three sorts of change in different directions to the corepoint. This researcher is to increase the recognition rate and matching rate by extracting the corepoint through the separation of region by direction using the maximum direction and labeling, not search the zone of feasibility of corepoint or candidate region of corepoint used in the existing method. According to the result of experimenting with 300 fingerprints, the poincare index method is 94.05%, the proposed method is 97.11%.

• Mass Spectrometry Letters
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• v.5 no.3
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• pp.63-69
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• 2014

In this study, we present a new isotope-coded carbamidomethylation (iCCM)-based quantitative proteomics, as a complementary strategy for conventional isotope labeling strategies, with providing the simplicity, ease of use, and robustness. In iCCM-based quantification, two proteome samples can be separately isotope-labeled by means of covalently reaction of all cysteinyl residues in proteins with iodoacetamide (IAA) and its isotope (IAA-$^{13}C_2$, $D_2$), denoted as CM and iCCM, respectively, leading to a mass shift of all cysteinyl residues to be + 4 Da. To evaluate iCCM-based isotope labeling in proteomic quantification, 6 protein standards (i.e., bovine serum albumin, serotransferrin, lysozyme, beta-lactoglobulin, beta-galactosidase, and alpha-lactalbumin) isotopically labeled with IAA and its isotope, mixed equally, and followed by proteolytic digestion. The resulting CM-/iCCM-labeled peptide mixtures were analyzed using a nLC-ESI-FT orbitrap-MS/MS. From our experimental results, we found that the efficiency of iCCM-based quantification is more superior to that of mTRAQ, as a conventional nonisobaric labeling method, in which both of a number of identified peptides from 6 protein standards and the less quantitative variations in the relative abundance ratios of heavy-/light-labeled corresponding peptide pairs. Finally, we applied the developed iCCM-based quantitative method to lung cancer serum proteome in order to evaluate the potential in biomarker discovery study.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.519-521
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• 2015

세라믹 소재 영상에서 결함 영역이 다른 영역보다 명암도가 밝게 나타나는 정보를 이용하여 ROI 영역을 추출한다. 추출된 ROI 영역에서 Blurring 기법을 적용하여 미세 잡음을 제거한다. 미세 잡음이 제거된 ROI 영역에서 Median Filter기법을 적용하여 임펄스 잡음을 제거한다. 임펄스 잡음이 제거된 영역에서 Prewit Mask을 적용하여 수평과 수직 에지를 검출하고 검출된 에지에 윤곽선 추적 기법을 적용하여 결함 영역의 경계를 보정한다. 보정된 영상에서 Blob Labeling 기법을 적용하여 최종적으로 결함 영역을 추출한다. 제안된 방법을 8mm와 10mm 세라믹 소재 영상을 대상으로 실험한 결과, 기존의 결함 검출 방법보다 제안된 검출 방법의 검출 성능이 개선된 것을 확인하였다.

• The KIPS Transactions:PartA
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• v.8A no.2
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• pp.147-156
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• 2001

A high performance routing switch is an essential device to either the high performance parallel processing or communication networks that handle multimedia transfer systems such as VOD. The high performance routing chip called STC104 is a typical example in the technical aspect which has 32 bidirectional links of 100Mbps transfer sped. It has exploited new technologies, such as wormhole routing, interval labeling, and adaptive routing method. The high speed router has been applied into some parallel processing system as a single chip. However, its performance over the various interconnection networks with multiple routing chips has not been studied. In this paper, the strucrtures and characteristics of the STC104 have been investigated in order to evaluate the high speed router. Various topology of the STC104, such as meshes, torus, and N-cube are defined and constructed. Algorithms of packet transmission have been proposed based on the interval labeling and the group adaptive routing method implemented in the interconnected network. Multicast algorithms, which are often requited to the processor networks and broadcasting systems, modified from U-mesh and U-torus algorithms have also been proposed overcoming the problems of point-to-point communication.

• Mass Spectrometry Letters
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• v.13 no.4
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• pp.139-145
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• 2022

Protein glycosylation is a common post-translational modification by non-template-based biosynthesis. In fungal biotechnology, which has great applications in pharmaceuticals and industries, the importance of research on fungal glycoproteins and glycans is accelerating. In particular, the importance of quantitative analysis of fungal glycans is emerging in research on the production of filamentous fungal proteins by genetic modification. Reliable mass spectrometry-based techniques for quantitative glycomics have evolved into chemical, enzymatic, and metabolic stable isotope labeling methods. In this study, we intend to expand quantitative glycomics by metabolic isotope labeling of glycans in Aspergillus niger, a filamentous fungus model, by the MILPIG method. We demonstrate that incubation of filamentous fungi in a culture medium with carbon-13 labeled glucose (1-¹³C₁) efficiently incorporates carbon-13 into N-linked glycans. In addition, for quantitative validation of this method, light and heavy glycans are mixed 1:1 to show the performance of quantitative analysis of various N-linked glycans simultaneously. We have successfully quantified fungal glycans by MILPIG and expect it to be widely applicable to glycan expression levels under various biological conditions in fungi.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.222-224
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• 2022

Deep learning-based computer vision applications in the field of object segmentation take a transfer learning method using hyperparameters and models pretrained and distributed by STOA techniques to improve performance. Custom datasets used in this process require a lot of resources, such as time and labeling, in labeling tasks to generate Ground Truth information. In this paper, we present an automatic labeling construction method using object segmentation techniques so that resources such as time and labeling can be used less to build custom datasets used in deep learning neural networks.

• The Journal of Information Technology and Database
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• v.8 no.1
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• pp.95-104
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• 2001

In the text classification domain, labeling the training documents is an expensive process because it requires human expertise and is a tedious, time-consuming task. Therefore, it is important to reduce the manual labeling of training documents while improving the text classifier. Selective sampling, a form of active learning, reduces the number of training documents that needs to be labeled by examining the unlabeled documents and selecting the most informative ones for manual labeling. We apply this methodology to Naive Bayes, a text classifier renowned as a successful method in text classification. One of the most important issues in selective sampling is to determine the criterion when selecting the training documents from the large pool of unlabeled documents. In this paper, we propose two measures that would determine this criterion : the Mean Absolute Deviation (MAD) and the entropy measure. The experimental results, using Renters 21578 corpus, show that this proposed learning method improves Naive Bayes text classifier more than the existing ones.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.625-635
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• 2005

In this paper, we propose a hybrid approach for segmenting the lungs efficiently and automatically in chest CT images. The proposed method consists of the following three steps. first, lungs and airways are extracted by two- and three-dimensional automatic seeded region growing and connected component labeling in low-resolution. Second, trachea and large airways are delineated from the lungs by two-dimensional morphological operations, and the left and right lungs are identified by connected component labeling in low-resolution. Third, smooth and accurate lung region borders are obtained by refinement based on image subtraction. In experiments, we evaluate our method in aspects of accuracy and efficiency using 10 chest CT images obtained from 5 patients. To evaluate the accuracy, we Present results comparing our automatic method to manually traced borders from radiologists. Experimental results show that proposed method which use connected component labeling in low-resolution reduce processing time by 31.4 seconds and maximum memory usage by 196.75 MB on average. Our method extracts lung surfaces efficiently and automatically without additional processing like hole-filling.