• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1309-1320
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• 2023

The technological development and popularization of mobile devices have developed so that users can check their location anywhere and use the Internet. However, in the case of indoors, the Internet can be used smoothly, but the global positioning system (GPS) function is difficult to use. There is an increasing need to provide real-time location information in shaded areas where GPS is not received, such as department stores, museums, conference halls, schools, and tunnels, which are indoor public places. Accordingly, research on the recent indoor positioning technology based on light detection and ranging (LiDAR) equipment is increasing to build a landmark database. Focusing on the accessibility of building a landmark database, this study attempted to develop a technique for estimating the user's location by using a single image taken of a landmark based on a mobile device and the landmark database information constructed in advance. First, a landmark database was constructed. In order to estimate the user's location only with the mobile image photographing the landmark, it is essential to detect the landmark from the mobile image, and to acquire the ground coordinates of the points with fixed characteristics from the detected landmark. In the second step, by applying the bag of words (BoW) image search technology, the landmark photographed by the mobile image among the landmark database was searched up to a similar 4th place. In the third step, one of the four candidate landmarks searched through the scale invariant feature transform (SIFT) feature point extraction technique and Homography random sample consensus(RANSAC) was selected, and at this time, filtering was performed once more based on the number of matching points through threshold setting. In the fourth step, the landmark image was projected onto the mobile image through the Homography matrix between the corresponding landmark and the mobile image to detect the area of the landmark and the corner. Finally, the user's location was estimated through the location estimation technique. As a result of analyzing the performance of the technology, the landmark search performance was measured to be about 86%. As a result of comparing the location estimation result with the user's actual ground coordinate, it was confirmed that it had a horizontal location accuracy of about 0.56 m, and it was confirmed that the user's location could be estimated with a mobile image by constructing a landmark database without separate expensive equipment.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.89-94
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• 2024

Indian subcontinent is a birthplace of multilingual people where documents such as job application form, passport, number plate identification, and so forth is composed of text contents written in different languages/scripts. These scripts may be in the form of different indic numerals in a single document page. Due to this reason, building a generic recognizer that is capable of recognizing handwritten indic digits written by diverse writers is needed. Also, a lot of work has been done for various non-Indic numerals particularly, in case of Roman, but, in case of Indic digits, the research is limited. Moreover, most of the research focuses with only on MNIST datasets or with only single datasets, either because of time restraints or because the model is tailored to a specific task. In this work, a hybrid model is proposed to recognize all available indic handwritten digit images using the existing benchmark datasets. The proposed method bridges the automatically learnt features of Capsule Network with hand crafted Bag of Feature (BoF) extraction method. Along the way, we analyze (1) the successes (2) explore whether this method will perform well on more difficult conditions i.e. noise, color, affine transformations, intra-class variation, natural scenes. Experimental results show that the hybrid method gives better accuracy in comparison with Capsule Network.

• Journal of information and communication convergence engineering
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• v.13 no.3
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• pp.205-214
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• 2015

In this paper, we propose a new approach to learning a discriminative model for figure/ground segmentation by incorporating the bag-of-features and conditional random field (CRF) techniques. We advocate the use of image patches instead of superpixels as the basic processing unit. The latter has a homogeneous appearance and adheres to object boundaries, while an image patch often contains more discriminative information (e.g., local image structure) to distinguish its categories. We use pixel-level sparse coding to represent an image patch. With the proposed feature representation, the unary classifier achieves a considerable binary segmentation performance. Further, we integrate unary and pairwise potentials into the CRF model to refine the segmentation results. The pairwise potentials include color and texture potentials with neighborhood interactions, and an edge potential. High segmentation accuracy is demonstrated on three benchmark datasets: the Weizmann horse dataset, the VOC2006 cow dataset, and the MSRC multiclass dataset. Extensive experiments show that the proposed approach performs favorably against the state-of-the-art approaches.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.52-67
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• 2020

Recently, the Arctic has been exposed to snow-covered land due to melting permafrost every year, and the Korea Geographic Information Institute(NGII) provides polar spatial information service by establishing spatial information of the polar region. However, there is a lack of spatial information on vegetation sensitive to climate change. This research used a multi-temporal Sentinel-2 image to perform land cover classification of the Ny-Ålesund in Arctic Svalbard. In the pre-processing step, 10 bands and 6 vegetation spectral index were generated from multi-temporal Sentinel-2 images. In image-classification step is consisted of extracting the vegetation area through 8-class land cover classification and performing the vegetation species classification. The image classification algorithm used Random Forest to evaluate the accuracy and calculate feature importance through Out-Of-Bag(OOB). To identify the advantages of multi- temporary Sentinel-2 for vegetation classification, the overall accuracy was compared according to the number of images stacked and vegetation spectral index. Overall accuracy was 77% when using single-time Sentinel-2 images, but improved to 81% when using multi-time Sentinel-2 images. In addition, the overall accuracy improved to about 83% in learning when the vegetation index was used additionally. The most important spectral variables to distinguish between vegetation classes are located in the Red, Green, and short wave infrared-1(SWIR1). This research can be used as a basic study that optimizes input characteristics in performing the classification of vegetation in the polar regions.

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.315-324
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• 2011

In Web environment, a flood of spam causes serious social problems such as personal information leak, monetary loss from fishing and distribution of harmful contents. Moreover, types and techniques of spam distribution which must be controlled are varying as days go by. The learning based spam classification method using Bag-of-Words model is the most widely used method until now. However, this method is vulnerable to anti-spam avoidance techniques, which recent spams commonly have, because it classifies spam documents utilizing only keyword occurrence information from classification model training process. In this paper, we propose a spam document detection method using a characteristic of repeating words occurring in spam documents as a solution of anti-spam avoidance techniques. Recently, most spam documents have a trend of repeating key phrases that are designed to spread, and this trend can be used as a measure in classifying spam documents. In this paper, we define six variables, which represent a characteristic of word repetition, and use those variables as a feature set for constructing a classification model. The effectiveness of proposed method is evaluated by an experiment with blog posts and E-mail data. The result of experiment shows that the proposed method outperforms other approaches.

• KIISE Transactions on Computing Practices
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• v.22 no.11
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• pp.619-624
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• 2016

Social-aware video displays not only the relationships between characters but also diverse information on topics such as economics, politics and culture as a story unfolds. Particularly, the speaking habits and behavioral patterns of people in different situations are very important for the analysis of social relationships. However, when dealing with this dynamic multi-modal data, it is difficult for a computer to analyze the drama data effectively. To solve this problem, previous studies employed the deep concept hierarchy (DCH) model to automatically construct and analyze social networks in a TV drama. Nevertheless, since location knowledge was not included, they can only analyze the social network as a whole in stories. In this research, we include location knowledge and analyze the social relations in different locations. We adopt data from approximately 4400 minutes of a TV drama Friends as our dataset. We process face recognition on the characters by using a convolutional- recursive neural networks model and utilize a bag of features model to classify scenes. Then, in different scenes, we establish the social network between the characters by using a deep concept hierarchy model and analyze the change in the social network while the stories unfold.

• Food Science and Preservation
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• v.5 no.4
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• pp.355-362
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• 1998

Polyethylene films contained 0, 10, 20, 30 and 40% of raw-ore powder(PERO) were prepared. The characteristics feature of the film and the powder were investigated in order to use packaging material for kimchi quality. Kimchi was packaged in the PERO bass md stored at 10$^{\circ}C$. The kimchi was examined for a pH, acidity, number of total microbe and lactic acid bacteria, E. coli, color values and sensory evaluation. The ore powder at 20$^{\circ}C$ produced infrared rays at 800-1100nm. The growth of E. coli md Staphylococcus aureus was extremely inhibited in the EMB and nutrient broth containing 10% of raw-ore powder but, that of lactobacillus plantarum and Leuconostoc mesenteriodes was slightly promoted in MRS broth containing 1%. The ripening by pH and acidity was slightly accelerated in kimchi in PERO bag(PERO-kimchi) compared to control kimchi but the maintenance of ripened-kimchi taste was prolonged in PERO-kimchi. The number of lactic acid bacteria of PERO-kimchi was more numerous than that of contol sample but that of E. coli wag exremely legs. The color L* values of PERO-kimchi was lower than control but a* and b* values were higher. Sensory evaluation of PERO-kimchi was higher score than control sample in crispness and overall taste about 10 to 20% of raw-ore contents for kimchi-packaging material was desirable.

• Pediatric Infection and Vaccine
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• v.8 no.2
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• pp.191-198
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• 2001

Purpose : The purpose of this study was to investigate clinical features of hospitalized infants 28~90 days of age with fever without source and to analyze those of young febrile infants using risk criteria for serious bacterial infection. Methods : The clinical features of 131 infants 28~90 days of age admitted to the Ulsan Dong-Kang General Hospital Pediatric Department because of fever(temperature ${\geq}38^{\circ}C$ rectally) without source, from January 2000 to December 2000, were investigated by retrospective chart review. The clinical features of 131 febrile infants were analyzed using Rochester criteria. Results : Among 131 cases, there were 60 cases(45.8%) of urinary tract infection, 33 cases (25.2%) of aseptic meningitis, 2 cases(1.5%) of bacteremia and 36 cases(27.5%) of no specific diagnosis. Among 131 cases, there were 57 cases(43.5%) in low risk group and 74 cases(56.5%) in not low risk one by Rochester criteria. A significant difference in the incidence of urinary tract infection, aseptic meningitis and no specific diagnosis was not found between both groups. Male to female ratio was 1.8 : 1. Sex ratio between both groups was not significantly different. Most febrile infant were noted in spring(35.1%) and the summer(36.7%). The peak incidence of aseptic meningitis was noted in May and June. The fever subsided mostly within 48~72 hours after administering antimicrobial agents(61.8~83.2%). A significant difference in duration of fever after administering antimicrobial agents was not found between both groups. Conclusion : A selected group of low risk infants 28~90 days of age with fever without source can be managed as outpatients provided that a thorough initial evaluation is performed, that parents can reliably monitor their infant closely at home and that careful follow up can be assured. Because bag collected specimens were more likely to yield indeterminate urine culture result, a suprapubic or catheter obtained urine specimen for culture is a necessary part of the evaluation of all febrile infants 28~90 days of age. The further prospective study on evaluation and management of young febrile infant should be performed in our hospital.