• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.5
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• pp.67-77
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• 2007

The images of MODerate resolution Imaging Spectroradiometer (MODIS) that provide wider swath and shorter revisit frequency than Land Satellite (Landsat) and Satellite Pour I' Observation de la Terre (SPOT) has been used fer land cover classification with better spatial resolution than National Oceanic and Atmosphere Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR)'s images. Due to the advantages of MODIS, several researches have conducted, however the results for the land cover classification using MODIS images have less accuracy of classification in small areas because of low spatial resolution. In this study, uncertainty of paddy fields classification using MODIS images was conducted in the region of Gyeonggi-do and the relation between this uncertainty of estimating paddy fields and topographical factors was also explained. The accuracy of classified paddy fields was compared with the land cover map of Environmental Geographic Information System (EGIS) in 2001 classified using Landsat images. Uncertainty of paddy fields classification was analyzed about the elevation and slope from the 30m resolution Digital Elevation Model (DEM) provided in EGIS. As a result of paddy classification, user's accuracy was about 41.5% and producer's accuracy was 57.6%. About 59% extracted paddy fields represented over 50 uncertainty in one hundred scale and about 18% extracted paddy fields showed 100 uncertainty. It is considered that several land covers mixed in a MODIS pixel influenced on extracted results and most classified paddy fields were distributed through elevation I, II and slope A region.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.5 s.311
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• pp.38-47
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• 2006

Conventional classification method uses a single classifier based on shape or motion feature. However this method exhibits a weakness if naively used since the classification performance is highly sensitive to the accuracy of moving region to be detected. The detection accuracy, in turn, depends on the condition of the image background. In this paper, we propose to resolve the drawback and thus strengthen the classification reliability by employing a Bayesian decision fusion and by optimally combining the decisions of three classifiers. The first classifier is based on shape information obtained from Fourier descriptors while the second is based on the shape information obtained from image gradients. The third classifier uses motion information. Our experimental results on the classification Performance of human and vehicle with a static camera in various directions confirm a significant improvement and indicate the superiority of the proposed decision fusion method compared to the conventional Majority Voting and Weight Average Score approaches.

• Journal of the Korean association of regional geographers
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• v.16 no.4
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• pp.428-439
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• 2010

Map of geographical names can give us information for understanding of region because geographical name reflects regional perception of human. This study aimed to make an web-based map by constructing database of geographical names. Main contents carried out research on methods for classification of geographical names, database construction, and mapping on the website. Geographical name classified into four categories of the physical geography, culture and historical geography, economic geography, and the other and also, 18 sub-categories by classification criteria. Geographical name designed to input by collecting geographical names from paper-based maps and vernacular place names only known to the local region. Fields of database consisted of address, coordinates, geographical name(hangeul, hanja), classification, explanation, photographs. Map of geographical names can be represented with regional geographical information. The result of research is expected to offer information for distribution of geographical names as well as regional interpretation.

• Journal of KIBIM
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• v.11 no.3
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• pp.22-33
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• 2021

In order to maximize the use of BIM, all data related to individual elements in the model must be correctly assigned, and it is essential to check whether it corresponds to the IFC entity classification. However, as the BIM modeling process is performed by a large number of participants, it is difficult to achieve complete integrity. To solve this problem, studies on semantic integrity verification are being conducted to examine whether elements are correctly classified or IFC mapped in the BIM model by applying an artificial intelligence algorithm to the 2D image of each element. Existing studies had a limitation in that they could not correctly classify some elements even though the geometrical differences in the images were clear. This was found to be due to the fact that the geometrical characteristics were not properly reflected in the learning process because the range of the region to be learned in the image was not clearly defined. In this study, the CRF-RNN-based semantic segmentation was applied to increase the clarity of element region within each image, and then applied to the MVCNN algorithm to improve the classification performance. As a result of applying semantic segmentation in the MVCNN learning process to 889 data composed of a total of 8 BIM element types, the classification accuracy was found to be 0.92, which is improved by 0.06 compared to the conventional MVCNN.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.525-528
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• 1999

Abstract - In this paper, a segmentation method for brain Magnetic Resonance(MR) image using region clustering technique with statistical distribution of gradient image and fuzzy rules is described. The brain MRI consists of gray matter and white matter, cerebrospinal fluid. But due to noise, overlap, vagueness, and various parameters, segmentation of MR image is a very difficult task. We use gradient information rather than intensity directly from the MR images and find appropriate thresholds for region classification using gradient approximation, rayleigh distribution function, region clustering, and merging techniques. And then, we propose the adaptive fuzzy rules in order to extract anatomical structures and diseases from brain MR image data. The experimental results shows that the proposed segmentation algorithm given better performance than traditional segmentation techniques.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.911-914
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• 2011

Detecting Hand Region is essencial technology to providing User based interface and many research has been continue. In this paper will propose Hand Region Detection method by using HSV space based on Back Projection and Hand Shape Recognition using Hu Moment. By using Back Projection, I updated reliability on Hand Region Detection by Back Projection method and, Confirmed Hand Shape could be recognized through Hu moment.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1252-1255
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• 2002

In this paper, face recognition algorithm, by using skin color information of HSI color coordinate collected from face images, elliptical mask, fratures of face including eyes, nose and mouth, and geometrical feature vectors of face and facial angles, is proposed. The proposed algorithm improved face region extraction efficacy by using HSI information relatively similar to human's visual system along with color tone information about skin colors of face, elliptical mask and intensity information. Moreover, it improved face recognition efficacy with using feature information of eyes, nose and mouth, and Θ1(ACRED), Θ2(AMRED) and Θ 3(ANRED), which are geometrical face angles of face. In the proposed algorithm, it enables exact face reading by using color tone information, elliptical mask, brightness information and structural characteristic angle together, not like using only brightness information in existing algorithm. Moreover, it uses structural related value of characteristics and certain vectors together for the recognition method.

• Korean Journal of Remote Sensing
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• v.19 no.4
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• pp.329-339
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• 2003

In this study, an approach of image fusion in decision level has been proposed for unsupervised image classification using the images acquired from multiple sensors with different characteristics. The proposed method applies separately for each sensor the unsupervised image classification scheme based on spatial region growing segmentation, which makes use of hierarchical clustering, and computes iteratively the maximum likelihood estimates of fuzzy class vectors for the segmented regions by EM(expected maximization) algorithm. The fuzzy class vector is considered as an indicator vector whose elements represent the probabilities that the region belongs to the classes existed. Then, it combines the classification results of each sensor using the fuzzy class vectors. This approach does not require such a high precision in spatial coregistration between the images of different sensors as the image fusion scheme of pixel level does. In this study, the proposed method has been applied to multispectral SPOT and AIRSAR data observed over north-eastern area of Jeollabuk-do, and the experimental results show that it provides more correct information for the classification than the scheme using an augmented vector technique, which is the most conventional approach of image fusion in pixel level.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.793-798
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• 2000

As we review the mechanism regarding digital font generation and birth of TrueType font, we realizes that the process is composed of sequential steps such as contour fonts from glyph table. This fact implies that we propose classification of TrueType font in terms of segment width and the number of occurrence from the glyph data.

• Journal of information and communication convergence engineering
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• v.6 no.1
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• pp.94-99
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• 2008

Segmentation for the region of nucleus in the image of uterine cervical cytodiagnosis is known as the most difficult and important part in the automatic cervical cancer recognition system. In this paper, the region of nucleus is extracted from an image of uterine cervical cytodiagnosis using the HSI model. The characteristics of the nucleus are extracted from the analysis of morphemetric features, densitometric features, colormetric features, and textural features based on the detected region of nucleus area. The classification criterion of a nucleus is defined according to the standard categories of the Bethesda system. The fuzzy C-means clustering algorithm is employed to the extracted nucleus and the results show that the proposed method is efficient in nucleus recognition and uterine cervical Pap-Smears extraction.