• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.123-131
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• 2008

In Oriental medicine, the status of a tongue is the important indicator to diagnose the condition of one's health like the physiological and the clinicopathological changes of internal organs in a body. A tongue diagnosis is not only convenient but also non-invasive, and therefore widely used in Oriental medicine. However, the tongue diagnosis is affected by examination circumstances like a light source, patient's posture, and doctor's condition a lot. To develop an automatic tongue diagnosis system for an objective and standardized diagnosis, segmenting a tongue region from a facial image captured and classifying tongue coating are inevitable but difficult since the colors of a tongue, lips, and skin in a mouth are similar. The proposed method includes preprocessing, over-segmenting, detecting the edge with a local minimum over a shading area from the structure of a tongue, correcting local minima or detecting the edge with the greatest color difference, selecting one edge to correspond to a tongue shape, and smoothing edges, where preprocessing consists of down-sampling to reduce computation time, histogram equalization, and edge enhancement, which produces the region of a segmented tongue. Finally, the systematic procedure separated only a tongue region from a face image with a tongue, which was obtained from a digital tongue diagnosis system. Oriental medical doctors' evaluation for the results illustrated that the segmented region excluding a non-tongue region provides important information for the accurate diagnosis. The proposed method can be used for an objective and standardized diagnosis and for an u-Healthcare system.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.85-91
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• 2015

This paper presents a novel frame rate up-conversion (FRUC) algorithm. Existing algorithms, in general, employ rectangular blocks for motion estimation and arbitrary shape of an actual object region cannot be precisely represented. On the other hand, the proposed wedge-shaped block partitioning algorithm partitions a rectangular block into two wedge-shaped blocks using the texture information, which makes better approximation for an actual object region. The wedge-shaped block partitioning algorithm as well as the adaptive motion vector prediction algorithm is used to reliably estimate the actual motion. Experimental results show that the proposed FRUC algorithm is superior to existing algorithms up to 1.988dB in PSNR and 0.0167 in SSIM comparisons.

• Journal of Korea Multimedia Society
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• v.2 no.3
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• pp.320-328
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• 1999

The Laplacian operator is usually used as a regularization operator which may be used as any differential operator in the regularization iterative restoration. In this paper, several kinds of differential operator and 1-H operator that has been used in our lab as well, as a regularization operator, were compared with each other. In the restoration of noisy motion-blurred images, 1-H operator worked better than Laplacian operator in flat region, but in the edge the Laplacian operator operated better. For noisy gaussian-blurred image, 1-H operator worked better in the edge, while in flat region the Laplacian operator resulted better. In regularization, smoothing the noise and resorting the edges should be considered at the same time, so the regions divided into the flat, the middle, and the detailed, which were processed in separate and compared their MSE. Laplacian and 1-H operator showed to be suitable as the regularization operator, while the other differential operators appeared to be diverged as iterations proceeded.

• Journal of Intelligence and Information Systems
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• v.8 no.1
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• pp.87-101
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• 2002

In this paper, we proposes the car plate recognition and describe the parking control system using the proposed car plate recognition algorithm. The car plate recognition system using color information and fuzzy c-means algorithm consists of the extraction part of a car plate from a car image and the recognition part of characters in the extracted car plate. This paper eliminates green noise from car image using the mode smoothing and extract plate region using green and white information of RGB color. The codes of extracted plate region is extracted by histogram based approach method and is recognized by fuzzy c-means algorithm. For experimental, we tested 80 car images. We shows that the proposed extraction method is better than that from the color information of RGB and HSI, respectively. So, we can know that the proposed car plate recognition method using fuzzy c-means algorithm was very efficient. We develop the parking control system using the proposed car plate recognition method, which showed performance improvement by the experimental results.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.5 s.311
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• pp.37-44
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• 2006

In this paper, a new version of the ICM method(MICM, modified ICM) in which the contextual information is modelled by Markov random fields (MRF) is introduced. To extract the feature, a new local MRF model with a fitting block neighbourhood is proposed. This model selects contextual information not only from the relative intensity levels but also from the geometrically directional position of neighbouring cliques. Feature extraction depends on each block's contribution to the local variance. They discriminates it into several regions, for example context and background. Boundaries between these regions are also distinctive. The proposed algerian performs segmentation using directional block fitting procedure which confines merging to spatially adjacent elements and generates a partition such that pixels in unified cluster have a homogeneous intensity level. From experiment with ink rubbed copy images(Takbon, 拓本), this method is determined to be quite effective for feature identification. In particular, the new algorithm preserves the details of the images well without over- and under-smoothing problem occurring in general iterated conditional modes (ICM). And also, it may be noted that this method is applicable to the handwriting recognition.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.47-53
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• 2007

True motion estimation is necessary for deinterlacing, frame-rate conversion, and film judder compensation. There have been several block-based approaches to find true motion vectors by tracing minimum sum-of-absolute-difference (SAD) values by considering spatial and temporal consistency. However, the algorithms cannot find robust motion vectors when the texture of objects is changed. To find the robust motion vectors in the region, a recursive vector selection scheme and an adaptive weighting parameter are proposed. Previous frame vectors are recursively averaged to be utilized for motion error region. The weighting parameter controls fidelity to input vectors and the recursively averaged ones, where the input vectors come from the conventional estimators. If the input vectors are not reliable, then the mean vectors of the previous frame are used for temporal consistency. Experimental results show more robust motion vectors than those of the conventional methods in time-varying texture objects.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.610-616
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• 2012

In this paper, we present a method to detect an object such as ship, rock and buoy from sea IR image for the safety navigation. To this end, we do the image smoothing first and the apply watershed algorithm to segment image into subregions. Since watershed algorithm almost always produces over-segmented regions, it requires posterior merging process to get meaningful segmented regions. We propose an efficient merger algorithm that requires only two times of direct access to the pixels regardless of the number of regions. Also by analyzing IR image obtained from sea environments, we could find out that most horizontal edge come out from object regions. For the given input IR image we extract horizontal edge and eliminate isolated edges produced from background and noises by adopting morphological operator. Among the segmented regions, the regions that have horizontal edges are extracted as final results. Experimental results show the adequacy of the proposed method.

• Atmosphere
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• v.31 no.1
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• pp.85-100
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• 2021

The necessity of accurate high-resolution meteorological forecasts becomes increasing in socio-economical applications and disaster risk management. The Korea Meteorological Administration Post-Processing (KMAPP) system has been operated to provide high-resolution meteorological forecasts of 100 m over the South Korea region. This study evaluates and improves the KMAPP performance in simulating wind speeds over complex terrain areas using the ICE-POP 2018 field campaign measurements. The mountainous measurements give a unique opportunity to evaluate the operational wind speed forecasts over the complex terrain area. The one-month wintertime forecasts revealed that the operational Local Data Assimilation and Prediction System (LDAPS) has systematic errors over the complex mountainous area, especially in deep valley areas, due to the orographic smoothing effect. The KMAPP reproduced the orographic height variation over the complex terrain area but failed to reduce the wind speed forecast errors of the LDAPS model. It even showed unreasonable values (~0.1 m s-1) for deep valley sites due to topographic overcorrection. The model's static parameters have been revised and applied to the KMAPP-Wind system, developed newly in this study, to represent the local topographic characteristics better over the region. Besides, sensitivity tests were conducted to investigate the effects of the model's physical correction methods. The KMAPP-Wind system showed better performance in predicting near-surface wind speed during the ICE-POP period than the original KMAPP version, reducing the forecast error by 21.2%. It suggests that a realistic representation of the topographic parameters is a prerequisite for the physical downscaling of near-ground wind speed over complex terrain areas.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.174-174
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• 2023

The impact of global warming on the south Asian summer monsoon is of critical importance for the large population of this region. This study aims to investigate the future changes of the precipitation extremes during pre-monsoon and monsoon, across this region in a more organized regional structure. The study area is divided into six major divisions based on the Köppen-Geiger's climate structure and 10 sub-divisions considering the geographical locations. The future changes of extreme precipitation indices are analyzed for each zone separately using five indices from ETCCDI (Expert Team on Climate Change Detection and Indices); R10mm, Rx1day, Rx5day, R95pTOT and PRCPTOT. 10 global climate model (GCM) outputs from the latest CMIP6 under four combinations of SSP-RCP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) are used. The GCMs are bias corrected using nonparametric quantile transformation based on the smoothing spline method. The future period is divided into near future (2031-2065) and far future (2066-2100) and then the changes are compared based on the historical period (1980-2014). The analysis is carried out separately for pre-monsoon (March, April, May) and monsoon (June, July, August, September). The methodology used to compare the changes is probability distribution functions (PDF). Kernel density estimation is used to plot the PDFs. For this study we did not use a multi-model ensemble output and the changes in each extreme precipitation index are analyzed GCM wise. From the results it can be observed that the performance of the GCMs vary depending on the sub-zone as well as on the precipitation index. Final conclusions are made by removing the poor performing GCMs and by analyzing the overall changes in the PDFs of the remaining GCMs.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.109-112
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• 1999

Image segmentation is the critical first step in image information extraction for computer vision systems. Clustering methods have been used extensively in color image segmentation. Most analytic fuzzy clustering approaches are divided from the fuzzy c-means(FCM) algorithm. The FCM algorithm uses fie probabilistic constraint that the memberships of a data point across classes sum to 1. However, the memberships resulting from the FCM do not always correspond to the intuitive concept of degree of belonging or compatibility. Moreover, the FCM algorithm has considerable trouble under noisy environments in the feature space. Recently, a possibilistic approach to clustering(PCM) for solving above problems was proposed. In this paper, we used the PCM for color image segmentation. This approach differs from existing fuzzy clustering methods for color image segmentation in that the resulting partition of the data can be interpreted as a possibilistic partition. So, the problems in the FCM can be solved by the PCM. But, the clustering results by the PCM are not smoothly bounded, and they often have holes. The region growing was used as a postprocessing after smoothing the noise points in the pixel seeds. In our experiments, we illustrate that the PCM us reasonable than the FCM in noisy environments.