• Korean Journal of Optics and Photonics
• /
• v.20 no.1
• /
• pp.16-22
• /
• 2009

We propose a method to convert a two-dimensional movie to a three-dimensional movie using normalized cut and optical flow. In this paper, we segment an image of a two-dimensional movie to objects first, and then estimate the depth of each object. Normalized cut is one of the image segmentation algorithms. For improving speed and accuracy of normalized cut, we used a watershed algorithm and a weight function using optical flow. We estimate the depth of objects which are segmented by improved normalized cut using optical flow. Ordinal depth is estimated by the change of the segmented object label in an occluded region which is the difference of absolute values of optical flow. For compensating ordinal depth, we generate the relational depth which is the absolute value of optical flow as motion parallax. A final depth map is determined by multiplying ordinal depth by relational depth, then dividing by average optical flow. In this research, we propose the two-dimensional/three-dimensional movie conversion method which is applicable to all three-dimensional display devices and all two-dimensional movie formats. We present experimental results using sample two-dimensional movies.

• Journal of Korea Water Resources Association
• /
• v.51 no.8
• /
• pp.677-686
• /
• 2018

There remain numerous ungauged watersheds in Korea owing to limited spatial and temporal streamflow data with which to estimate hydrological model parameters. To deal with this problem, various regionalization approaches have been proposed over the last several decades. However, the results of the regionalization models differ according to climatic conditions and regional physical characteristics, and the results of the regionalization models in previous studies are generally inconclusive. Thus, to improve the performance of the regionalization methods, this study attaches hydrological model parameters obtained using a spatial proximity model to the explanatory variables of a regional regression model and defines it as a hybrid regionalization model (hybrid model). The performance results of the hybrid model are compared with those of existing methods for 37 test watersheds in South Korea. The GR4J model parameters in the gauged watersheds are estimated using a shuffled complex evolution algorithm. The variation inflation factor is used to consider the multicollinearity of watershed characteristics, and then stepwise regression is performed to select the optimum explanatory variables for the regression model. Analysis of the results reveals that the highest modeling accuracy is achieved using the hybrid model on RMSE overall the test watersheds. Consequently, it can be concluded that the hybrid model can be used as an alternative approach for modeling ungauged watersheds.

• Journal of Korea Water Resources Association
• /
• v.34 no.4
• /
• pp.303-316
• /
• 2001

In this study, Radial Basis Function(RBF) Neural Networks Model, a kind of Hybrid Neural Networks was applied to hydrological forecasting in a small watershed. RBF Neural Networks Model has four kinds of parameters in it and consists of unsupervised and supervised training patterns. And Gaussian Kernel Function(GKF) was used among many kinds of Radial Basis Functions(RBFs). K-Means clustering algorithm was applied to optimize centers and widths which ate the parameters of GKF. The parameters of RBF Neural Networks Model such as centers, widths weights and biases were determined by the training procedures of RBF Neural Networks Model. And, with these parameters the validation procedures of RBF Neural Networks Model were carried out. RBF Neural Networks Model was applied to Wi-Stream basin which is one of the IHP Representative basins in South Korea. 10 rainfall events were selected for training and validation of RBF Neural Networks Model. The results of RBF Neural Networks Model were compared with those of Elman Neural Networks(ENN) Model. ENN Model is composed of One Step Secant BackPropagation(OSSBP) and Resilient BackPropagation(RBP) algorithms. RBF Neural Networks shows better results than ENN Model. RBF Neural Networks Model spent less time for the training of model and can be easily used by the hydrologists with little background knowledge of RBF Neural Networks Model.

• Journal of Korean Society for Geospatial Information Science
• /
• v.19 no.3
• /
• pp.107-114
• /
• 2011

In this study, when interpreting leakage using the concept of geographical dispersion based on grid, to choose an appropriate spatial resolution, the statistical characteristics of drainage path length and the pattern of change of hydrodynamic parameters have been observed. Drainage path length has been calculated using an 8-direction algorithm from digital elevation model, from which the hydrodynamic parameters of the watershed were estimated. The scales of topographical map for this analysis are 1:5,000 and 1:25,000, appling grid sizes 5, 10, 15, 20 m and 20, 30, 50, 100, 150, 200 m, respectively. As results of this analysis, depending on the scale of stream networks, the statistical characteristics of drainage path length by spatial resolution and hydrodynamic parameters of the watershed have been changed. Based on the above results, when interpreting leakage using the concept of the geographical dispersion based on grid, in the case of 1:5,000 scale topographical map, a spatial resolution of 5 m will be better showing geographical and hydrodynamic characteristics to apply to the well developed stream network in basins, spatial resolution of 5~20 m to the less developed stream network in basins. And in the case of 1:25,000 scale topographical map, spatial resolution below 50 m is more desirable to show above two characteristics to apply to both cases.

• Journal of Korean Society on Water Environment
• /
• v.27 no.1
• /
• pp.61-72
• /
• 2011

In order to assess hydrologic and nonpoint source pollutant behaviors in a watershed with Soil and Water Assessment Tool (SWAT) model, the accuracy evaluation of SWAT model should be conducted prior to the application of it to a watershed. When calibrating and validating hydrological components of SWAT model, the Nash-Sutcliffe efficiency coefficient (EI) has been widely used. However, the EI value has been known as it is affected sensitively by big numbers among the range of numbers. In this study, a Web-based flow clustering EI estimation system using K-means clustering algorithm was developed and used for SWAT hydrology evaluation. Even though the EI of total streamflow was high, the EI values of hydrologic components (i.e., direct runoff and baseflow) were not high. Also when the EI values of flow group I and II (i.e., low and high value group) clustered from direct runoff and baseflow were computed, respectively, the EI values of them were much lower with negative EI values for some flow group comparison. The SWAT auto-calibration tool estimated values also showed negative EI values for most flow group I and II of direct runoff and baseflow although EI value of total streamflow was high. The result obtained in this study indicates that the SWAT hydrology component should be calibrated until all four positive EI values for each flow group of direct runoff and baseflow are obtained for better accuracy both in direct runoff and baseflow.

• The KIPS Transactions:PartB
• /
• v.15B no.6
• /
• pp.561-574
• /
• 2008

The spot detection phase of the 2-DE image analysis program segments a gel image into spot regions by an image segmentation algorithm and fits the spot regions to a spot shape model and quantifies the spot informations for the next phases. Currently the watershed algorithm is generally used as the segmentation algorithm and there are the Gaussian model and the diffusion model for the shape model. The diffusion model is closer to real spot shapes than the Gaussian model however spots have very various shapes and especially an asymmetric formation in x-coordinate and y-coordinate. The reason for asymmetric formation of spots is known that a protein could not be diffused completely because the 2-DE could not be processed under the ideal environment usually. Accordingly we propose an asymmetric diffusion model in this paper. The asymmetric diffusion model assumes that a protein spot is diffused from a disc at initial time of diffusing process, but is diffused asymmetrically for x-axis and y-axis respectively as time goes on. In experiments we processed spot matching for 19 gel images by using three models respectively and evaluated averages of SNR for comparing three models. As averages of SNR we got 14.22dB for the Gaussian model, 20.72dB for the diffusion model and 22.85dB for the asymmetric diffusion model. By experimental results we could confirm the asymmetric diffusion model is more efficient and more adequate for spot matching than the Gaussian model and the diffusion model.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.35S no.11
• /
• pp.152-165
• /
• 1998

The contour coding usually occupies the biggest part in the encoded bitstream, which causes the bottleneck problem of a region-based coding scheme. In this paper, new adaptive contour coding technique is proposed for the segmentation-based image coding. By adaptive contour coding considering contrast of neighbor regions in the proposed method, the overall bitrate can be significantly reduced without loss of the subjective image quality. After segmentation using watershed algorithm to the image, the contour segments are classified according to the contrast of the adjacent regions. Then, the contour segments between classified low contrast regions are highly compressed using morphological low pass filtering. The needed bits for encoding the contour information is reduced without loss of subjective image quality in the experiment.

• Journal of Korean Society on Water Environment
• /
• v.29 no.2
• /
• pp.184-195
• /
• 2013

Urbanization from agricultural/forest areas has been causing increased runoff and pollutant loads from it. Thus, numerous models have been developed to estimate NPS loading from urban area and Long-Term Hydrologic Impact Analysis (L-THIA) model has been used to evaluate effects of landuse changes on runoff and pollutant loads. However, the L-THIA model could not consider rainfall intensity in runoff evaluation. Therefore, the L-THIA model, capable of simulating runoff using 10-minute rainfall data, was applied to the study areas for evaluation of estimated runoff and NPS. The estimated Nash-Sutcliffe coefficient (NSE) values were over 0.6 for runoff, BOD, TN, and TP for most sites and watershed. The calibrated model was further extended to other counties for pollutant load potential evaluation. Pollutant load potential maps were developed and target areas were identified. As shown in this study, the L-THIA 2012 can be used for evaluation runoff and pollutant loads with limited data sets and its estimation could be used in identifying pollutant load hot spot areas for implementation of site-specific Best Management Practices.

• Journal of Korea Water Resources Association
• /
• v.36 no.6
• /
• pp.961-969
• /
• 2003

Formulation of quasi-dynamic wetness index was derived to predict the spatial and temporal distribution of the soil moisture. The algorithm of dynamic wetness index was developed through introducing the convolution integral with the rainfall input. The spatial and temporal behaviors of the wetness index of the Sulmachun Watershed was calculated using the digital elevation model(DEM) and the rainfall data for two years. The spatial distribution of the dynamic wetness index shows most dispersive feature of flow generation among the three assumptions of steady, quasi-dynamic and dynamic. The statistical distribution of the quasi-dynamic wetness index and the dynamic wetness index approximate to the steady state wetness index as the time step is increased. The dynamic wetness index shows mixed distribution of the normalized probability density function.

• The Journal of the Korea Contents Association
• /
• v.10 no.4
• /
• pp.19-27
• /
• 2010

In recent years, high-throughput genome-wide RNA interference screening is emerging as an essential tool to biologists in understanding complex cellular processes. The manual analysis of the large number of images produced in each study spends much time and the labor. Hence, automatic cellular image analysis becomes an urgent need, where segmentation is the first and one of the most important steps. However, those factors such as the region overlapping, a variety of shapes, and non-uniform local characteristics of cellular images become obstacles to efficient cell segmentation. To avoid the problem, a new watershed-based cell segmentation algorithm using a localized segmentation method and a feature vector is proposed in this paper. Localized approach in segmentation resolves the problems caused by a variety of shapes and non-uniform characteristics. In addition, the poor performance of segmentation in overlapped regions can be improved by taking advantage of a feature vector whose component features complement each other. Simulation results show that the proposed method improves the segmentation performance compared to the method in Cellprofiler.