• Journal of the Institute of Electronics Engineers of Korea TC
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• v.40 no.6
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• pp.213-224
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• 2003

The proposed algorithm can always set up the optimal threshold value regardless of channel characteristics using an adaptive threshold establishment method that determines the threshold level according to channel noise power, and then it uses the specially designed training symbols that can make the algorithm's estimation performance be less sensitive to power delay profile variation in a multipath channel. In result, the estimation performance of the proposed technique is less affected by channel characteristic variation.

• Journal of the Optical Society of Korea
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• v.13 no.3
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• pp.361-366
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• 2009

We propose the performance enhancing method optimization of an asynchronous 2.5 Gbps/1.25 Gbps optical subscriber network with inverse RZ (Return to Zero) coded downstream and NRZ (Non Return to Zero) upstream re-modulation by adjusting threshold level control of a receiver. We theoretically analyze the BER (Bit Error Rate) performance by modeling the occurrence of BER by simulation with MATLAB according to the types of downstream data. The results have shown that the normalized threshold level in an optical receiver could be saturated at 1/3 as the SNR (Signal to Noise Ratio) increases. The needed SNR for obtaining the BER $10^{-9}$ can be reduced by $\sim$5 dB by optimizing the normalized threshold level at 1/3 instead of by using the conventional receiver with threshold level of 0.5. The proposed system can be a useful technology for asynchronous optical access networks with asymmetric upstream and downstream data rates, because the improved minimum receiving power could replace a light source with a source with lower power and lower cost in an OLT (Optical Line Termination).

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.725-730
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• 2007

Gray-level histogram-based threshold selection methods such as Otsu's method, Huang and Wang's method, and etc. have been widely used for the threshold selection in image processing. They are simple and effective, but take too much time to determine the optimal multilevel threshold values as the number of thresholds are increased. In this paper, we measure correlation between gray-levels by using the Gaussian function and define a Gaussian-type finite mixture distribution which is combination of the Gaussian distribution function with the gray-level histogram, and propose a fast and effective threshold selection method using it. We show the effectiveness of the proposed through experimental results applied it to three images and the efficiency though comparison of the computational complexity of the proposed with that of Otsu's method.

• Communications of the Korean Mathematical Society
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• v.14 no.4
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• pp.805-814
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• 1999

We consider an MMPP/G/1/K finite queue with two-level threshold overload control. This model has frequently arisen in the design of the integrated communication systems which support a wide range applications having various Quality of Service(QoS) requirements. Through the supplementary variable method, se derive the queue length distribution.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.303-307
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• 2002

Although Synthetic Aperture Radar(SAR) is a very powerful and attractive tool, automatic interpretation of SAR images is extremely difficult because of several reason. Spatially, speckle noise reduction in SAR images is important step to interpret the SAR image at the preprocessing step. The speckle noise in SAR images is modeled to be multiplicative, and therefore, a signal-dependent noise. So, it has deflated many image-denoising algorithms that are based on additive noise model. In this paper, we propose an adaptive wavelet shrinkage method for speckle noise reduction in SAR images by analyzing the high frequency level in detail. We first decompose minutely the high frequency level to analyze the noise level. And then, we determine the weighting threshold value per the level, and layer. Finally, using those weighting threshold, we produce the efficient wavelet shrinkage method. So, this method not only reduces the speckle noise, but also preserves image detail and sharpness.

• Journal of Korea Water Resources Association
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• v.46 no.3
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• pp.287-299
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• 2013

The objective of this study is to derive and evaluate the drought threshold level based on hydro-meteorological data using historical drought events. After collecting the drought events during 1991 to 2009 year, the observed meteorological data and estimated hydrological component from LSM are used as input for the percentile analysis that is drought analysis data. The drought threshold level that precipitation and runoff of 3 month duration are less than 35%, soil moisture of 2 month duration is less than 35% and evapotranspiration of 3 month duration is more than 65% is derived using ROC analysis that are objective test method. ROC analysis with SPI (3) is performed to evaluate the applicability of threshold level in the domestic. As a result, it can be concluded that the derived drought threshold level show better performance to reflect the historical drought events than SPI (3) and it reasonably explain the spatial drought situation through the spatial analysis.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.159-162
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• 1998

Land cover and land use classification on a huge scale, e.g. national or continental scale, has become more and more important because environmental researches need land cover: And land use data on such scales. We developed a crop field extraction method, which is one of the steps in our land cover classification system for a huge area. Firstly, a crop field model is defined to characterize "crop field" in terms of NDVI value and textual information Textual information is represented by the density of straight lines which are extracted by wavelet transform. Secondly, candidates of NDVI threshold value are determined by "scale-space filtering" method. The most appropriate threshold value among the candidates is determined by evaluating the line density of the area extracted by the threshold value. Finally, the crop field is extracted by applying level slicing to Landsat TM image with the threshold value determined above. The experiment demonstrates that the extracted area by this method coincides very well with the one extracted by visual interpretation.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.363-374
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• 2010

In this paper, we propose the method for extracting Homogeneity Threshold($H_T$) and for segmenting homogeneous regions by USRG(Unseeded Region Growing) with $H_T$. The $H_T$ is a criterion to distinguish homogeneity in neighbor pixels and is computed automatically from the original image by proposed method. Theoretical background for proposed method is based on the Otsu's single level threshold method. The method is used to divide a small local part of original image int o two classes and the sum($\sigma_c$) of standard deviations for the classes to satisfy special conditions for distinguishing as different regions from each other is used to compute $H_T$. To find validity for proposed method, we compare the original image with the image that is regenerated with only the segmented homogeneous regions and show up the fact that the difference between two images is not exist visually and also present the steps to regenerate the image in order the size of segmented homogeneous regions and in order the intensity that includes pixels. Also, we show up the validity of proposed method with various results that is segmented using the homogeneity thresholds($H^*_T$) that is added a coefficient ${\alpha}$ for adjusting scope of $H_T$. We expect that the proposed method can be applied in various fields such as visualization and animation of natural image, anatomy and biology and so on.

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.110-115
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• 2019

Blur detection and segmentation play vital role in many computer vision applications. Among various methods, local binary pattern based methods provide reasonable blur detection results. However, in conventional local binary pattern based methods, the blur map is computed by using a fixed threshold irrespective of the type and level of blur. It may not be suitable for images with variations in imaging conditions and blur. In this paper we propose an effective method based on local binary pattern with adaptive threshold for blur detection. The adaptive threshold is computed based on the model learned through the multinomial logistic regression. The performance of the proposed method is evaluated using different datasets. The comparative analysis not only demonstrates the effectiveness of the proposed method but also exhibits it superiority over the existing methods.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.6
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• pp.587-595
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• 2011

In this paper, we focus on the issue of automatic selection for multi-level threshold, and we greatly improve the efficiency of Otsu's method for image segmentation based on genetic algorithm. We have investigated and evaluated the performance of the Otsu and Valley-emphasis threshold methods. Based on this observation we propose a method for automatic threshold method that segments an image into more than two regions with high performance and processing in real-time. Our paper introduced new peak detection, combines with evolution algorithm using MAGA (Modified Adaptive Genetic Algorithm) and HCA (Hill Climbing Algorithm), to find the best threshold automatically, accurately, and quickly. The experimental results show that the proposed evolutionary algorithm achieves a satisfactory segmentation effect and that the processing time can be greatly reduced when the number of thresholds increases.