• Journal of Korea Society of Industrial Information Systems
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• v.17 no.3
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• pp.35-42
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• 2012

The database for driving patterns can be utilized in various system such as automatic driving system, driver safety system, and it can be helpful to monitor driving style. Therefore, we propose a driving pattern recognition system in which the sensor streams from a smartphone are recorded and used for recognizing driving events. In this paper we focus on the driving pattern recognition that is an essential and preliminary step of driving style recognition. We divide input sensor streams into 7 driving patterns such as, Left-turn(L), U-turn(U), Right-turn(R), Rapid-Braking(RB), Quick-Start(QS), Rapid-Acceleration (RA), Speed-Bump(SB). To classify driving patterns, first, a preprocessing step for data smoothing is followed by an event detection step. Last the detected events are classified by DTW(Dynamic Time Warping) algorithm. For assisting drivers we provide the classified pattern with the corresponding video stream which is recorded with its sensor stream. The proposed system will play an essential role in the safety driving system or driving monitoring system.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.6
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• pp.665-674
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• 2001

In this paper, a reduced complexity fillet to simultaneously suppress the blocking and ringing artifacts of compressed video sequence is addressed. A new one dimensional regularized function to incorporate the smoothness to its neighboring pixels into the solution is defined, resulting in very low complexity filter The proposed regularization function consists of two sub-functions that combine local data fidelity and local smoothing constraints. The regularization parameters to control the trade-off between the local fidelity to the data and the smoothness are determined by available overhead information in decoder, such as maroc-block type and quantization step size. In addition, the regularization parameters are designed to have the limited range and stored as look-up-table, and therefore, the computational cost to determine the parameters can be reduced. The experimental results show the capability and efficiency of the proposed algorithm.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.11
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• pp.56-63
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• 2012

In this paper, adaptive unsharp masking using bilateral filter, edge-preserving smoothing filter is proposed to reduce the overshoot and jagging artifact in sharpening images. Previous image enhancement methods including unsharp masking(UM) can emphasize high-frequency details strongly, but often cause several artifacts such as overshooting, noise, jagging and so on. Proposed image enhancement method preserves edges well because of using bilateral filter and sensitively controls a weight according to edge's directions. Therefore, it enhances sharpness and effectively reduces overshoot and jagging artifacts. Simulation results comparing output of previous AUM with proposed method show that proposed algorithm makes images properly enhanced, and we know that overshoot and jagging artifacts are many reduced.

• Progress in Medical Physics
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• v.16 no.3
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• pp.138-147
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• 2005

The Monte Carlo method cannot have been used for routine treatment planning because of heavy time consumption for the acceptable accuracy. Since calculation time is proportional to particle histories, we can save time by decreasing the number of histories. However, a small number of histories can cause serious uncertainties. In this study, we proposed Monte Carlo dose computation time and uncertainty reduction method using specially designed filters and adaptive denoising process. Proposed algorithm was applied to 6 MV photon and 21 MeV electron dose calculations in homogeneous and heterogeneous phantoms. Filtering time was negligible comparing to Monte Carlo simulation time. The accuracy was improved dramatically in all situations and the simulation of 1 $\%$ to 10$\%$ number of histories of benchmark in photon and electron dose calculation showed the most beneficial result. The empirical reduction of necessary histories was about a factor of ten to fifty from the result.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.5
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• pp.575-583
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• 2009

Various algorithms and methods, related to preprocessing of LiDAR data, are being developed and proposed. These methods are two ways, one of them is to use the regular form such as DSM or the image converted from raw LiDAR data, and the other is to use raw LiDAR data directly. The image processing method is one of representative method for the regular grid form data. This method is easy to apply to a numerical analysis technique and has an advantage of modeling and noise elimination through smoothing, but it lose the information during the data conversion. This study apply the image processing method to the irregular raw LiDAR data directly for the extracting ground information with minimized information loss and evaluate the extracting accuracy of ground information.

• Journal of Sensor Science and Technology
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• v.15 no.6
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• pp.433-441
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• 2006

Because of the low power and low rate of a sensor network, outlier is frequently occurred in the time series data of sensor network. In this paper, we suggest periodic pattern analysis that is applied to the time series data of sensor network and predict outlier that exist in the time series data of sensor network. A periodic pattern is minimum period of time in which trend of values in data is appeared continuous and repeated. In this paper, a quantization and smoothing is applied to the time series data in order to analyze the periodic pattern and the fluctuation of each adjacent value in the smoothed data is measured to be modified to a simple data. Then, the periodic pattern is abstracted from the modified simple data, and the time series data is restructured according to the periods to produce periodic pattern data. In the experiment, the machine learning is applied to the periodic pattern data to predict outlier to see the results. The characteristics of analysis of the periodic pattern in this paper is not analyzing the periods according to the size of value of data but to analyze time periods according to the fluctuation of the value of data. Therefore analysis of periodic pattern is robust to outlier. Also it is possible to express values of time attribute as values in time period by restructuring the time series data into periodic pattern. Thus, it is possible to use time attribute even in the general machine learning algorithm in which the time series data is not possible to be learned.

• Journal of Digital Contents Society
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• v.15 no.6
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• pp.675-682
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• 2014

Prostate is only an organ of men. To diagnose the disease of the prostate, generally transrectal ultrasound(TRUS) images are used. Detecting its boundary is a challenging and difficult task due to weak prostate boundaries, speckle noise and the short range of gray levels. In this paper a method for automatic prostate segmentation in TRUS images using Support Vector Machine(SVM) is presented. This method involves preprocessing, extracting Gabor feature, training, and prostate segmentation. The speckle reduction for preprocessing step has been achieved by using stick filter and top-hat transform has been implemented for smoothing. Gabor filter bank for extraction of rotation-invariant texture features has been implemented. SVM for training step has been used to get each feature of prostate and nonprostate. Finally, the boundary of prostate is extracted. A number of experiments are conducted to validate this method and results shows that the proposed algorithm extracted the prostate boundary with less than 10% relative to boundary provided manually by doctors.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.121-132
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• 2005

In the formation of images in a camera, the exposure time is appropriately adjusted to obtain a good image. Hence for a successful alignment of a sequence of images to the same scene, it is required to compensate the different exposure times. If we have no knowledge regarding the exposure time, then we should develop an algorithm that can compensate an image with respect to a reference image without using any camera formation models. In this paper, an exposure compensation is performed by designing predictors based on the conditional expectation between the reference and input images. Further, an adaptive predictor design is conducted to manage the irregular exposure or histogram problem. In order to alleviate the blocking artifact and the overfitting problems in the adaptive scheme, a smoothing technique, which uses the pixels of the adjacent blocks, is proposed. We successfully conducted the exposure compensation using real images obtained from digital cameras and the transmission electron microscopy.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.2
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• pp.166-172
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• 2018

To develop and evaluate the real-time SAR(Synthetic Aperture Radar) motion measurement system, true antenna phase center(APC) positions during SAT(Synthetic Aperture Time) are needed. In this paper, CDGPS(Carrier phase Differential Global Positioning System) post processing method is proposed to get the true APC position for spotlight SAR image formation. The CDGPS position is smoothed to remove high frequency noise which exists inherently in the carrier phase measurement. This paper shows smoothed CDGPS data is enough to provide the true APC for high-quality SAR image formation through motion measurement result, phase error estimation and IRF(Impulse Response Function) analysis.

• Journal of Navigation and Port Research
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• v.39 no.4
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• pp.307-312
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• 2015

ARPA(Automatic Radar Plotting Aid) is a device to calculate CPA(closest point of approach)/TCPA(time of CPA), true course and speed of targets by vector operation of relative courses and speeds. The purpose of this study is to develop target acquisition and tracking technology for ARPA Radar implementation. After examining the previous studies, applicable algorithms and technologies were developed to be combined and basic ARPA functions were developed as a result. As for main research contents, the sequential image processing technology such as combination of grayscale conversion, gaussian smoothing, binary image conversion and labeling was deviced to achieve a proper target acquisition, and the NNS(Nearest Neighbor Search) algorithm was appllied to identify which target came from the previous image and finally Kalman Filter was used to calculate true course and speed of targets as an analysis of target behavior. Also all technologies stated above were implemented as a SW program and installed onboard, and verified the basic ARPA functions to be operable in practical use through onboard test.