• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.2 s.5
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• pp.19-28
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• 2004

This paper shows how to compensate the skew from the traffic sign included in the natural image and extract the text. The research deals with the Process related to the array image. Ail the process comprises four steps. In the first fart we Perform the preprocessing and Canny edge extraction for the edge in the natural image. In the second pan we perform preprocessing and postprocessing for Hough Transform in order to extract the skewed angle. In the third part we remove the noise images and the complex lines, and then extract the candidate region using the features of the text. In the last part after performing the local binarization in the extracted candidate region, we demonstrate the text extraction by using the differences of the features which appeared between the tett and the non-text in order to select the unnecessary non-text. After carrying out an experiment with the natural image of 100 Pieces that includes the traffic sign. The research indicates a 82.54 percent extraction of the text and a 79.69 percent accuracy of the extraction, and this improved more accurate text extraction in comparison with the existing works such as the method using RLS(Run Length Smoothing) or Fourier Transform. Also this research shows a 94.5 percent extraction in respect of the extraction on the skewed angle. That improved a 26 percent, compared with the way used only Hough Transform. The research is applied to giving the information of the location regarding the walking aid system for the blind or the operation of a driverless vehicle

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.4105-4105
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• 2001

Near infrared spectroscopy (NIRS) was employed to qualify and quantify on survival, the injury rate and apoptosis of the human breast cancer cell line MCF-7 cells. MCF-7 cells were cultured in RPMI medium supplemented with 10% FCS in a 95% air and 5% CO2 atmosphere at 37$^{\circ}C$. For the viable cells preparation, cells were de-touched by 0.1% of trypsin treatment and washed with RPMI supplemented with 10% FCS medium by centrifugation at 1000 rpm for 3min. For the dead cells preparation, cells were de-touched by a cell scraper. The cells were counted by a hemacytometer, and the viability was estimated by the exclusion method with frypan blue dye. Each viable and dead cells were suspended in PBS (phosphate bufferred saline) or milk at the cell density desired. For the quantitative determination of cell death by measuring the LDH (lactate dehydrogenase) activity liberated from cells with cell membrane injuries, LDH-Cytotoxic Test Wako (Wako, Pure Pharmaceutical Co. Ltd., Japan) was used. We found that NIRS measurement of MCF-7 cells at the density range could evaluate and monitor the different characteristics of living cells and dead cells. The spectral analysis was performed in two wavelength ranges and with 1,4, 10 mm pathlength. Different spectral data pretreatment and chemometrics methods were used. We applied SIMCA classificator on spectral data of living and dead cells and obtained good accuracy when identifying each class. Bigger variation in the spectra of living cells with different concentrations was observed when compared to the same concentrations of dead cells. PLS was used to measure the number of cells in PBS. The best model for measurement of dead cells, as well as living cells, was developed when raw spectra in the 600-1098 nm region and 4 mm pathlength were used. Smoothing and second derivative spectral data pretreatment gave worst results. The analysis of PLS loading explained this result with the scatter effect found in the raw spectra and increased with the number of cells. Calibration for cell count in the 1100-2500 nm region showed to be very inaccurate.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.1
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• pp.16-22
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• 2012

In this paper, we proposed a method of baseline correction for analysis of Raman spectra of platelets from Alzheimer's disease (AD) transgenic mice. Measured Raman spectra include the meaningful information and unnecessary noise which is composed of baseline and additive noise. The Raman spectrum is divided into the local region including several peaks and the spectrum of the region is modeled by curve fitting using Gaussian model. The additive noise is clearly removed from the process of replacing the original spectrum with the fitted model. The baseline correction after interpolating the local minima of the fitted model with linear, piecewise cubic Hermite and cubic spline algorithm. The baseline corrected models extract the feature with principal component analysis (PCA). The classification result of support vector machine (SVM) and maximum $a$ posteriori probability (MAP) using linear interpolation method showed the good performance about overall number of principal components, especially SVM gave the best performance which is about 97.3% true classification average rate in case of piecewise cubic Hermite algorithm and 5 principal components. In addition, it confirmed that the proposed baseline correction method compared with the previous research result could be effectively applied in the analysis of the Raman spectra of platelet.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.6
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• pp.1094-1103
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• 2001

According to standard procedures as defined in the users handbook for sea level data processes, I was compared to Topex/Poseidon sea level data from the first 350days of mission and Tide Gauge sea level data from the Amsterdam- Crozet- Kerguelen region in the South Indian Ocean. The comparison improves significantly when many factors for the corrections were removed, then only the aliased oceanic tidal energy is removed by oceanic tide model(11) in this period. Making the corrections and smoothing the sea level data ()ver 60km along-track segments and the Tide Gauge sea level data for the time series results in the digital correlation and RMS difference between the two data of c=-0.12 and rms= 11.4cm, c=0.55 and rms=5.38cm, c=0.83 and rms=2.83cm, and c=0.24 and rms=6.72 for the Amsterdam, Crozet and Kerguelenplateau, and Kerguelen coast, respectively. It was also found that the Kerguelen plateau has a comparisons due to propagating signals(the baroclinic Rossby wave with velocity of -3.9 ~-4.2cm/sec, period of 167days and amplitude of 10cm) that introduce temporal lags(${\gamma}$: 10~30days) between the altimeter and tide gauge time series. The conclusion is that on timescales longer than about 10days the RMS sea level errors are less than or of the order of several centimeters and are mainly due to the effects of currents rather than the effects of stories(water temperature, density) and winds.

• Journal of the Korea Computer Graphics Society
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• v.20 no.3
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• pp.1-8
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• 2014

We propose a method to compose a foreground object into a background image, where the foreground object is a part (or a region) of an image taken by a front-facing camera and the background image is a whole image taken by a back-facing camera in a smart phone at the same time. Recent high-end cell-phones have two cameras and provide users with preview video before taking photos. We extract the foreground object that is moving along with the front-facing camera using the optical flow during the preview. We compose the extracted foreground object into a background image using a simple image composition technique. For better-looking result in the composed image, we apply a border smoothing technique using a weighted-border mask to blend transparency from background to foreground. Since constructing and grouping pixel-level dense optical flow are quite slow even in high-end cell-phones, we compute a mask to extract the foreground object in low-resolution image, which reduces the computational cost greatly. Experimental result shows the effectiveness of our extraction and composition techniques, with much less computational time in extracting the foreground object and better composition quality compared with Poisson image editing technique which is widely used in image composition. The proposed method can improve limitedly the color bleeding artifacts observed in Poisson image editing using weighted-border blending.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.3
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• pp.227-239
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• 2008

Processing LiDAR (Light Detection And Ranging) data obtained from ALS (Airborne Laser Scanning) systems mainly involves organization and segmentation of the data for 3D object modeling and mapping purposes. The ALS systems are viable and becoming more mature technology in various applications. ALS technology requires complex integration of optics, opto-mechanics and electronics in the multi-sensor components, Le. data captured from GPS, INS and laser scanner. In this study, digital image processing techniques mainly were implemented to gray level coded image of the LiDAR data for building extraction and superstructures segmentation. One of the advantages to use gray level image is easy to apply various existing digital image processing algorithms. Gridding and quantization of the raw LiDAR data into limited gray level might introduce smoothing effect and loss of the detail information. However, smoothed surface data that are more suitable for surface patch segmentation and modeling could be obtained by the quantization of the height values. The building boundaries were precisely extracted by the robust edge detection operator and regularized with shape constraints. As for segmentation of the roof structures, basically region growing based and gap filling segmentation methods were implemented. The results present that various image processing methods are applicable to extract buildings and to segment surface patches of the superstructures on the roofs. Finally, conceptual methodology for extracting characteristic information to reconstruct roof shapes was proposed. Statistical and geometric properties were utilized to segment and model superstructures. The simulation results show that segmentation of the roof surface patches and modeling were possible with the proposed method.

• Journal of the Korea Society of Computer and Information
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• v.21 no.12
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• pp.11-18
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• 2016

Magnetic resonance image (MRI) is widely used in brain research field and medical image. Especially, non-invasive brain activation acquired image technique, which is functional magnetic resonance image (fMRI) is used in brain study. In this study, we investigate brain activation occurred by LED light stimulation. For investigate of brain activation in experimental small animal, we used high magnetic field 9.4T MRI. Experimental small animal is Balb/c mouse, method of fMRI is using echo planar image (EPI). EPI method spend more less time than any other MRI method. For this reason, however, EPI data has low contrast. Due to the low contrast, image pre-processing is very hard and inaccuracy. In this study, we planned the study protocol, which is called block design in fMRI research field. The block designed has 8 LED light stimulation session and 8 rest session. All block is consist of 6 EPI images and acquired 1 slice of EPI image is 16 second. During the light session, we occurred LED light stimulation for 1 minutes 36 seconds. During the rest session, we do not occurred light stimulation and remain the light off state for 1 minutes 36 seconds. This session repeat the all over the EPI scan time, so the total spend time of EPI scan has almost 26 minutes. After acquired EPI data, we performed the analysis of this image data. In this study, we analysis of EPI data using statistical parametric map (SPM) software and performed image pre-processing such as realignment, co-registration, normalization, smoothing of EPI data. The pre-processing of fMRI data have to segmented using this software. However this method has 3 different method which is Gaussian nonparametric, warped modulate, and tissue probability map. In this study we performed the this 3 different method and compared how they can change the result of fMRI analysis results. The result of this study show that LED light stimulation was activate superior colliculus region in mouse brain. And the most higher activated value of segmentation method was using tissue probability map. this study may help to improve brain activation study using EPI and SPM analysis.

• Journal of Korea Water Resources Association
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• v.54 no.5
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• pp.301-309
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• 2021

In this study, we tried to improve the performance of the existing U-net-based deep learning rainfall prediction model, which can weaken the meaning of time series order. For this, ConvLSTM2D U-Net structure model considering temporal consistency of data was applied, and we evaluated accuracy of the ConvLSTM2D U-Net model using a RainNet model and an extrapolation-based advection model. In addition, we tried to improve the uncertainty in the model training process by performing learning not only with a single model but also with 10 ensemble models. The trained neural network rainfall prediction model was optimized to generate 10-minute advance prediction data using four consecutive data of the past 30 minutes from the present. The results of deep learning rainfall prediction models are difficult to identify schematically distinct differences, but with ConvLSTM2D U-Net, the magnitude of the prediction error is the smallest and the location of rainfall is relatively accurate. In particular, the ensemble ConvLSTM2D U-Net showed high CSI, low MAE, and a narrow error range, and predicted rainfall more accurately and stable prediction performance than other models. However, the prediction performance for a specific point was very low compared to the prediction performance for the entire area, and the deep learning rainfall prediction model also had limitations. Through this study, it was confirmed that the ConvLSTM2D U-Net neural network structure to account for the change of time could increase the prediction accuracy, but there is still a limitation of the convolution deep neural network model due to spatial smoothing in the strong rainfall region or detailed rainfall prediction.