• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.209-223
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• 2024

In the context of the fourth industrial revolution, data-driven decision-making has increasingly become pivotal. However, the integrity of data analysis is compromised if data quality is not adequately ensured, potentially leading to biased interpretations. This is particularly critical for water level data, essential for water resource management, which often encounters quality issues such as missing values, spikes, and noise. This study addresses the challenge of noise-induced data quality deterioration, which complicates trend analysis and may produce anomalous outliers. To mitigate this issue, we propose a noise removal strategy employing Wavelet Transform, a technique renowned for its efficacy in signal processing and noise elimination. The advantage of Wavelet Transform lies in its operational efficiency - it reduces both time and costs as it obviates the need for acquiring the true values of collected data. This study conducted a comparative performance evaluation between our Wavelet Transform-based approach and the Denoising Autoencoder, a prominent machine learning method for noise reduction.. The findings demonstrate that the Coiflets wavelet function outperforms the Denoising Autoencoder across various metrics, including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE). The superiority of the Coiflets function suggests that selecting an appropriate wavelet function tailored to the specific application environment can effectively address data quality issues caused by noise. This study underscores the potential of Wavelet Transform as a robust tool for enhancing the quality of water level data, thereby contributing to the reliability of water resource management decisions.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.154-154
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• 2017

Root is an important organ of plant that typically lies below the surface of the soil. Root surface determines the ability of plants to absorb nutrient and water from the surrounding soil. This study describes an application of image processing and computer vision which was implemented for rapid measurement of seed germination such as root length, surface area, average diameter, branching points of roots. A CCD camera was used to obtain RGB image of seed germination which have been planted by wet paper in a humidity chamber. Temperature was controlled at approximately 250C and 90% relative humidity. Pre-processing techniques such as color space, binarized image by customized threshold, removal noise, dilation, skeleton method were applied to the obtained images for root segmentation. The various morphological parameters of roots were estimated from a root skeleton image with the accuracy of 95% and the speed of within 10 seconds. These results demonstrated the high potential of computer vision technique for the measurement of seed germination.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.66 no.4
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• pp.200-205
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• 2017

In this paper, we propose a technique to determine the normal and arc of an alternating current using a mixed filter composed of an average filter and a band-pass filter and a frequency analysis. The proposed method uses the moving average filter of the FIR filter structure for noise removal and the band-pass filter of the IIR filter structure for detecting only specific frequency components after normalizing the measured current signal based on the maximum value. After performing Fast Fourier Transform (FFT) using the band-pass filtered signal, the total energy is calculated using the magnitude component of the frequency, and the arc is detected using the magnitude of the calculated energy. In order to show the validity of the proposed method, we experimented with various data and found that arc and steady state can be easily discriminated by calculating spectral energy. Therefore, it is considered that the proposed method can be applied to arc diagnosis of low voltage electric wire.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.5
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• pp.449-457
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• 2011

As automated image processing techniques have been required in multi-temporal/multi-sensor geospatial image applications, use of automated but highly invariant image matching technique has been a critical ingredient. Note that there is high possibility of geometric and spectral differences between multi-temporal/multi-sensor geospatial images due to differences in sensor, acquisition geometry, season, and weather, etc. Among many image matching techniques, the SIFT (Scale Invariant Feature Transform) is a popular method since it has been recognized to be very robust to diverse imaging conditions. Therefore, the SIFT has high potential for the geospatial image processing. This paper presents a performance test results of the SIFT on geospatial imagery by simulating various image differences such as shear, scale, rotation, intensity, noise, and spectral differences. Since a geospatial image application often requires a number of good matching points over the images, the number of matching points was analyzed with its matching positional accuracy. The test results show that the SIFT is highly invariant but could not overcome significant image differences. In addition, it guarantees no outlier-free matching such that it is highly recommended to use outlier removal techniques such as RANSAC (RANdom SAmple Consensus).

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.5
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• pp.425-433
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• 2002

After the removal of SA (Selective Availability), horizontal accuracy of 25m(2dRMS) is easily obtained using GPS (Global Positioning System). In this paper, the error characteristics without SA are analyzed and a navigation algorithm concerns this error characteristics is proposed to further improve the accuracy. The proposed method utilizes the relationship between elevation angle and errors that are remained after ionospheric and troposheric delay compensation. The relationship is derived from real measurements and used as a weighting matrix of weighted least squares estimator. Furthermore, a RAIM (Receiver Autonomous Integrity Monitoring) technique is included to remove abnormal measurements affected by multi-path or low SNR (Signal-to-Noise Ratio). It is shown that using the proposed method, more than 4 times accurate result, which is comparable with DGPS (Differential GPS), can be obtained from experiments with real data. Besides accuracy and reliability, the proposed method reduces large jumps in position and maintains better performance than a method using mask angle to completely remove satellites below this mask angle. Thus it is expected that the proposed method can be efficiently applied to land navigation where some satellites are blocked by building or forest.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.1
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• pp.1-8
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• 2010

Noise removal methods for noisy images have been studied a lot in the domain of spatial and transform filtering. Low pass filtering was initially applied in the spatial domain. Recently, discrete wavelet transform has widely used for image denoising as well as image compression due to an excellent energy compaction and a property of multiresolution. In this paper, Gabor cosine and sine transform which is considered as human visual filter is applied to image denoising areas using soft thresholding technique. GCST is compared with excellent wavelet transform which uses existing soft thresholding methods from PSNR point of view. Resultant images removed noises are also visually compared. Experimental results with adding four different standard deviation levels of Gaussian distributed noises to real images show that the proposed transform has better PSNR performance of a maximum of 1.18 dB and visible perception than wavelet transform.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5419-5435
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• 2017

The construction of panoramic images on smartphones and low-powered devices is a challenging task. In this paper, we propose a new approach for smoothly stitching images on mobile phones in the presence of moving objects in the scene. Our main contributions include handling moving object problems, reducing processing time, and generating rectangular panoramic images. First, unique and robust feature points are extracted using fast ORB method and a feature matching technique is applied to match the extracted feature points. After obtaining good matched feature points, we employ the non-deterministic RANSAC algorithm to discard wrong matches, and the hommography transformation matrix parameters are estimated with the algorithm. Afterward, we determine precise overlap regions of neighboring images and calculate their absolute differences. Then, thresholding operation and noise removal filtering are applied to create a mask of possible moving object regions. Sequentially, an optimal seam is estimated using dynamic programming algorithm, and a combination of linear blending with the mask information is applied to avoid seam transition and ghosting artifacts. Finally, image-cropping operation is utilized to obtain a rectangular boundary image from the stitched image. Experiments demonstrate that our method is able to produce panoramic images quickly despite the existence of moving objects.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.10 no.6
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• pp.889-899
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• 1999

A technique to obtain an exact frequency characteristics of desired targets in radar measurements is presented. The pulsing network composed of two RF switches was installed between the Network Analyzer and the antenna, and the backscattering from a metal sphere was measured at X-band. It is shown that the pulsing effectively eliminated undesired returns from antenna and other circuitry of the systems. The antenna return was suppressed by more than 60 dB, and the signal-to-noise ratio was improved drastically. The pulsed frequency data were processed to extract the responses of the desired target. The result agrees well with the theoretical backscattering characteristics of the sphere. The methods presented here are applicable to RCS measurements in compact ranges, and also to the backscattering measurements of distributed targets outdoors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.1
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• pp.137-143
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• 2017

X-ray image is a widely used to medical examination, airport security and cargo inspection. However, X-ray images contain many visual noise, which interrupt image analysis. Consequently, it is primary importance to reduce noises of X-ray image. In this paper, we present a improved denoise technique for x-ray image using pixel value and range weights. First, we denoise a x-ray image using bilateral filter. Next, we detect a edge region of the original x-ray image. If a denoised pixel belongs to the edge region, we calculate weighting values of original x-ray image and denoised x-ray image in $3{\times}3$ neighboring pixels and compute the cost value to determine the boundary pixel value. Finally, the pixel value having minimum cost is determined as the pixel value of the denoised x-ray image. Simulation results show that the proposed algorithm achieves good performance in terns of PSNR comparison and subjective visual quality.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5D
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• pp.635-644
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• 2009

Image processing technique in the outdoor environment is very sensitive, and it tends to lose a lot of accuracy when it rapidly changes by outdoor environment. Therefore, in order to calculate accurate traffic information using the traffic monitoring system, we must resolve removing shadow in transition time, Distortion by the vehicle headlights at night, noise of rain, snow, and fog, and occlusion. In the research, we developed a system to calibrate the amount of traffic, speed, and time occupancy by using image processing technique in a variety of outdoor environments change. This system were tested under outdoor environments at the Gonjiam test site, which is managed by Korea Institute of Construction Technology (www.kict.re.kr) for testing performance. We evaluated the performance of traffic information, volume counts, speed, and occupancy time, with 4 lanes (2 lanes are upstream and the rests are downstream) from the 16th to 18th December, 2008. The evaluation method performed as based on the standard data is a radar detection compared to calculated data using image processing technique. The System evaluation results showed that the amount of traffic, speed, and time occupancy in period (day, night, sunrise, sunset) are approximately 92-97% accuracy when these data compared to the standard data.