• Journal of Biomedical Engineering Research
• /
• v.16 no.4
• /
• pp.425-430
• /
• 1995

The correlation technique has been widely used in ctRl data processing. The proposed CLT (clus- tering threshold) technique is a modified CCT (correlation coefficient threshold) technique and has many advantages compared with the conventional CCT technique. The CLT technique is explained by the following two steps. First, once the correlation coefficient map above the proper TH value is obtained using the CCT technique which is discrete and includes splash noise data, then the spurious pixels are rejected and the real neural activity pixels extracted using an nxn matrix box. Second, a clustering operation is performed by the two correction rules. The real neuronal activated pixels can be clustered and the false spurious pixels can be suppressed by the proposed CLT technique. The proposed CLT technique used in the post processing in ctRl has advantages over other existing techniques. It is especially proved to be robust in noisy environment.

• Journal of Digital Convergence
• /
• v.14 no.8
• /
• pp.233-243
• /
• 2016

Due to speech recognition problems in noisy environment, Audio Visual Speech Recognition (AVSR) system, which combines speech information and visual information, has been proposed since the mid-1990s,. and lip reading have played significant role in the AVSR System. This study aims to enhance recognition rate of utterance word using only lip shape detection for efficient AVSR system. After preprocessing for lip region detection, Convolution Neural Network (CNN) techniques are applied for utterance period detection and lip shape feature vector extraction, and Hidden Markov Models (HMMs) are then used for the recognition. As a result, the utterance period detection results show 91% of success rates, which are higher performance than general threshold methods. In the lip reading recognition, while user-dependent experiment records 88.5%, user-independent experiment shows 80.2% of recognition rates, which are improved results compared to the previous studies.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.37 no.3
• /
• pp.98-109
• /
• 2000

While the bispectrum has desirable properties in itself and therefore has a lot of potential to be applied to signal and Image restoration. few real-world application results have appeared in literature The major problem with this IS the difficulty In realizing the expectation operator of the true bispectrum, due to the lack of realizations. In this paper, the true bispectrum is defined as the expectation of the sample bispectrum, which IS the Fourier representation of the triple correlation given one realization The characteristics of the sample bispectrum are analyzed and a way to obtain an estimate of the true bispectrum without stochastic expectation, using the generalized theory of weighted regularization is shown. The bispectrum estimated by the proposed algorithm is experimentally demonstrated to be useful for signal recovery under blurred noisy condition.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.54 no.2
• /
• pp.99-105
• /
• 2017

Generally, stereo matching methods are used to estimate depth information based on color and spatial similarity. However, most depth estimation methods suffer from the occlusion region because occlusion regions cause inaccurate depth information. Moreover, they do not consider the temporal dimension when estimating the disparity. In this paper, we propose a temporal stereo matching method, considering occlusion and disregarding inaccurate temporal depth information. First, we apply a global stereo matching algorithm to estimate the depth information, we segment the image to occlusion and non-occlusion regions. After occlusion detection, we fill the occluded region with a reasonable disparity value that are obtained from neighboring pixels of the current pixel. Then, we apply a temporal disparity estimation method using the reliable information. Experimental results show that our method detects more accurate occlusion regions, compared to a conventional method. The proposed method increases the temporal consistency of estimated disparity maps and outperforms per-frame methods in noisy images.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.28 no.1
• /
• pp.85-93
• /
• 2018

Biometric user authentications, day after day, propagate more to human life instead of traditional systems which use passwords and ID cards. However, most of these systems have many problems for given biometric information such noisy data, low-quality data, a limitation of recognition rate, and so on. To deal with these problems, I used Dual QML which is non-linear classification for classifying correctly the real-world data and then proposed preprocessing method for increasing recognition rate and performance by segmenting a specific region on an image. The previous published Dual QML used face, palmprint, ear for the experiment. In this paper, I used iris for experiment and then proved excellence of Dual QML at iris recognition. Finally I demonstrated these results (e.g. increasing recognition rate and performance, suitability for iris recognition) through experiments.

• Korean Journal of Remote Sensing
• /
• v.23 no.4
• /
• pp.273-285
• /
• 2007

Most stereo matching methods use intensity values in small image patches to measure the correspondence between two points. If the noisy pixels are used in computing the corresponding point, the matching performance becomes low. For this reason, the noise plays a critical role in determining the matching performance. In this paper, we propose a method for combining intensity and edge filters robust to the noise in order to improve the performance of stereo matching using high resolution satellite imagery. We used intensity filters such as Mean, Median, Midpoint and Gaussian filter and edge filters such as Gradient, Roberts, Prewitt, Sobel and Laplacian filter. To evaluate the performance of intensity and edge filters, experiments were carried out on both synthetic images and satellite images with uniform or gaussian noise. Then each filter was ranked based on its performance. Among the intensity and edge filters, Median and Sobel filter showed best performance while Midpoint and Laplacian filter showed worst result. We used Ikonos satellite stereo imagery in the experiments and the matching method using Median and Sobel filter showed better matching results than other filter combinations.

• The Journal of Korean Institute of Information Technology
• /
• v.17 no.9
• /
• pp.99-112
• /
• 2019

Cracks on pavement surfaces are critical signs and symptoms of the degradation of pavement structures. Image-based pavement crack detection is a challenging problem due to the intensity inhomogeneity, topology complexity, low contrast, and noisy texture background. In this paper, we address the problem of pavement crack detection and segmentation at pixel-level based on a Deep Neural Network (DNN) using gray-scale images. We propose a novel DNN architecture which contains a modified U-net network and a high-level features network. An important contribution of this work is the combination of these networks afforded through the fusion layer. To the best of our knowledge, this is the first paper introducing this combination for pavement crack segmentation and detection problem. The system performance of crack detection and segmentation is enhanced dramatically by using our novel architecture. We thoroughly implement and evaluate our proposed system on two open data sets: the Crack Forest Dataset (CFD) and the AigleRN dataset. Experimental results demonstrate that our system outperforms eight state-of-the-art methods on the same data sets.

• The Korean Journal of Petroleum Geology
• /
• v.11 no.1 s.12
• /
• pp.9-17
• /
• 2005

In the main target area of the block II, Targe-scale faults occur below the unconformity developed around 1 km in depth. The contrast of seismic velocity around the unconformity is generally so large that the strong multiples and the radical velocity variation would deteriorate the quality of migrated section due to serious distortion. More than 15 kinds of data processing techniques have been applied to improve the image resolution for the structures farmed from this active crustal activity. The bad and noisy traces were edited on the common shot gathers in the first step to get rid of acquisition problems which could take place from unfavorable conditions such as climatic change during data acquisition. Correction of amplitude attenuation caused from spherical divergence and inelastic attenuation has been also applied. Mild F/K filter was used to attenuate coherent noise such as guided waves and side scatters. Predictive deconvolution has been applied before stacking to remove peg-leg multiples and water reverberations. The velocity analysis process was conducted at every 2 km interval to analyze migration velocity, and it was iterated to get the high fidelity image. The strum noise caused from streamer was completely removed by applying predictive deconvolution in time space and ${\tau}-P$ domain. Residual multiples caused from thin layer or water bottom were eliminated through parabolic radon transform demultiple process. The migration using curved ray Kirchhoff-style algorithm has been applied to stack data. The velocity obtained after several iteration approach for MVA (migration velocity analysis) was used instead or DMO for the migration velocity. Using various testing methods, optimum seismic processing parameter can be obtained for structural and stratigraphic interpretation in the Block II, Yellow Sea Basin.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.5
• /
• pp.518-523
• /
• 2017

This paper proposes a novel denoising method based on non-local(NL) means. The NL-means algorithm is effective for removing an additive Gaussian noise, but the denoising parameter should be controlled depending on the noise level for proper noise elimination. Therefore, the proposed method optimizes the denoising parameter according to the noise levels. The proposed method consists of two processes: off-line and on-line. In the off-line process, the relations between the noise level and the denoising parameter of the NL-means filter are analyzed. For a given noise level, the various denoising parameters are applied to the NL-means algorithm, and then the qualities of resulting images are quantified using a structural similarity index(SSIM). The parameter with the highest SSIM is chosen as the optimal denoising parameter for the given noise level. In the on-line process, we estimate the noise level for a given noisy image and select the optimal denoising parameter according to the estimated noise level. Finally, NL-means filtering is performed using the selected denoising parameter. As shown in the experimental results, the proposed method accurately estimated the noise level and effectively eliminated noise for various noise levels. The accuracy of noise estimation is 90.0% and the highest Peak Signal-to-noise ratio(PSNR), SSIM value.

• Korean Journal of Remote Sensing
• /
• v.28 no.2
• /
• pp.181-189
• /
• 2012

Recently, interest in potential for estimating water quality using high resolution satellite images is increasing. However, low SNR(Signal to Noise Ratio) over inland water and radiometric errors such as non-linearity of brightness value of high resolution satellite images often lead to accuracy degradation in water quality estimation. Therefore radiometric correction should be carried out to estimate water quality for high resolution satellite images. For KOMPSAT-2 images parameters for brightness value-radiance conversion are not available and precise radiometric correction is difficult. To exploit KOMPSAT-2 images for water quality monitoring, it is necessary to investigate non-linearity of brightness value and noise over inland water. In this paper, we performed brightness value comparison between KOMPSAT-2 images and IKONOS/GeoEye-1, which are known to show the linearity. We used the images obtained over the same area and on the same date for comparison. As a result, we showed that although KOMPSAT-2 images are more noisy;the trend of brightness value and pattern of noise are almost similar to reference images. The results showed that appropriate target area to minimize the impact of noise was $5{\times}5$. Non-linearity of brightness value between KOMPSAT-2 and reference images was not observed. Therefore we could conclude that KOMPSAT-2 may be used for estimation of water quality parameters such as concentration of chlorophyll.