• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.25-33
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• 2010

We propose a retouching method that converts a general photography to a watercolor painting image using bilateral filtering and mean shift segmentation which are mostly used in image processing. The first step is to weaken high frequency components of the image, while preserving the edge of image using the bilateral filtering. And after that we perform DoG(Difference of Gradient) edge extraction and mean shift segmentation respectively from the bilateral filtered image. The DoG edge extraction is performed using luminance component of the image whose RGB color space is transformed into CIELAB space. Experimental result shows that our method can be applied to various types of image and bring better result, especially against the photo taken in daylight.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.11
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• pp.1714-1725
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• 1993

This paper analyzes the properties of such algorithm that corresponds to the nonlinear adaptive algorithm with additional update terms, parameterized by the scalar factors a1, a2, a3 and Presents its structure. The analysis of convergence leads to eigenvalues of the transition matrix for the mean weight vector. Regions in which the algorithm becomes stable are demonstrated. The time constant is derived and the computational complexities of MLMS algorithms are compared with those of the conventional LMS, sign, LFG, and QFG algorithms. The properties of convergence in the mean square are analyzed and the expressions of the mean square recursion and the excess mean square error are derived. The necessary condition for the CFG algorithm to be stable is attained. In the computer simulation applied to the system identification the CFG algorithm has the more computation complexities but the faster convergence speed than LMS, LFG and QFG algorithms.

• The Journal of the Acoustical Society of Korea
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• v.18 no.7
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• pp.45-55
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• 1999

In this paper, we used the WLMS(Wavelet domain Least Mean Square) adaptive filter based on the wavelet transform to cancel grain noise. Usually, grain noise occurs in changes of the crystalline structure of metals in high temperature environment. It makes the detection of flaw difficult. The WLMS adaptive filtering algorithm establishes the faster convergence rate by orthogonalizaing the input vector of adaptive filter as compared with that of LMS adaptive filtering algorithm in time domain. We implemented the WLMS adaptive filter by using the delayed version of the primary input vector as the reference input vector and then implemented the CA-CFAR(Cell Averaging- Constant False Alarm Rate) threshold estimator. CA-CFAR threshold estimator enables to detect the flaw and back echo signals automatically. Here, we used the output signals of adaptive filter as its input signal. To Cow the statistical characteristic of ultrasonic signals corrupted by grain noise, we performed run test. The results showed that ultrasonic signals are nonstationary signal, that is, signals whose statistical properties vary with time. The performance of each filter is appreciated by the signal-to-noise ratio. After LMS adaptive filtering in time domain, SNR improves to about 2-3㏈ but after WLMS adaptive filtering in wavelet domain, SNR improves to about 4-6㏈.

• The Journal of the Acoustical Society of Korea
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• v.21 no.1
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• pp.42-46
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• 2002

In this paper, a new pre-processing algorithm is proposed to improve the performance of stereo acoustic echo canceller. The proposed algorithm has the improved performance by the estimation error reduction of filter coefficient using input signal which was reduced reverberation of room in the basis MINT (Mu1tip1e-input/output Inverse Theorem) filtering. For real stereo speech signal and real room impulse response the results of simulation, we showed that the proposed method could improved 3∼5 dB ERLE (Echo Return Loss Enhancement) regardless of NLMS (Normalized Least Mean Square) and Projection adaptive algorithm.

• The Journal of the Acoustical Society of Korea
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• v.15 no.4
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• pp.70-76
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• 1996

In the case of an image corrupted with mixed noise, conventional MMSE filter can not remove such a mixed noise properly, because the impulse moise cause a certain bias of the minimum mean-square error estimate at regions close to outliers. In this paper, we proposed the new method or removal of mixed noise by combining MMSE filtering structure with local multi-windowing method according to directions and with ranked-order method. As a result, the improvement of the image quality with the proposed was obtained between about 9.7 and 35.2 times in the sense of NMSE(normalized mean square errors) evaluation than that of MMSE filter. Also, we could obtain the enhanced image in the mixed noisy image from visual and quantitative aspect.

• Information Systems Review
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• v.9 no.1
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• pp.85-103
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• 2007

The purpose of this study is to evaluate the performance of collaborative filtering recommender algorithms for better prediction accuracy of the customer's preference. The accuracy of customer's preference prediction is compared through the MAE of neighborhood based collaborative filtering algorithm and correspondence mean algorithm. It is analyzed by using MovieLens 1 Million dataset in order to experiment with the prediction accuracy of the algorithms. For similarity, weight used in both algorithms, commonly, Pearson's correlation coefficient and vector similarity which are used generally were utilized, and as a result of analysis, we show that the accuracy of the customer's preference prediction of correspondence mean algorithm is superior. Pearson's correlation coefficient and vector similarity used in two algorithms are calculated using the preference rating of two customers' co-rated movies, and it shows that similarity weight is overestimated, where the number of co-rated movies is small. Therefore, it is intended to increase the accuracy of customer's preference prediction through expanding the number of the existing co-rated movies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.661-663
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• 2016

Corruption occurs in the process of processing image signal and the corruption changes the pixel value within the image to damage the original information. AWGN(additive white Gaussian noise) is a representative example. For filters to remove AWGN, there are filters such as MF(mean filter), WF(wiener filter), and AWMF(adaptive weighted mean filter). However images processed through standard previous filters lock preservation characteristics in edge areas. Therefore, threshold value is applied for processing on the standard deviation of the local mask in this study and if the standard deviation is smaller than the threshold value, it is not filtered and if the value is bigger than the threshold value, the study suggested an algorithm that processes using weighted value utilizing standard deviation.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.45-54
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• 2009

In this paper, we propose an adult images detection algorithm using a mean intensity filter and an improved 2D Hough Transform. This paper is composed of three major steps including a training step, a recognition step, and a verification step. The training step generates a mean nipple variance filter that will be used for detecting nipple candidate regions in the recognition step. To make the mean variance filter, we converts an input color image into a gray scale image and normalize it, and make an average intensity filter for nipple areas. The recognition step first extracts edge images and finds connected components, and decides nipple candidate regions by considering the ratio of width and height of a connected component. It then decides final nipple candidates by calculating the similarity between the learned nipple average intensity filter and the nipple candidate areas. Also, it detects breast lines of an input image through the improved 2D Hough transform. The verification step detects breast areas and identifies adult images by considering the relations between nipple candidate regions and locations of breast lines.

• Journal of Astronomy and Space Sciences
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• v.15 no.1
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• pp.175-182
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• 1998

Any classification process using SAR images presupposes the reduction of multiplicative speckle noise, since the variations caused by speckle make it extremely difficult to distinguish between neighboring classes within the feature space. Therefore, several adaptive filter algorithms have been developed in order to distinguish between them. These algorithms aim at the preservation of edges and single scattering peaks, and smooths homogeneous areas as much as possible. This task is rendered more difficult by the multiplicative nature of the speckle noise the signal variation depends on the signal itself. In this paper, LEE(Lee 1908) and R-LEE(Lee 1981) filters using local statistics, local mean and variance, are applied to RADARSAT SAR images. Also, a new method of speckle filtering, EPOS(Edge Preserving Optimal Speckle)(Hagg & Sties 1994) filter based on the statistical properties of speckle noise is described and applied. And then, the results of filtering SAR images with LEE, R-LEE and EPOS filters are compared with mean and median filters.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.1
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• pp.33-38
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• 2013

In this paper, we propose an algorithm to track multi-small targets in infrared image sequences in case of dissipation or creation of targets by using the background estimation filter, Kahnan filter and mean shift algorithm. We detect target candidates in a still image by subtracting an original image from an background estimation image, and we track multi-targets by using Kahnan filter and target selection. At last, we adjust specific position of targets by using mean shift algorithm In the experiments, we compare the performance of each background estimation filters, and verified that proposed algorithm exhibits better performance compared to classic methods.