• Journal of Biomedical Engineering Research
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• v.27 no.5
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• pp.291-299
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• 2006

A Computer-Aided Diagnosis system has been examined to reduce the effort of radiologist. In this paper, we propose the algorithm using Support Vector Machine(SVM) classifier to discriminate whether microcalcifications are malignant or benign tumors. The proposed method to detect microcalcifications is composed of two detection steps each of which uses SVM classifier. The coarse detection step finds out pixels considered high contrasts comparing with neighboring pixels. Then, Region of Interest(ROI) is generated based on microcalcification characteristics. The fine detection step determines whether the found ROIs are microcalcifications or not by merging potential regions using obtained ROIs and SVM classifier. The proposed method is specified on Korean mammogram database. The experimental result of the proposed algorithm presents robustness in detecting microcalcifications than the previous method using Artificial Neural Network as classifier even when using small training data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.217-219
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• 2019

In response to the increased use of digital video device, more researches are actively made on the image processing technologies. Image processing is practically used on various applied fields such as medical photographic interpretation, and object recognition. The types of image noise include Gaussian Noise, Impulse Noise, and Salt and Pepper. Noise refers to the unnecessary information which damages the video and the noise is mainly removed by a filter. Typical noise removal methods are Median Filter and Average Filter. While Median Filter is effective for removing Salt and Pepper noise, the noise removal performance is relatively lower in the environment with high noise density. To address such issue, this study suggested an algorithm which utilizes neighboring pixels to remove noise.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.520-524
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• 2009

In compressed color images, colors are usually represented by luminance and chrominance (YCbCr) components. Considering characteristics of human vision system, chrominance (CbCr) components are generally represented more coarsely than luminance component. Aiming at possible recovery of chrominance components, we propose a model-based chrominance estimation algorithm where color images are modeled by a Markov random field (MRF). A simple MRF model is here used whose local conditional probability density function (pdf) for a color vector of a pixel is a Gaussian pdf depending on color vectors of its neighboring pixels. Chrominance components of a pixel are estimated by maximizing the conditional pdf given its luminance component and its neighboring color vectors. Experimental results show that the proposed chrominance estimation algorithm is effective for quality improvement of compressed color images such as JPEG and JPEG2000.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.321-326
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• 2019

Image denoising methods based on block matching are founded on the experimental observations that neighboring patches or blocks in images retain similar features with each other, and have been proved to show superior performance in denoising different kinds of noise. The methods, however, take into account only neighboring blocks in searching for similar blocks, and ignore the characteristic features of the reference block itself. Consequently, denoising performance is negatively affected when outliers of the Gaussian distribution are included in the reference block which is to be denoised. In this paper, we propose an expanded block matching method in which noisy images are first decomposed into a number of bit-planes, then the range of true signals are estimated based on the distribution of pixels on the bit-planes, and finally outliers are replaced by the neighboring pixels belonging to the estimated range. In this way, the advantages of the conventional Gaussian filter can be added to the blocking matching method. We tested the proposed method through extensive experiments with well known test-bed images, and observed that performance gain can be achieved by the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.10C
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• pp.787-797
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• 2008

In this paper, we propose a fast disparity estimation method considering temporal and spatial redundancy based on a dynamic programming for stereo matching. For the first step, the dynamic programming is performed to estimate disparity vectors with correlation between neighboring pixels in an image. Next, we efficiently compensate regions, which disparity vectors are not allocated, with neighboring disparity vectors assuming that disparity vectors in same object are quite similar. Moreover, in case of video sequence, we can decrease a complexity with temporal redundancy between neighboring frames. For performance comparison, we generate an intermediate-view image using the estimated disparity vector. Test results show that the proposed algorithm gives $0.8{\sim}2.4dB$-increased PSNR(peak signal to noise ratio) compared to a conventional block matching algorithm, and the proposed algorithm also gives approximately 0.1dB-increased PSNR and $48{\sim}68%$-lower complexity compared to the disparity estimation method based on general dynamic programming.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.1C
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• pp.37-47
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• 2009

When H.264 encoded video streams are transmitted over wireless network, packet loss is unavoidable. Responding on this environment, we propose methods to recover missed motion vector in the decoder: At first, A candidate vector set for missing macroblock is estimated from high correlation coefficient of neighboring motion vectors and missing block vectors the algorithm clusters candidate vectors through distances amongst motion vectors of neighboring blocks. Then the optimal candidate vector is determined by the median value of the clustered motion vector set. In next stage, from the candidate vector set, the final candidate vector of missing block is determined it has minimum distortion value considering directions of neighboring pixels' boundary. Test results showed that the proposed algorithm decreases the candidate motion vectors $23{\sim}61%$ and reduces $3{\sim}4sec$ on average processing(decoding) time comparing the existing H.264 codec. The PSNR, in terms of visual quality is similar to existing methods.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.4
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• pp.237-243
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• 2007

In this paper, we propose the Real-time Dead-Pixel Detection and Compensation System for mobile camera and its hardware architecture. The CMOS image sensors as image input devices are becoming popular due to the demand for miniaturized, low-power and cost-effective imaging systems. However a conventional Dead-Pixel Detection Algorithm is disable to detect neighboring dead pixels and it degrades image quality by wrong detection and compensation. To detect dead pixels the proposed system is classifying dead pixels into Hot pixel and Cold pixel. Also, the proposed algorithm is processing line-detector and $5{\times}5$ window-detector consecutively. The line-detector and window-detector can search dead pixels by using one-dimensional(only horizontal) method in low frequency area and two-dimensional(vertical and diagonal) method in high frequency area, respectively. The experimental result shows that it can detect 99% of dead pixels. It was designed in Verilog hardware description language and total gate count is 23K using TSMC 0.25um ASIC library.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.819-822
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• 2004

To transmit MPEG-2 video on an erroneous channel, a number of error control techniques He needed. Especially, error concealment techniques which can be implemented on receivers independent of transmitters are essential to obtain good video quality. In this paper, a motion adaptive boundary matching algorithm (MA-BMA) is presented for temporal error concealment. Before carrying out BMA, we perform error concealmmt by a motion vector prediction using neighboring motion vectors. If the candidate of error concealment is rot satisfied, search range and reliable boundary pixels are selected by the motion activity or motion vectors ane a damaged macroblock is concealed by applying the MA-BMA. This error concealment technique reduces the complexity and maintains PSNR gain of 0.3 0.7dB compared to the conventional BMA.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.22 no.3
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• pp.684-687
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• 1998

The biggest problem common on to all forms of Phase Shift Interferometer is unwrapping the phase. Simple phase unwrapping algorithms assume that every pixel is within radians of its neighbors. If this is true, any reasonable algorithm will return the correct unwrapped phase. If not, correct unwrapped phase will not be obtained. In rough surface, frequently, neighboring pixels have phase steps greater than. This paper proposes the new method which makes phase steps smaller than by sub-pixel movement.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.476-479
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• 2003

The Rough transform (HT) is often used for extracting global features in binary images, for example curve and line segments, from local features such as single pixels. The HT is useful due to its insensitivity to missing edge points and occlusions, and robustness in noisy images. However, it possesses some disadvantages, such as time and memory consumption due to the number of input data and the selection of an optimal and efficient resolution of the accumulator space can be difficult. Another problem of the HT is in the difficulty of peak detection due to the discrete nature of the image space and the round off in estimation. In order to resolve the problem mentioned above, a possibilistic C-means approach to clustering [1] is used to cluster neighboring peaks. Several experimental results are given.