• The KIPS Transactions:PartB
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• v.11B no.7 s.96
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• pp.779-784
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• 2004

In this paper, we have proposed an image contrast control method using brightness preserving on the FPD(Flat Panel Display). The proposed method can be easily applied to the FPD required real-time processing, since hardware complexity is greatly reduced using linear approximation method of CDF(Cumulative Density Function). For effective processing of the proposed algorithm, we have utilized the sample value of CDF and Barrel Shift. Visual test and standard deviation of their histogram have been introduced to evaluate the resultant output images of the pro-posed method and the original ones.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.176-183
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• 1995

Mean opinion score (MOS) method has been used in many areas to quantify opinions of respondents not only in survey research but in evaluating the parameters of population that are not measurable of are technically hard to be measured. Histogram is an important graphical technique because of the role it plays in describing categorical data as well as quantitative. In MOS method, subjective opinions of respondents are quantified by opinion scores and the arithmetic means of opinion scores have been used to describe the interesting population. Since opinion scores are polytomous, the values of arithmetic means have little meanings. In this paper, cumulative percentage curves as a function of the means of opinion scores are derived by combining means of opinion scores and histograms. It is proposed for better interpretation to opinion scores in MOS method, one of subjective evaluation methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.653-656
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• 2006

본 논문은 Cumulative Distribution Function(CDF) 부합에 의한 영상 개선 방법에 대해서 제안하였다. 제안한 방법은 원본 영상의 히스토그램 분포도를 조사하여 히스토그램 그래프상의 특정 색도값들을 선정, 이 점들을 보간법을 이용하여 히스토그램을 재 작성한다. 이를 이용하여 원본 CDF 그래프를 크게 벋어나지 않고, 즉 밝기 정보가 크게 훼손 되지 않은 상태로 색도 값을 재 배치 함으로써 히스토그램 평활화와 스트레칭 효과를 모두 만족하는 영상 향상의 결과를 얻을 수 있다.

• Journal of Korea Multimedia Society
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• v.8 no.9
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• pp.1187-1194
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• 2005

In this paper, new contrast enhancement algorithms that use temporal decimation method and approximated CDF(Cumulative Distribution Function) are proposed. They reduce the amount of computation which is required for image contrast enhancement. Simulation results show that the algorithms can achieve significant reduction in the computational cost and the hardware complexity. Visual test and standard deviation of their histogram have been introduced to evaluate the resultant output images of the proposed method and the original ones.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.317-320
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• 2008

Between traffic-induced vibration of a bridge and fatigue damage of its attached structures are very closely related. But any evaluation and design method considering the fatigue damage is not established yet. As an experimental method of evaluation of the fatigue durability, a method based on cumulative damage using a stress range histogram has been often used. However, to use the method, the fatigue durability of unmeasured points could not be evaluated. Then, in this paper, dynamic analysis of a sign frame on a bridge is carried out based on the vibration data of the bridge. And model optimization was performed for good agreement between measured responses and computed responses. As a result, we could get stress range histograms and calculate fatigue durability of unmeasured points.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.3 s.309
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• pp.17-24
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• 2006

In this paper, an algorithm for anchor frame detection in news video is proposed, which consists of four steps. In the first step, the cumulative histogram method is used to detect shot boundaries in order to segment a news video into video shots. In the second step, skin color information is used to detect face regions in each shot boundary. In the third step, color information of upper body regions is used to extract anchor object, which produces candidate anchor frames. Then, from the candidate anchor frames, a graph-theoretic cluster analysis algorithm is utilized to classify the news video into anchor-person frames and non-anchor frames. Experiment results have shown the effectiveness of the proposed algorithm.

• The Journal of Korean Association of Computer Education
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• v.8 no.1
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• pp.81-91
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• 2005

In this paper we present a novel approach for image retrieval using color and edge information. We take into account the HSI(Hue, Saturation and Intensity) color space instead of RGB space, which emphasizes more on visual perception. In our system colors in an image are clustered into a small number of representative colors. The color feature descriptor consists of the representative colors and their percentages in the image. An improved cumulative color histogram distance measure is defined for this descriptor. And also, we have developed an efficient edge detection technique as an optional feature to our retrieval system in order to surmount the weakness of color feature. During the query processing, both the features (color, edge information) could be integrated for image retrieval as well as a standalone entity, by specifying it in a certain proportion. The content-based retrieval system is tested to be effective in terms of retrieval and scalability through experimental results and precision-recall analysis.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.1
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• pp.51-57
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• 2005

In this paper, we propose an adaptive contrast control method for the flat real-time processing. The proposed method has employed probability density function(PDF) in order to control a sudden change in image-brightness. In addition, the proposed algerian obtains the maximum contrast without affecting the processed image. In order to reduce hardware complexity, we have utilized approximated CDF based on sampling values. Visual test and standard deviation of their histogram have been introduced to evaluate the resultant output images of at: proposed method and the original ones.

• International Journal of Automotive Technology
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• v.6 no.1
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• pp.53-63
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• 2005

We propose a fuzzy neural network (FNN) theory capable of deciding the quality of a road image prior to extracting lane-related information. The accuracy of lane-related information obtained by image processing depends on the quality of the raw images, which can be classified as good or bad according to how visible the lane marks on the images are. Enhancing the accuracy of the information by an image-processing algorithm is limited due to noise corruption which makes image processing difficult. The FNN, on the other hand, decides whether road images are good or bad with respect to the degree of noise corruption. A cumulative distribution function (CDF), a function of edge histogram, is utilized to extract input parameters from the FNN according to the fact that the shape of the CDF is deeply correlated to the road image quality. A suitability analysis shows that this deep correlation exists between the parameters and the image quality. The input pattern vector of the FNN consists of nine parameters in which eight parameters are from the CDF and one is from the intensity distribution of raw images. Experimental results showed that the proposed FNN system was quite successful. We carried out simulations with real images taken in various lighting and weather conditions, and obtained successful decision-making about $99\%$ of the time.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.6
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• pp.468-476
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• 2002

The confidence of information from image processing depends on the original image quality. Enhancing the confidence by an algorithm has an essential limitation. Especially, road images are exposed to lots of noisy sources, which makes image processing difficult. We, in this paper, propose a FNN (fuzzy-neural network) capable oi deciding the quality of a road image prior to extracting lane-related information. According to the decision by the FNN, road images are classified into good or bad to extract lane-related information. A CDF (cumulative distribution function), a function of edge histogram, is utilized to construct input parameters of the FNN, it is based on the fact that the shape of the CDF and the image quality has large correlation. Input pattern vector to the FNN consists of ten parameters in which nine parameters are from the CDF and the other one is from intensity distribution of raw image. Correlation analysis shows that each parameter represents the image quality well. According to the experimental results, the proposed FNN system was quite successful. We carried out simulations with real images taken by various lighting and weather conditions and achieved about 99% successful decision-making.