• Journal of Korea Multimedia Society
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• v.8 no.8
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• pp.1069-1079
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• 2005

Though there have been many methods to detect features in spatial domain, in the case of a compressed image it has to be decoded, processed and encoded again. Alternatively, we can manipulate a compressed image directly in the Discrete Cosine Transform (DCT) domain that has been used for compressing videos or images in the standards like MPEG and JPEG. In our previous work we proposed a model-based discontinuity evaluation technique in the DCT domain that had problems in the rotated or non-ideal discontinuities. In this paper, we propose a fuzzy filtering technique that consists of height fuzzification, direction fuzzification, and forty filtering of discontinuities. The enhancement achieved by the fuzzy tittering includes the linking, thinning, and smoothing of discontinuities in the DCT domain. Although the detected discontinuities are rough in a low-resolution image for the size (8${\times}$8 pixels) of the DCT block, experimental results show that this technique is fast and stable to enhance the qualify of discontinuities.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2924-2932
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• 2015

Image binarization is a process to divide the image into objects and backgrounds, widely applied to the fields of image analysis and its recognition. In the existing method of binarization, there is some uncertainty when there is insufficient brightness gap between objects and backgrounds in setting threshold. The method of fuzzy binarization has improved the features of objects efficiently. However, since this method sets ${\alpha}$-cut value statically, there remain some problems that important features of objects can be lost during binarization. Therefore, in this paper, we propose a binarization method which does not set ${\alpha}$-cut value statically. The proposed method uses fuzzy membership functions calculated by thresholds of mean, iterative, and Otsu binarization. Experiment results show the proposed method binaries various images with less loss than the existing methods.

• Journal of Korea Multimedia Society
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• v.18 no.7
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• pp.806-817
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• 2015

The contrast limited adaptive histogram equalization(CLAHE) is an advanced method for the histogram equalization which is a common contrast enhancement technique. The CLAHE divides the image into sections, and applies the contrast limited histogram equalization for each section. X-ray images can be classified into three areas: skin, bone, and air area. In clinical application, the interest area is limited to the skin or bone area depending on the diagnosis region. The CLAHE could deteriorate X-ray image quality because the CLAHE enhances the area which doesn't need to be enhanced. In this paper, we propose a new method which automatically determines the clip limit of CLAHE's parameter to improve X-ray image quality using fuzzy logic. We introduce fuzzy logic which is possible to determine clip limit proportional to the interest of users. Experimental results show that the proposed method improve images according to the user's preference by focusing on the subject.

• Journal of IKEEE
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• v.3 no.2 s.5
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• pp.168-177
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• 1999

In this paper, we present a new interpolation scheme for image enhancement using fuzzy inference. In general, interpolation techniques are based on linear operators which are essentially lowpass filters, hence, they tend to blur fine details in the original image. In our approach, the operator itself balances the strength of its sharpening and noise suppressing components according to the Properties of the input image data.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.628-631
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• 1996

This paper proposes a surface recognition algorithm which determines the types of contact surfaces by fusing the information collected by the multisensor system, consisted of the optical tactile and force/torque sensors. Since the image shape measured by the optical tactile sensor system, which is used for determining the surface type, varies depending on the forces provided at the measuring moment, the force information measured by the f/t sensor takes an important role. In this paper, an image contour is represented by the long and short axes and they are fuzzified individually by the membership function formulated by observing the variation of the lengths of the long and short axes depending on the provided force. The fuzzified values of the long and short axes are fused using the average Minkowski's distance. Compared to the case where only the contour information is used, the proposed algorithm has shown about 14% of enhancement in the recognition ratio. Especially, when imposing the optimal force determined by the experiments, the recognition ratio has been measured over 91%.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.3
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• pp.55-60
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• 2001

In this paper a new algorithm is introduced to enhance the fingerprint image effectively using fuzzy logic. Our approach is not constrained to a particular image size and scale. It rather handle different conditions of noise and image scale. Ridges are thinned while the image is enhanced along the same orientation as the ridges. The problem of false minutia can be also resolved.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.3
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• pp.172-179
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• 2015

Because of the development of computers and high-technology applications, all devices that we use have become more intelligent. In recent years, security and surveillance systems have become more complicated as well. Before new technologies included video surveillance systems, security cameras were used only for recording events as they occurred, and a human had to analyze the recorded data. Nowadays, computers are used for video analytics, and video surveillance systems have become more autonomous and automated. The types of security cameras have also changed, and the market offers different kinds of cameras with integrated software. Even though there is a variety of hardware, their capabilities leave a lot to be desired. Therefore, this drawback is trying to compensate by dint of computer program solutions. Image processing is a very important part of video surveillance and security systems. Capturing an image exactly as it appears in the real world is difficult if not impossible. There is always noise to deal with. This is caused by the graininess of the emulsion, low resolution of the camera sensors, motion blur caused by movements and drag, focus problems, depth-of-field issues, or the imperfect nature of the camera lens. This paper reviews image processing, pattern recognition, and image digitization techniques, which will be useful in security services, to analyze bio-images, for image restoration, and for object classification.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.155-160
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• 2014

There are many image processing based algorithms and applications that fail when illumination change occurs. Therefore, the illumination change has to be detected then the illumination change occurred images need to be enhanced in order to keep the appropriate algorithm processing in a reality. In this paper, a new method for detecting illumination changes efficiently in a real time by using local region information and fuzzy logic is introduced. The effective way for detecting illumination changes in lighting area and the edge of the area was selected to analyze the mean and variance of the histogram of each area and to reflect the changing trends on previous frame's mean and variance for each area of the histogram. The ways are used as an input. The changes of mean and variance make different patterns w hen illumination change occurs. Fuzzy rules were defined based on the patterns of the input for detecting illumination changes. Proposed method was tested with different dataset through the evaluation metrics; in particular, the specificity, recall and precision showed high rates. An automatic parameter selection method was proposed for contrast limited adaptive histogram equalization method by using entropy of image through adaptive neural fuzzy inference system. The results showed that the contrast of images could be enhanced. The proposed algorithm is robust to detect global illumination change, and it is also computationally efficient in real applications.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3718-3726
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• 1999

Color image is more effective than gray one in human visual perception. Therefore, color image processing becomes important area. Color images are often corrupted by noises due to the input sensor, channel transmission errors and so on. Some filtering techniques such as vector median, mean filter, and vector $\alpha-trimmed$ mean filter have been used for color noise removal. Among them, vector $\alpha-trimmed$ mean filter gave the best performance in the mixed color noise. But, there are edge shift and blurring effect because vector $\alpha-trimmed$ mean filter is uniformly processed across the image. So, we proposed a conditional fuzzy cluster filter to improve this problems. Simulation results showed that the proposed scheme improves the NCD measure and visual quality over the conventional vector $\alpha-trimmed$ mean filter in the mixed color noise.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1998.11a
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• pp.214-218
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• 1998

칼라 영상은 단색조의 영상에 비해 인간의 시각을 크게 향상시킨다. 따라서 칼라 영상 처리에 관한 연구는 매우 중요하다. 칼라 영상은 센서 잡음이나 채널 전송 에러에 의해 생기는 잡음에 의해 자주 오염되어진다. 이러한 칼라 잡음을 제거하기 위해 여러 형태의 필터들이 개발되어 왔는데 혼합된 잡음에서 벡터 $\alpha$-trimmed 평균 필터는 우수한 성능을 보였다. 그러나, 벡터 $\alpha$-trimmed 평균 필터는 필터링 과정이 영상의 전 영역에 걸쳐 균일하게 적용되어지기 때문에 윤곽선 이동이 일어나 blurring 현상이 심하게 나타나는 문제점이 있다. 이러한 문제점을 개선하기 위해 본 논문에서는 윤곽선 영역과 smooth 영역을 구분한 뒤 각 영역에 적합한 선택적인 필터링을 하는 조건적인 퍼지 클러스터 필터를 제안하였고 제안된 조건적인 퍼지 클러스터 필터는 기존의 벡터 $\alpha$-trimmed 평균 필터에 비해 혼합된 잡음에서 우수한 성능을 보였다.