• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1545-1546
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• 2008

In this study, an image processing method with FRANSAC(Fuzzy RANSAC) is presented and discussed for the development of a microscopic gap measuring algorithm. Many problems in edge detection processing are mainly occurred by the illumination system. A serious problem is that the edge set of gap could include the error elements that have relatively larger error than normal. This problem leads to a incorrect measurement of gap. We present a gap measuring algorithm using FRANSAC[1] that is a representative robust estimation algorithm. FRANSAC is peformed by first categorizing all data into good sample set, bad sample set and vague sample set using a fuzzy classification and then sampling in only good sample set. Experimental results show that the presented gap measuring algorithm gives a higher accurate value of gap especially for the more noisy image data.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.246-249
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• 2009

Due to the additive white Gaussian noise (AWGN), images are often corrupted. In recent days, Bayesian estimation techniques to recover noisy images in the wavelet domain have been studied. The probability density function (PDF) of an image in wavelet domain can be described using highly-sharp head and long-tailed shapes. If a priori probability density function having the above properties would be applied well adaptively, better results could be obtained. There were some frequently proposed PDFs such as Gaussian, Laplace distributions, and so on. These functions model the wavelet coefficients satisfactorily and have its own of characteristics. In this paper, mixture distributions of Gaussian and Laplace distribution are proposed, which attempt to corporate these distributions' merits. Such mixture model will be used to remove the noise in images by adopting Maximum a Posteriori (MAP) estimation method. With respect to visual quality, numerical performance and computational complexity, the proposed technique gained better results.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.4
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• pp.13-18
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• 2012

In this paper, we propose a noise estimation method for noise reduction. It is based on block and pixel-based noise estimation. We assume that an input image is contaminated by the additive white Gaussian noise. Thus, we use an adaptive Gaussian filter and estimate the amount of noise. It computes the standard deviation of each block and estimation is performed on pixel-based operation. The proposed algorithm divides an input image into blocks. This method calculates the standard deviation of each block and finds the minimum standard deviation block. The block in flat region shows well noise and filtering effects. Blocks which have similar standard deviation are selected as test blocks. These pixels are filtered by adaptive Gaussian filtering. Then, the amount of noise is calculated by the standard deviation of the differences between noisy and filtered blocks. Experimental results show that our proposed estimation method has better results than those by existing estimation methods.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.4
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• pp.159-165
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• 2012

The accurate focusing position should be determined for accurate measurements In VMS. Camera lens focusing is an important problem in computer vision and video measuring systems (VMS) that use CCD cameras and high precision XYZ stages. Camera focusing is a very important step in high precision measurement systems that use computer vision technique. The auto focusing process consists of two steps, the focus value measurement step and the exact focusing position determination step. It is suitable for eliminating high frequency noises with lower processing time and without blurring. An automatic focusing technique is applied to measure a crater with a one-dimensional search algorithm for finding the best focus. Throughout this paper, the suggested algorithm for the Auto focusing was combined with the learning. As a result, it is expected that such a combination would be expanded into the system of recognizing voices in a noisy environment.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.7
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• pp.147-153
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• 1996

For shape invariant recognitin of korean characters iwth noise, an optical wavelet SDF filter is proposed To preserve the features of a reference image and inimize effects of a random noise in the inpt image wavelet transformed images with different dialation parameters are used. And to adapt to divese variations in the combinatorial form, eCP-SDF filter synthesis algorithm is used. The proposed optical wavelet SDF filter is the type of the matched filter so that it can use the structure of 4f optical correlation system. The computer simulation results show that the proposed filter is useful in the noisy input.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.458-461
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• 2000

Variable length codes are often used in entropy coding, but are very vulnerable in noisy environments. Reversible variable length codes, however, muse possible to decode instantaneously in both forward and backward directions, so that more usable data can be retrieved when bit errors occur via transmission. Furthermore, partial decodability is desirable to introduce in the reversible variable length code because ROI (Region Of Interest) decoding function is sometimes required in recent image information systems such as the medical imaging, the digital museum and so on. In this paper, we propose a partially decodable and reversible variable length code by modifying Golomb-Rice code.

• Journal of the Semiconductor & Display Technology
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• v.10 no.3
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• pp.99-103
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• 2011

The stereoscopic vision system is the algorithm to obtain the depth of target object of stereo vision image. This paper presents an efficient disparity matching method using blue edge filter and graph cut algorithm. We do recommend the use of the simple sobel edge operator. The application of B band sobel edge operator over image demonstrates result with somewhat noisy (distinct border). The basic technique is to construct a specialized graph for the energy function to be minimized such that the minimum cut on the graph also minimizes the energy (either globally or locally). This method has the advantage of saving a lot of data. We propose a preprocessing effective stereo matching method based on sobel algorithm which uses blue edge information and the graph cut, we could obtain effective depth map.

• Current Optics and Photonics
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• v.1 no.5
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• pp.491-499
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• 2017

Ghost imaging offers great potential, with respect to standard imaging, for imaging objects in optically harsh or noisy environments. It can solve the problems that are difficult to solve by conventional imaging techniques. Recently, it has become a hot topic in quantum optics. In this paper, we propose a scheme for ghost imaging based on rosette scanning, named rosette ghost imaging. Sampling a small area sampling instead of the whole object, the instantaneous field of view of rosette scanning is used as the modulation light field in ghost imaging. This scheme reduces energy loss, the number of samples, and the sampling time, while improving the quality of the reconstructed image.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.12
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• pp.3759-3766
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• 1996

In this paper a new gefchmique to measaure the creater wear using image processing and automatic focusing is presented. The contour detection algorithm, which can adopt ina noisy image, is suggested. It is suitable for eliminating high frequency noses with lower processing time and without blurring. An automatic focusing technique is applied to measure a createrwear depth with a one-dimensional search algorithm for finding the bestfocus. This method is implemented in the tool microscope driven by a servo motor. The results show that the countour and depth of crater wear can be measured reliably.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.2
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• pp.83-90
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• 2011

In cell biology area, microscopy enables detecting objects inside cells that are stained or fluorescently tagged. It is disadvantageous for observing these objects because of the noisy characteristics of their environmental surrounding. In this paper, a framework is proposed to increase the throughput and reliability for analysis of these images. First, we apply adaptive beamlet transform to extract edges meaningfully followed by orientation, location, and length in different scales. Then, a post-process is implemented to extend and map them onto original image. Our proposed scheme is compared with Canny edge detector and conventional beamlet transform from four evaluation aspects. It produces better results when experiments are conducted on real images. Much better results for observing internal parts make this framework competitive for analysis of cell images.