• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.945-961
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• 2019

Tongue diagnosis is one of the most important diagnostic methods in Traditional Chinese Medicine (TCM). Tongue image segmentation aims to extract the image object (i.e., tongue body), which plays a key role in the process of manufacturing an automated tongue diagnosis system. It is still challenging, because there exists the personal diversity in tongue appearances such as size, shape, and color. This paper proposes an innovative segmentation method that uses image thresholding, gray projection and active contour model (ACM). Specifically, an initial object region is first extracted by performing image thresholding in HSI (i.e., Hue Saturation Intensity) color space, and subsequent morphological operations. Then, a gray projection technique is used to determine the upper bound of the tongue body root for refining the initial object region. Finally, the contour of the refined object region is smoothed by ACM. Experimental results on a dataset composed of 100 color tongue images showed that the proposed method obtained more accurate segmentation results than other available state-of-the-art methods.

• 한국정보컨버전스학회:학술대회논문집
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• 2008.06a
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• pp.113-116
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• 2008

In this paper we suggest two novel methods for an implementation of the spot detection phase in the 2-DE gel image analysis program. The one is the adaptive thresholding method for eliminating noises and the other is the asymmetric diffusion model for spot matching. Remained noises after the preprocessing phase cause the over-segmentation problem by the next segmentation phase. To identify and exclude the over-segmented background regions, il we use a fixed thresholding method that is choosing an intensity value for the threshold, the spots that are invisible by one's human eyes but mean very small amount proteins which have important role in the biological samples could be eliminated. Accordingly we suggest the adaptive thresholding method which comes from an idea that is got on statistical analysis for the prominences of the peaks. There are the Gaussian model and the diffusion model for the spot shape model. The diffusion model is the closer to the real spot shapes than the Gaussian model, but spots have very various and irregular shapes and especially asymmetric formation in x-coordinate and y-coordinate. The reason for irregularity of spot shape is that spots could not be diffused perfectly across gel medium because of the characteristics of 2-DE process. Accordingly we suggest the asymmetric diffusion model for modeling spot shapes. In this paper we present a brief explanation ol the two methods and experimental results.

• Current Optics and Photonics
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• v.2 no.2
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• pp.165-171
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• 2018

Fourier ptychographic microscopy (FPM) is a recently proposed computational imaging method that achieves both high resolution (HR) and wide field of view. In the FPM framework, a series of low-resolution (LR) images at different illumination angles is used for high-resolution image reconstruction. On the basis of previous research, image noise can significantly degrade the FPM reconstruction result. Since the captured LR images contain a lot of dark-field images with low signal-to-noise ratio, it is very important to apply a noise-reduction process to the FPM raw dataset. However, the thresholding method commonly used for the FPM data preprocessing cannot separate signals from background noise effectively. In this work, we propose an improved thresholding method that provides a reliable background-noise threshold for noise reduction. Experimental results show that the proposed method is more efficient and robust than the conventional thresholding method.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.1
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• pp.1-8
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• 2010

Noise removal methods for noisy images have been studied a lot in the domain of spatial and transform filtering. Low pass filtering was initially applied in the spatial domain. Recently, discrete wavelet transform has widely used for image denoising as well as image compression due to an excellent energy compaction and a property of multiresolution. In this paper, Gabor cosine and sine transform which is considered as human visual filter is applied to image denoising areas using soft thresholding technique. GCST is compared with excellent wavelet transform which uses existing soft thresholding methods from PSNR point of view. Resultant images removed noises are also visually compared. Experimental results with adding four different standard deviation levels of Gaussian distributed noises to real images show that the proposed transform has better PSNR performance of a maximum of 1.18 dB and visible perception than wavelet transform.

• Korean Journal of Remote Sensing
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• v.24 no.4
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• pp.341-349
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• 2008

This study proposes new automatic thresholding method, which is important step for detecting binary change/non-change information using satellite images. Result value through pixel-based similarity measurement is calculated cumulatively with regular interval, and thresholding is pointed at the steep slope position. The proposed method is assessed in comparison with expectation-maximization algorithm and coner method using synthetic images, ALI images, and Hyperion images. Throughout the results, we validated that our method can guarantee the similar accuracy with previous algorithms. It is simpler than EM algorithm, and can be applied to the binormal histogram unlike the coner method.

• ETRI Journal
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• v.29 no.6
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• pp.736-744
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• 2007

We propose a fast macroblock (MB) mode prediction and decision algorithm based on temporal correlation for P-slices in the H.264/AVC video standard. There are eight block types for temporal decorrelation, including SKIP mode based on rate-distortion (RD) optimization. This scheme gives rise to exhaustive computations (search) in the coding procedure. To overcome this problem, a thresholding method for fast inter mode decision using a MB tracking scheme to find the most correlated block and RD cost of the correlated block is suggested for early stop of the inter mode determination. We propose a two-step inter mode candidate selection method using statistical analysis. In the first step, a mode is selected based on the mode information of the co-located MB from the previous frame. Then, an adaptive thresholding scheme is applied using the RD cost of the most correlated MB. Secondly, additional candidate modes are considered to determine the best mode of the initial candidate modes that does not satisfy the designed thresholding rule. Comparative analysis shows that a speed-up factor of up to 70.59% is obtained when compared with the full mode search method with a negligible bit increment and a minimal loss of image quality.

• The Journal of the Korea Contents Association
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• v.3 no.4
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• pp.23-28
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• 2003

This paper describes, in order to extract contour from topographic map image, an adaptive segment-length thresholding using a threshold depended on target image. First of all, after recognizing the primary symbols and detecting two edges from the projection histogram of the elevation value area, the threshold value is determined by the distance between the edges. Then, the subdivision is peformed by searching a branch point and erasing its neighboring Hack pixels. And contour components are extracted by segment-length thresholding. The experimental result shows that the final image contains non-contour component of 2.41% and contour one of 97.59%.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.8
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• pp.767-773
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• 2009

This research have introduced wavelet signal processing technic for improving navigation signals. INS signals can be distorted with conventional pre-filtering method such as low-pass filtering by unwanted smoothing on real signals. But in this paper, wavelet thresholding method is implemented to INS signal to denoise for INS-GPS integrated system. This method reduces signal noise but not distorts the rapid varing signal. And this paper applied thresholding to INS-GPS integrated navigation system and improved navigation performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.11
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• pp.2453-2458
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• 2012

In this paper, we propose an adaptive thresholding method to binarize two-dimensional barcode images. Adaptive thresholding methods that minimize light effects convert an original image into a binary image. The methods are applied to document image binarization. The methods, however, have problems of determining box size used in adaptive thresholding. thus, they inappropriate to use in recognition of two-dimensional barcode images. To overcome the problem, we analysis the problem and propose a new adaptive threshold method using the integral image. To show the effectiveness of our method, we compared our method with the well-known existing methods in terms of visual quality and processing time. The experimental result indicates that the proposed method is superior to the existing method.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.1-9
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• 2008

One of the problems for implementing the spot detection phase in the 2-DE gel image analysis program is the eliminating noises in the image. Remained noises after the preprocessing phase cause the over-segmented regions by the segmentation phase. To identify and exclude the over-segmented background regions, if we use the fixed thresholding method that is choosing an intensity value for the threshold, the spots that is invisible by the eyes but mean a very small amount proteins which have important role in the biological samples could be eliminated. This paper propose an adaptive thresholding method that come from an idea that is got on statistical analysing for the prominences of the peaks. The adaptive thresholding method works as following. Firstly we calculate an average prominence value curve and fit it to exponential function curve, as a result we get parameters for the exponential function. And then we calculate a threshold value by using the parameters and probability distribution of errors. Lastly we apply the threshold value to the region for determining the region is a noise or not. According to the probability distribution of errors, the reliability is 99.85% and we show the correctness of the proposed method by representing experiment results.