• Title, Summary, Keyword: thresholding

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Thresholding for CFD data compression based on Supercompact Multiwavelets (Supercompact Multiwavelets 을 이용한 CFD 데이터 압축의 Thresholding)

  • Kwon, Do-Hoon;Lee, Do-Hyung
    • Proceedings of the KSME Conference
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    • pp.962-967
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    • 2003
  • CFD data compression method based on supercompact multiwavelets is presented. High data compression can be achieved when taking advantage of the compact nature of multiwavelets. Thresholding technique is also a matter of primary concern in determining pressure ratio. In this paper, we apply thresholding for multiwavelets that considers the coefficient vector as a whole rather than thresholding individual elements. Various thresholding methods are described briefly. CFD data compression suggests that the multivariate thresholding method is suitable for supercompact multiwavelets.

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Translation-invariant Wavelet Denoising Method Based on a New Thresholding Function for Underwater Acoustic Measurement (수중 음향 측정을 위한 새로운 임계치 함수에 의한 TI 웨이블렛 잡음제거 기법)

  • Choi, Jae-Yong
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.16 no.11
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    • pp.1149-1157
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    • 2006
  • Donoho et al. suggested a wavelet thresholding denoising method based on discrete wavelet transform. This paper proposes an improved denoising method using a new thresholding function based on translation-invariant wavelet for underwater acoustic measurement. The conventional wavelet thresholding denoising method causes Pseudo-Gibbs phenomena near singularities due to the lack of translation-invariant of the wavelet basis. To suppress Pseudo-Gibbs phenomena, a denoising method combining a new thresholding function based on the translation-invariant wavelet transform is proposed in this paper. The new thresholding function is a modified hard-thresholding to each node according to the discriminated threshold so as to reject unknown external noise and white gaussian noise. The experimental results show that the proposed method can effectively eliminate noise, extract characteristic information of radiated noise signals.

Cell Image Segmentation Using Multi-level Thresholding Technique (다단계 thresholding에 의한 세포 영상 영역 분할)

  • 김호영;김선아;최예찬;김백섭
    • Proceedings of the Korean Information Science Society Conference
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    • pp.435-437
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    • 1998
  • 영상에 대한 영역분할은 영상에 대한 인식 시스템에서 가장 중요하고도 어려운 분야로 알려져 있다. 주로 사용되는 방법은 화소중심기법과 영역중심기법이 사용되는데, 화소중심기법은 적은 시간이 걸리는데 비해 영역분할 효과가 떨어지고, 영역중심기법은 상대적으로 양질의 영역분할 효과를 얻을 수 있지만 많은 시간이 걸린다. 본 논문에서는 영역분할에 대한 방법으로 thresholding방법을 이용한 2단계로 이루어진 영역분할 방법을 제안한다. 제안된 방법은 화소의 전역정보와 지역정보를 모두 사용하여 기존의 전역 thresholding방법에 비해 향상된 영역 분할을 수행하고, 지역정보를 이용하는 영역중심 기법에 비해 시간을 단축하는 효과를 가지고 있다. 첫 번째 단계에서는 기존에 알려진 전역 thresholding방법을 사용하여 영역분할을 하고, 두 번째 단계에서는 영상에 대해 미리 알려진 사전지식을 이용하여 영역분할이 제대로 되지 않은 영역을 구분하여 해당 영역에 대해서만 thresholding작업을 수행한다. 사용된 영상은 자궁경부 세포진 영상으로 대상이 되는 영역은 자궁경부 세포의 핵으로 제한하였다.

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Face seqmentation using automatic searching algorithm of thresholding value and statistical projection analysis (자동 임계점 탐색 알고리즘과 통계적 투영 분석을 이용한 얼굴 분할)

  • 김장원;이흥복;김창석
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.21 no.8
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    • pp.1874-1884
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    • 1996
  • In this paper, we proposed automatic searching algorithm of thresholding value using multilevel thresholding for face segmentation from input bust image effectively. The proposed algorithm extracted the thresholding value of brightness that is formed background region, face region and hair region without illumination, background and face size from input image. The statistical projection analysis project the brightness of multilevel thresholding image into horizontal and vertical direction and decide the thresholding value of face. And the algorithm extracted elliptical type block of face from input image in order to reduce the back ground region and hair region efficiently. The proposed algorithm can reduce searching area of feature extraction and processing time for face recognication.

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Pupil Detection using Multistage Adaptive Thresholding and Circular Hough Transform

  • Navastara, Dini Adni;Park, Hyun-Jun;Cha, Eui-Young
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.90-93
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    • 2013
  • This paper presents a multistage adaptive thresholding method and circular Hough transform for pupil detection. Multistage adaptive thresholding is a thresholding method that applies local image statistic within a neighborhood variable and the global thresholds. Therefore, the method can adopt the benefit of local thresholding and prevent an over segmentation at the same time because of the global image information. To detect a pupil, a circular Hough transform is applied to it in which the pupil pattern is considered as a circle shape. The experimental results show the reliability of our proposed method in detecting pupil properly.

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Choice of Thresholding Technique in Micro-CT Images of Trabecular Bone Does Not Influence the Prediction of Bone Volume Fraction and Apparent Modulus

  • Kim, Chi-Hyun;Kim, Byung-Gwan;Guo, X. Edward
    • Journal of Biomedical Engineering Research
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    • v.28 no.2
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    • pp.174-177
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    • 2007
  • Trabecular bone can be accurately represented using image-based finite element modeling and analysis of these bone models is widely used to predict their mechanical properties. However, the choice of thresholding technique, a necessary step in converting grayscale images to finite element models which can thus significantly influence the structure of the resulting finite element model, is often overlooked. Therefore, we investigated the effects of thresholding techniques on micro-computed tomography (micro-CT) based finite element models of trabecular bone. Three types of thresholding techniques were applied to micro-CT images of trabecular bone which resulted in three unique finite element models for each specimen. Bone volume fractions and apparent moduli were predicted for each model and compared to experimental results. Our findings suggest that predictions of apparent properties agree well with experimental measurements regardless of the choice of thresholding technique in micro CT images of trabecular bone.

Multi-level thresholding using Entropy-based Weighted FCM Algorithm in Color Image (Entropy 기반의 Weighted FCM 알고리즘을 이용한 컬러 영상 Multi-level thresholding)

  • Oh, Jun-Taek;Kwak, Hyun-Wook;Kim, Wook-Hyun
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.42 no.6
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    • pp.73-82
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    • 2005
  • This paper proposes a multi-level thresholding method using weighted FCM(Fuzzy C-Means) algorithm in color image. FCM algerian determines a more optimal thresholding value than the existing methods and can extend to multi-level thresholding. But FCM algerian is sensitive to noise because it doesn't include spatial information. To solve the problem, we can remove noise by applying a weight based on entropy that is obtained from neighboring pixels to FCM algerian. And we determine the optimal cluster number by using within-class distance in code image based on the clustered pixels of each color component. In the experiments, we show that the proposed method is more tolerant to noise and is more superior than the existing methods.

A Study on Translation-Invariant Wavelet De-Noising with Multi-Thresholding Function (다중 임계치 함수의 TI 웨이브렛 잡음제거 기법)

  • Choi, Jae-Yong
    • The Journal of the Acoustical Society of Korea
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    • v.25 no.7
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    • pp.333-338
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    • 2006
  • This paper proposes an improved do-noising method using multi-thresholding function based on translation-invariant (W) wavelet proposed by Donoho et al. for underwater radiated noise measurement. The traditional wavelet thresholding de-noising method causes Pseudo-Gibbs phenomena near singularities due to discrete wavelet transform. In order to suppress Pseudo-Gibbs Phenomena, a do-noising method combining multi-thresholding function with the translation-invariant wavelet transform is proposed in this paper. The multi-thresholding function is a modified soft-thresholding to each node according to the discriminated threshold so as to reject かon external noise and white gaussian noise. It is verified by numerical simulation. And the experimental results are confirmed through sea-trial using multi-single sensors.

Region-based Multi-level Thresholding for Color Image Segmentation (영역 기반의 Multi-level Thresholding에 의한 컬러 영상 분할)

  • Oh, Jun-Taek;Kim, Wook-Hyun
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.43 no.6
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    • pp.20-27
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    • 2006
  • Multi-level thresholding is a method that is widely used in image segmentation. However most of the existing methods are not suited to be directly used in applicable fields and moreover expanded until a step of image segmentation. This paper proposes region-based multi-level thresholding as an image segmentation method. At first we classify pixels of each color channel to two clusters by using EWFCM(Entropy-based Weighted Fuzzy C-Means) algorithm that is an improved FCM algorithm with spatial information between pixels. To obtain better segmentation results, a reduction of clusters is then performed by a region-based reclassification step based on a similarity between regions existing in a cluster and the other clusters. The clusters are created using the classification information of pixels according to color channel. We finally perform a region merging by Bayesian algorithm based on Kullback-Leibler distance between a region and the neighboring regions as a post-processing method as many regions still exist in image. Experiments show that region-based multi-level thresholding is superior to cluster-, pixel-based multi-level thresholding, and the existing mettled. And much better segmentation results are obtained by the post-processing method.

Automatic Segmentation of Skin and Bone in CT Images using Iterative Thresholding and Morphological Image Processing

  • Kang, Ho Chul;Shin, Yeong-Gil;Lee, Jeongjin
    • IEIE Transactions on Smart Processing and Computing
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    • v.3 no.4
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    • pp.191-194
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    • 2014
  • This paper proposes a fast and efficient method to extract the skin and bone automatically in CT images. First, the images were smoothed by applying an anisotropic diffusion filter to remove noise. The whole body was then detected by thresholding, which was set automatically. In addition, the contour of the skin was segmented using morphological operators and connected component labeling (CCL). Finally, the bone was extracted by iterative thresholding.