• Journal of the Korean Society for Precision Engineering
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• v.20 no.8
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• pp.108-114
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• 2003

This research describes a image-processing technique for the scratch detecting algorithm for ITO coated glass. We use the modified logical thresholding method (called adaptive logical thresholding method) for binarization of gray-scale glass image. This method is useful to the algorithm for detecting the scratch of ITO coated glass automatically without need of any prior information of manual fine-tuning of parameters.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.373-376
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• 2002

This paper represents a survey of a variety thresholding techniques including both global and local thresholding. Several thresholding methods are examined in detail to evaluate their performance based on a given set of test images. We also attempt to evaluate the performance of several thresholding methods for construction field documents image recognition system using a broken line structures, broken symbols and text, blurring of lines, symbols and text, noise in homogeneous areas measure as a criterion functions.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.419-421
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• 1999

본 논문은 자궁경부암 검사를 위한 전처리 과정인 자궁경부암 세포 영상분할 문제 연구의 결과이다. 자궁경부암 세포 영상은 배경과 세포질 및 세포핵의 구별이 어렵다. 게다가 자궁경부암 검사 시스템은 짧은 시간동안 많은 영상을 처리해야 하기 때문에, 영상의 분석 속도가 빠르고 강력한 영상 분할 기법이 필요하다. 이를 위하여 우리는 thresholding 기법을 연구하였다. 먼저 세포 영상의 각 화소의 명암의 분포를 조사하여 히스토그램을 구하였다. 히스토그램은 0~255 사이에 존재하게 되는데, 0~255의 전 영역에 존재하기 보다는 그 중 일부분에만 존재한다. 우리는 히스토그램이 존재하는 영역을 백분율로 나누고 세포핵 및 세포질이 존재하는 영역의 분포를 구하여 global threshold를 찾았고, 이를 기준으로 각 점을 thresholding 할 때에 주위의 평균값을 보정값으로 두어 local thresholding을 수행하였다. 결과 영상은 핵의 영역을 탐색하기 위한 seed로 사용하기에 적합하다.

• Journal of the Korea society of information convergence
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• v.6 no.1
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• pp.1-6
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• 2013

The electrocardiogram(ECG) is widely used for the diagnosis of heart disease recent. In order to correct diagnosis, wavelet and thresholding is studied. In this study, we study hard inverse thresholding that is apply the existing hard thresholding. It apply to hard inverse thresholding on Pan-Tomkins algorism, that was simplified. The results of mit-bih No. 103 ECG signal is detected R peaks was detected unaffected by signal distortion and noise.

• Journal of information and communication convergence engineering
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• v.10 no.1
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• pp.45-52
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• 2012

Thedenoising of a natural image corrupted by Gaussian noise is a long established problem in signal or image processing. Today the research is focus on the wavelet domain, especially using the wavelet threshold method. In this paper, a waveletbased image denoising modified adaptive thresholding method is proposed. The proposed method computes thethreshold adaptively based on the scale level and adaptively estimates wavelet coefficients by using a modified thresholding function that considers the dependency between the parent coefficient and child coefficient and the soft thresholding function at different scales. Experimental results show that the proposed method provides high peak signal-to-noise ratio results and preserves the detailed information of the original image well, resulting in a superior quality image.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.10
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• pp.112-122
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• 1994

Number plates of vehicles operating in real world are sometimes difficult to recognize due to the number plate degradation (bent or dirty plates). To recognize the vehicle number from a number plate with severe degradation, good segmentation is necessary, which in turn requires good thresholding. This paper proposes a binarization method that combines the fast processing speed of global thresholding methods with the local thresholding methods' ability to adapt to lacal gray level characteristics. The proposed method overcomes the degradation of number plates quickly and maintains the widths of digit strokes uniform. The paper presents results of comparison with existing global and local thresholding methods.

• 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.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.1
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• pp.19-32
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• 2001

This paper proposes a local adaptive thresholding method based on a water flow model, in which an image surface is considered as a 3-dimensional (3-D) terrain. To extract characters from backgrounds, we pour water onto the terrain surface. Water flows down to the lower regions of the terrain and fills valleys. Then, the amount of filled water is thresholded, in which the proposed thresholding method is applied to gray level document images consisting of characters and backgrounds. The proposed method based on a water flow model shows the property of locally adaptive thresholding. Computer simulation with synthetic and real document images shows that the proposed method yields effective adaptive thresholding results for binarization of document images.

• Journal of the Korea Society of Computer and Information
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• v.8 no.4
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• pp.7-12
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• 2003

This paper propose the 1-pass spatially adaptive wavelet thresholding for image denosing. The method of wavelet thresholding for denosing, has been concentrated on finding the best uniform threshold or best basis. However, not much has been done to make this method adaptive to spatially changing statistics which is typical of a large class of images. This spatially adaptive thresholding is extended to the overcomplete wavelet expansion, which yields better results than the orthogonal transform. Experiments show that this proposed method does indeed remove noise significantly, especially for large noise power. Experimental results show that the proposed method outperforms level dependent thresholding techniques and is comparable to spatial Wiener filtering method, 2-pass spatially adaptive wavelet thresholding method in matlab.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.543-548
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• 2011

Entropy measuring the richness in details of the image is generally obtained by using the histogram of gray levels in an image, and has been widely used as an index for thresholding of the image. In this paper, we propose an entropy-based thresholding method, where the entropy is obtained not by the histogram but by the variance of the gray levels, to binalize a given image. The effectiveness of the proposed method is demonstrated by thresholding experiments on nine test images and comparison with conventional two thresholding methods, that is, Otsu method and entropy-based method using the histogram.