• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.283-286
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• 2007

임계값을 이용한 영상 분할은 대표적인 영상 분할 기법으로 Otsu의 임계값 결정법, Fuzzy 엔트로피를 이용한 H&W의 기법 및 Clustering을 이용한 Kwon의 기법 등 많은 방법이 있다. 대부분의 임계값 결정 기법은 영상에서 얻어진 빈도수 히스토그램의 분석을 통해서 임계값을 결정한다. 특히 Otsu의 임계값 결정 기법은 빈도수 히스토그램의 분산을 최대화하는 방법으로 임계값을 결정하는 빈도수 히스토그램에 기반한 대표적 기법이다. 하지만 영상 기술이 발전함에 따라서 하나의 임계값으로부터 영상을 이진화 하는 기법은 효용성이 떨어지고 있다. 따라서 다중의 임계값을 결정하는 효과적인 방법이 필요하다. 본 논문에서는 그레이 레벨간의 관계성을 파악하고 이러한 관계성으로부터 다중의 임계값을 결정하는 기법을 제안한다. 제안된 기법의 효용성은 모의실험에서 다중 임계값을 사용한 분할영상을 통해서 보인다.

• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.167-175
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• 2007

Fuzzy logic is used to represent qualitative knowledge and provides interpretability to a controlling system model in bioinformatics. This paper focuses on a bioinformatics data classification which is an important bioinformatics application. This paper reviews the two traditional controlling system models The sequence-based threshold controller have problems of optimal range decision for threshold readjustment and long processing time for optimal threshold induction. And the binary-based threshold controller does not guarantee for early system stability in the GPCR data classification for optimal threshold induction. To solve these problems, we proposes a fuzzy-based threshold controller for ART1 clustering in GPCR classification. We implement the proposed method and measure processing time by changing an induction recognition success rate and a classification threshold value. And, we compares the proposed method with the sequence-based threshold controller and the binary-based threshold controller The fuzzy-based threshold controller continuously readjusts threshold values with membership function of the previous recognition success rate. The fuzzy-based threshold controller keeps system stability and improves classification system efficiency in GPCR classification.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.04a
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• pp.153-156
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• 2000

동영상 정보는 영상정보뿐만 아니라 음성정보, 문자정보 및 각종 의미있는 정보들을 포함하고 있어서 기존의 검색방법으로는 사용자가 원하는 이미지를 찾는데 어려움이 따른다. 따라서, 본 연구에서는 동영상 정보의 효율적인 활용을 위한 색인방법으로 칼라 임계값을 이용한 컷 검출 방법을 제안하였다. 이것은 frame 간의 유사도를 측정해서 이 값이 주어진 임계값보다 작을 경우, 장면의 전환이 일어나는 곳을 컷 지점으로 검출하는 것인데, 동영상의 장면에 따른 유사도가 다를 수 있기 때문에, 컷을 구성하는 프레임들간의 칼라 임계값에 변동을 주어 최적의 컷 검출율을 구하고자 했다. 초기의 칼라 임계값은 '80'을 사용했고, 이후 frame 의 유사도가 임계값보다 클 경우, 즉 장면전환이 일어나지 않았을 경우일정한 상수 값을 초기 임계값에서 감산토록 하였다. 이러한, 과정을 거쳐 추출된 frame을 가지고 원하는 이미지를 검색하게 되면 사용자의 노력 및 검색 시간이 단축되고, 동영상 정보의 관리가 용이해진다.

• Journal of the Korea Computer Industry Society
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• v.2 no.3
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• pp.389-396
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• 2001

In detection of a scene change of the moving pictures which has massive information capacity, the temporal sampling method has a faster searching speed and lower missing scene change detection than the sequential searching method for the whole moving pictures, yet employed searching algorithm and detection interval greatly affect missing frame and searching precision. In this study, the whole moving pictures were primarily retrieved threshold by the temporal difference of histogram scene change detection method. We suggested a dynamic threshold algorithm using cut detection interval and derived an equation formula to determine optimal primary retrieval threshold which can cut detection interval computation. Experimental results show that the proposed dynamic threshold algorithm using cut detection interval method works up about 30 percent in precision of performance than the sequential searching method.

• Journal of Satellite, Information and Communications
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• v.8 no.1
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• pp.66-70
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• 2013

In this paper, Threshold setting method is proposed to improve detection probability for cooperative sensing. Even if cooperative users have all same false alarm rate, each user has different threshold due to pass ad-hoc channel. threshold level is related to detection probability. So, we select the highest threshold among cooperative users and then share threshold information for getting the high detection probability.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.605-608
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• 2007

The denoising for image restoration based on the Wavelet Packet with absolute average threshold is presented. The Existing method is used standard deviation estimated results in increasing the noise and threshold, and damaging an image quality. In addition that is decreased image restoration PSNR by using the same threshold in spite of changing image because of installing a threshold in proportion of noise size. In contrast, the absolute average threshold with wavelet packet is adapted by changing image to set up threshold by statistic quantity of resolved image and is avoided an extreme impart. The results on the experiment has improved 10% and 5% over than the denoising based on simple wavelet transform and wavelet packet respectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.725-730
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• 2007

Gray-level histogram-based threshold selection methods such as Otsu's method, Huang and Wang's method, and etc. have been widely used for the threshold selection in image processing. They are simple and effective, but take too much time to determine the optimal multilevel threshold values as the number of thresholds are increased. In this paper, we measure correlation between gray-levels by using the Gaussian function and define a Gaussian-type finite mixture distribution which is combination of the Gaussian distribution function with the gray-level histogram, and propose a fast and effective threshold selection method using it. We show the effectiveness of the proposed through experimental results applied it to three images and the efficiency though comparison of the computational complexity of the proposed with that of Otsu's method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1122-1128
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• 2007

The denoising for image restoration based on the Wavelet Packet Transform with AA(Absolute Average) making-threshold is presented. The wavelet packet transform leads to be better in the part of high frequency than wavelet transform to eliminate noise. And the existing threshold determination is used standard deviation estimated results in increasing the noise and threshold, and damaging an image quality. In addition that is decreased image restoration PSNR by using the same threshold in spite of changing image because of installing a threshold in proportion of noise size. In contrast the AA thresholding method with wavelet packet is adapted by changing image to set up threshold by statistic quantity of resolved image and is avoided an extreme impact. The results on the experiment has improved 10% and 5% over than the denoising based on simple wavelet transform and wavelet packet respectively.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.3
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• pp.255-264
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• 2002

The topography is recognized as an important factor in determining the streamflow response of watershed to precipitation. In watershed analysis, stream networks are very important parameters. Each DEM grid size and flowaccumulation threshold value of drainage accumulation matrix have influence on stream networks extracted by using grid DEM. Therefore, stream networks extracted from DEM varies with each DEM grid size and flowaccumulation threshold value. Generally, small threshold values will generate more detailed stream network with higher drainage density High threshold values will generate coarser stream networks. In this paper, total stream length in the study area was used to calculate the flowaccumulation threshold value by each DEM grid size. Stream network was derived by each DEM grid size, which is applied flowaccumulation threshold value. Regression equation was derived by correlation between flowaccumulation threshold value and each DEM grid size.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.1
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• pp.23-28
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• 2013

The process of a feature matching for two different spatial data sets is similar to the process of classification as a binary class such as matching or non-matching. In this paper, we calculated a threshold by applying an equal error rate (EER) which is widely used in biometrics that classification is a main topic into spatial data sets. In a process of discriminating what's a matching or what's not, a precision and a recall is changed and a trade-off appears between these indexes because the number of matching pairs is changed when a threshold is changed progressively. This trade-off point is EER, that is, threshold. To the result of applying this method into training data, a threshold is estimated at 0.802 of a value of shape similarity. By applying the estimated threshold into test data, F-measure that is a evaluation index of matching method is highly value, 0.940. Therefore we confirmed that an accurate threshold is calculated by EER without person intervention and this is appropriate to matching different spatial data sets.