• 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.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.9
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• pp.801-805
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• 2012

This paper proposes dynamic thresholding scheme for fingerprint identification. As a user authentication method by fingerprint recognition technology, verification method based on 1:1 matching was mainly used in the past, but identification method based on 1:N matching is generally used recently. The control of the value of FAR is very important in the application areas such as access control and time attendance systems. This paper proposes dynamic thresholding scheme which could properly control the value of FAR according to the field of applications and size of the fingerprints database.

• 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 Institute of Intelligent Systems
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• v.19 no.3
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• pp.304-310
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• 2009

In this paper, target extraction method in FLIR(forward-looking infrared) images based on fuzzy thresholding which used bi-modality and adjacency to determine membership value is proposed. The bi-modality represents how a pixel is classified into a part of target using distribution of pixel values in a local region, and The adjacency is a measure to represent how each pixel is far from the target region. First, membership value is calculated using above two measures, and then fuzzy thresholding is performed to extract the target. To evaluate performance of proposed target extraction method, we compare other segmentation methods using various FLIR tank image. Experimental results show that the proposed algorithm is a good segmentation performance.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.693-698
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• 2001

Noisy data are often fitted using a smoothing parameter, controlling the importance of two objectives that are opposite to a certain extent. One of these two is smoothness and the other is closeness to the input data. The optimal value of this parameter minimizes the error of the result. This optimum cannot be found exactly, simply because the exact data are unknown. This paper propose the threshold value for noise reduction based on wavelet-thresholding. In the proposed method PSNR results show that the threshold value performs excellently in comparison with conventional methods without knowing the noise variance and volume of signal.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.6
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• pp.1169-1174
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• 2001

A new approach for determining global threshold value of binary image is proposed in this paper. In the proposed algorithm, bit-plane information which involve the shapes of original image is used for dividing image into two parts; object and background. Optimal threshold value are selected based on difference values of average between two regions, which is considered in global binary thresholding. Proposed method is no need to set a initial value, and consequently, it is relatively simple as well as robust. Experimental results showed a good performance in preserving edge not only continuous tone images but also document image.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1240-1242
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• 1996

In a still mill, the effective surface defect inspection algorithm is necessary. For this purpose, this paper proposed the preprocessing algorithm for surface defect inspection of cold mill strip. This consists of live steps. They are edge detection, binarizing, noise deletion, combining of fragmented defect and selecting the largest defect. Especially, binarizing is a critical problem. Bemuse the performance of the preprocessing is largely depend on the binarized image. So, we develope the adaptive thresholding method, which is multilevel thresholding. The thresholding value is varied according to the mean graylevel value of each test image. To investigate the performance of the proposed algorithm, we classified the detected defect using neural network. The test image is 20 defect images captured at German Sick Co. This algorithm is proved to have good property in cold mill strip surface inspection.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.05a
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• pp.352-355
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• 2000

In this paper, we propose an adaptive thresholding for edge detection. first, we got histograms for background image and image with moving object, respectively. Then we make difference histogram between histograms of background and object image. A thresholding value is decided using gradient of peak to peak in the difference histogram. The experimentation is processed using a moving car in the road. The result is that edge is detected well regardless of the brightness.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.533-536
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• 1997

In this paper, we visualized 3-dimensional volume of heart using volume method by thresholding in EBT slices data. Volume rendering is the method that acquire the color by casting a pixel ray to volume data. The gray level of heart region is so high that we decide heart region by thresholding method. When a pixel ray is cast to volume data, the region that is higher than threshold value becomes heart region. We effectively rendered the heart volume and showed the 3-dimensional heart volume.