• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.2
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• pp.44-57
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• 1995

In this paper GFI (Generalized Fuzzy Isodata) and FI (Fuzzy Isodata) algorithms are studied and applied to the tire tread pattern classification problem. GFI algorithm which repeatedly grouping the partitioned cluster depending on the fuzzy partition matrix is general form of GI algorithm. In the constructing the binary tree using GFI algorithm cluster validity, namely, whether partitioned cluster is feasible or not is checked and construction of the binary tree is obtained by FDH clustering algorithm. These algorithms show the good performance in selecting the prototypes of each patterns and classifying patterns. Directions of edge in the preprocessed image of tire tread pattern are selected as features of pattern. These features are thought to have useful information which well represents the characteristics of patterns.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.655-660
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• 2003

In general, Support Vector Machine has a good performance in binary classification, but it has the limitation on multi-pattern classification. So, we proposed an Import Vector Voting model for two or more labels classification. This model applied kernel bagging strategy to Import Vector Machine by Zhu. The proposed model used a voting strategy which averaged optimal kernel function from many kernel functions. In experiments, not only binary but multi-pattern classification problems, our proposed Import Vector Voting model showed good performance for given machine learning data.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.385-399
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• 2017

Nowadays, with the development of signal processing technique, the protection to the integrity and authenticity of images has become a topic of great concern. A blind image authentication technology with high tamper detection accuracy for different common attacks is urgently needed. In this paper, an improved fragile watermarking method based on local binary pattern (LBP) is presented for blind tamper location in images. In this method, a binary watermark is generated by LBP operator which is often utilized in face identification and texture analysis. In order to guarantee the safety of the proposed algorithm, Arnold transform and logistic map are used to scramble the authentication watermark. Then, the least significant bits (LSBs) of original pixels are substituted by the encrypted watermark. Since the authentication data is constructed from the image itself, no original image is needed in tamper detection. The LBP map of watermarked image is compared to the extracted authentication data to determine whether it is tampered or not. In comparison with other state-of-the-art schemes, various experiments prove that the proposed algorithm achieves better performance in forgery detection and location for baleful attacks.

• The Journal of Korean Medicine
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• v.38 no.4
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• pp.104-109
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• 2017

Objectives: Epicardial fat is true visceral fat that is known to be associated with metabolic syndrome, high abdominal fat, insulin resistance, coronary artery diseases, low coronary flow reserve and subclinical atherosclerosis. Dampness-Phlegm pattern is one of the pattern diagnosis of traditional Korean medicine. Previous studies showed that Dampness-Phlegm pattern is associated with hypertension, dyslipidemia, metabolic syndrome. This study is intended to find association between Dampness-Phlegm pattern and epicardial fat thickness. Methods: This study was a community-based single center trial. Ischemic stroke patients within 30 days after their ictus were enrolled. Epicardial fat thickness was measured using transthoracic echocardiography. Other measured and obtained variables are medical history, weight, height, body mass index, fasting blood glucose, cholesterol, triglycerol, high density lipoprotein, lipid and low density lipoprotein. Results: Three hundred sixty six were enlisted, and one hundred forty were diagnosed with the Dampness-Phlegm pattern. Dampness-Phlegm pattern group had significantly thicker epicardial fat. Binary logistic regression also showed statistically significant result. Conclusions: This study showed close association between epicardial fat and Dampness-Phlegm pattern. This result suggests a clue to standardization of pattern identification.

• Journal of Electrical Engineering and information Science
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• v.3 no.3
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• pp.342-347
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• 1998

An OptiNeural system is developed for optical pattern classification. It is a novel hybrid system which consists of an optical processor and a multilayer neural network. It takes advantages of two dimensional processing capability of an optical processor and nonlinear mapping capability of a neural network. The optical processor with a binary phase only filter is used as a preprocessor for feature extraction and the neural network is used as a decision system through mapping. OptiNeural system is trained for optical pattern classification by use of a simulated annealing algorithm. Its classification performance for grey tone texture patterns is excellent, while a conventional optical system shows poor classification performance.

• Journal of Industrial Technology
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• v.17
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• pp.331-338
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• 1997

In this paper, we propose a new neural pattern recognition from wavelet transform. We first analysis in BFT(Binary Field Transform) in character image. The proposed neural network and wavelet transform is able to improve learning time and scaling. The ability and effectiveness of identifying image using the proposed wavelet transform will be demonstrated by computer simulation.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.11
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• pp.1867-1871
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• 1989

One of the most common problems in pattern recognition and image processing is filling the interior of a region when its contour is given. The existing algorithms of the filling are parity check technique, seeding technique, and technique based on chain coding the boundaries. In this paper, a very simple but effective technique for filling the interior of bounded region is proposed. This algorithm is based on the information of binary-coded boundary direction and covers some of the drawbacks reported in the earlier relevant works.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.29A no.8
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• pp.10-17
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• 1992

A new line code design technique based on the BUDA(Binary Unit DSV and ASV) concept is introduced. The new line code called MB34 and designed by this new technique is of the minimum bandwidth, dc-free, and runlength limited. To confirm the performance of the new code, its power spectrum and eye pattern are obtained, wherein spectral nulls at dc(f=0) and Nyguist frequency (f=1/2Ts) are clearly identified. It is also discussed how the transmission errors can be detected by monitoring the DSV, the ASV, and the runlength.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.11
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• pp.111-119
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• 1992

In this paper, we propose an edge detection scheme for noisy images based on the co-occurrence matrix. In the proposed scheme based on the step edge model, the gray level information is simply converted into a bit-map, i.e., the uniform and boundary regions of an image are transformed into a binary pattern by using the local mean. In this binary bit-map pattern, 0 and 1 densely distributed near the boundary region while they are randomly distributed in the uniform region. To detect the boundary region, the co-occurrence matrix on the bit-map is introduced. The effectiveness of the proposed scheme is shown via a quantitative performance comparison to the conventional edge detection methods and the simulation results for noisy images are also presented.

• Journal of Biosystems Engineering
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• v.21 no.3
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• pp.315-324
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• 1996

This study was one of a series of studies on application of machine vision and image processing to extract the geometrical features of plants and to analyze plant growth. Several algorithms were developed to measure morphological properties of plants and describing the growth development of in-situ lettuce(Lactuca sativa L.). Canopy, centroid, leaf density and fractal dimension of plant were measured from a top viewed binary image. It was capable of identifying plants by a thinning top viewed image. Overlapping the thinning side viewed image with a side viewed binary image of plant was very effective to auto-detect meaningful nodes associated with canopy components such as stem, branch, petiole and leaf. And, plant height, stem diameter, number and angle of branches, and internode length and so on were analyzed by using meaningful nodes extracted from overlapped side viewed images. Canopy, leaf density and fractal dimension showed high relation with fresh weight or growth pattern of in-situ lettuces. It was concluded that machine vision system and image processing techniques are very useful in extracting geometrical features and monitoring plant growth, although interactive methods, for some applications, were required.