• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.208-212
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• 1998

This paper suggests the method to recognize the various defect patterns of cold mill strip using binary decision tree constructed by genetic algorithm automatically. In case of classifying the complex the complex patterns with high similarity like the defect patterns of cold mill strip, the selection of the optimal feature set and the structure of recognizer is important for high recognition rate. In this paper genetic algorithm is used to select a subset of the suitable features at each node in binary decision tree. The feature subset of maximum fitness is chosen and the patterns are classified into two classes by linear decision function. After this process is repeated at each node until all the patterns are classified respectively into individual classes. In this way , binary decision tree classifier is constructed automatically. After construction binary decision tree, the final recognizer is accomplished by the learning process of neural network using a set of standard p tterns at each node. In this paper, binary decision tree classifier is applied to recognition of the defect patterns of cold mill strip and the experimental results are given to show the usefulness of the proposed scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.98-103
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• 2000

This paper suggests a method to recognize the various defect patterns of a cold mill strip using a binary decision tree automatically constructed by a genetic algorithm(GA). In classifying complex patterns with high similarity like the defect patterns of a cold mill stirp, the selection of an optimal feature set and an appropriate recognizer is important to achieve high recognition rate. In this paper a GA is used to select a subset of the suitable features at each node in the binary decision tree. The feature subset with maximum fitness is chosen and the patterns are classified into two classes using a linear decision function. This process is repeated at each node until all the patterns are classified into individual classes. In this way, the classifier using the binary decision tree is constructed automatically. After constructing the binary decision tree, the final recognizer is accomplished by having neural network learning sits of standard patterns at each node. In this paper, the classifier using the binary decision tree is applied to the recognition of defect patterns of a cold mill strip, and the experimental results are given to demonstrate the usefulness of the proposed scheme.

• 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.