• The Bulletin of The Korean Astronomical Society
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• v.39 no.1
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• pp.56.1-56.1
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• 2014

With the advent of high-resolution high-sensitivity observations, spiral patterns have been revealed around several asymptotic giant branch (AGB) stars. Such patterns can provide possible evidence for the existence of central binary stars embedded in outflowing circumstellar envelopes. It is, however, not generally recognized that the binary induced pattern, vertically extended from the orbital plane, exhibits a ring-like pattern with an inclined viewing angle. I will first review the binary-induced spiral-shell patterns on the AGB circumstellar envelopes with the effect of inclination angle with respect to the orbital plane, of which large inclination cases reveal incomplete ring-like patterns. I will describe a method of extracting such spiral-shell from the gas kinematics of an incomplete ring-like pattern to place constraints on the characteristics of the (unknown) central binary stars. This first success may open the possibility of connecting the ring-like patterns commonly found in the AGB circumstellar envelopes and in the outer parts of (pre-)planetary nebulae and pointing to the conceivable presence of central binary systems, which may give a clue for the onset of asymmetrical planetary nebulae.

• 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.

• 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 Broadcast Engineering
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• v.21 no.4
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• pp.562-568
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• 2016

In this paper, we focus on recognition of speed-limit signs among a few types of traffic signs because speed-limit sign is closely related to safe driving of drivers. Although histogram of oriented gradient (HOG) and local binary patterns (LBP) are representative features for object recognition, these features have a weakness with respect to rotation, in that it does not consider the rotation of the target object when generating patterns. Therefore, this paper propose the fast rotation-invariant binary patterns (FRIBP) algorithm to generate a binary pattern that is robust against rotation. The proposed FRIBP algorithm deletes an unused layer of the histogram, and eliminates the shift and comparison operations in order to quickly extract the desired feature. The proposed FRIBP algorithm is successfully applied to German Traffic Sign Recognition Benchmark (GTSRB) datasets, and the results show that the recognition capabilities of the proposed method are similar to those of other methods. Moreover, its recognition speed is considerably enhanced than related works as approximately 0.47second for 12,630 test data.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2330-2332
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• 1998

This paper suggests a method to recognize the various defect patterns of cold mill strip using binary decision tree automatically constructed by genetic algorithm. The genetic algorithm and K-means algorithm were used to select a subset of the suitable features at each node in binary decision tree. The feature subset with maximum fitness is chosen and the patterns are classified into two classes by a linear decision boundary. This process was repeated at each node until all the patterns are classified into individual classes. The final recognizer is accomplished by neural network learning of a set of standard patterns at each node. Binary decision tree classifier was applied to the recognition of the defect patterns of cold mill strip and the experimental results were given to demonstrate the usefulness of the proposed scheme.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.870-876
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• 1993

This paper presents a binary clustering network (BCN) and a heuristic algorithm to detect pitch for recognition of keywords in continuous speech. In order to classify nonlinear patterns, BCN separates patterns into binary clusters hierarchically and links same patterns at root level by using the supervised learning and the unsupervised learning. BCN has many desirable properties such as flexibility of dynamic structure, high classification accuracy, short learning time, and short recall time. Pitch Detection algorithm is a heuristic model that can solve the difficulties such as scaling invariance, time warping, time-shift invariance, and redundance. This recognition algorithm has shown recognition rates as high as 95% for speaker-dependent as well as multispeaker-dependent tests.

• Proceedings of the KIEE Conference
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• 1990.11a
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• pp.465-469
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• 1990

This thesis proposes a new scheme for the recognition of presegmented Hangul characters. The proposed approach is rather insensitive to noise and variation by applying 2 dimensional convolution to learning patterns. In this thesis, the hangul recognition neural network is implemented in the basis of this scheme and recognition rate is analyzed in boo cases of learning which are learning by binary patterns and learning by binary patterns and convoluted patterns together.

• Journal of Broadcast Engineering
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• v.18 no.3
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• pp.372-379
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• 2013

In this paper, we proposed a content-based image retrieval algorithm using local binary patterns and HSV color histogram. Images are retrieved using image input in image retrieval system. Many researches are based on global feature distribution such as color, texture and shape. These techniques decrease the retrieval performance in images which contained background the large amount of image. To overcome this drawback, the proposed method extract background fast and emphasize the feature of object by shrinking the background. The proposed method uses HSV color histogram and Local Binary Patterns. We also extract the Local Binary Patterns in quantized Hue domain. Experimental results show that the proposed method 82% precision using Corel 1000 database.

• ETRI Journal
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• v.32 no.5
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• pp.676-686
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• 2010

Mining sequential patterns is an important research issue in data mining and knowledge discovery with broad applications. However, the existing sequential pattern mining approaches consider only binary frequency values of items in sequences and equal importance/significance values of distinct items. Therefore, they are not applicable to actually represent many real-world scenarios. In this paper, we propose a novel framework for mining high-utility sequential patterns for more real-life applicable information extraction from sequence databases with non-binary frequency values of items in sequences and different importance/significance values for distinct items. Moreover, for mining high-utility sequential patterns, we propose two new algorithms: UtilityLevel is a high-utility sequential pattern mining with a level-wise candidate generation approach, and UtilitySpan is a high-utility sequential pattern mining with a pattern growth approach. Extensive performance analyses show that our algorithms are very efficient and scalable for mining high-utility sequential patterns.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1766-1784
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• 2014

Finger vein images contain rich oriented features. Local line binary pattern (LLBP) is a good oriented feature representation method extended from local binary pattern (LBP), but it is limited in that it can only extract horizontal and vertical line patterns, so effective information in an image may not be exploited and fully utilized. In this paper, an orientation-selectable LLBP method, called generalized local line binary pattern (GLLBP), is proposed for finger vein recognition. GLLBP extends LLBP for line pattern extraction into any orientation. To effectually improve the matching accuracy, the soft power metric is employed to calculate the matching score. Furthermore, to fully utilize the oriented features in an image, the matching scores from the line patterns with the best discriminative ability are fused using the Hamacher rule to achieve the final matching score for the last recognition. Experimental results on our database, MMCBNU_6000, show that the proposed method performs much better than state-of-the-art algorithms that use the oriented features and local features, such as LBP, LLBP, Gabor filter, steerable filter and local direction code (LDC).