• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.3
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• pp.322-326
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• 2004

This paper describes a new iris recognition method based on a shift-invariant wavelet sub-images. For the feature representation, we first preprocess an iris image for the compensation of the variation of the iris and for the easy implementation of the wavelet transform. Then, we decompose the preprocessed iris image into multiple subband images using a shift-invariant wavelet transform. For feature representation, we select a set of subband images, which have rich information for the classification of various iris patterns and robust to noises. In order to reduce the size of the feature vector, we quantize. each pixel of subband images using the Lloyd-Max quantization method Each feature element is represented by one of quantization levels, and a set of these feature element is the feature vector. When the quantization is very coarse, the quantized level does not have much information about the image pixel value. Therefore, we define a new similarity measure based on mutual information between two features. With this similarity measure, the size of the feature vector can be reduced without much degradation of performance. Experimentally, we show that the proposed method produced superb performance in iris recognition.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.23
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• pp.10539-10542
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• 2015

A method to predict the risk of lung cancer is proposed, based on two feature selection algorithms: Fisher and ReliefF, and BP Neural Networks. An appropriate quantity of risk factors was chosen for lung cancer risk prediction. The process featured two steps, firstly choosing the risk factors by combining two feature selection algorithms, then providing the predictive value by neural network. Based on the method framework, an algorithm LCRP (lung cancer risk prediction) is presented, to reduce the amount of risk factors collected in practical applications. The proposed method is suitable for health monitoring and self-testing. Experiments showed it can actually provide satisfactory accuracy under low dimensions of risk factors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.11
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• pp.2776-2788
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• 1996

A new feature-based motion parameter estimation for arbitrary-shaped regions is proposed. Existing motion parameter estimation algorithms such as gradient-based algorithm require iterations that are very sensitive to initial values and which often converge to a local minimum. In this paper, the motion parameters of an object are obtained by solving a set of linear equations derived by the motion of salient feature points of the object. In order to estimate the displacement of the feature points, a new process called the "bi-directional correspondence scheme" is proposed to ensure the robjstness of correspondence. The proposed correspondence scheme iteratively selects the feature points and their corresponding points until unique one-to-one correspondence is established. Furthermore, initially obtained motion paramerters are refined using an iterative method to give a better performance. The proposed algorithm can be used for motion estimationin object-based image coder, and the experimental resuls show that the proposed method outperforms existing schemes schemes in estimating motion parameters of objects in image sequences.sequences.

• Journal of Industrial Technology
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• v.28 no.B
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• pp.143-148
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• 2008

A real-time face detection is to find human faces robustly under the cluttered background free from the effect of occlusion by other objects or various lightening conditions. We propose a face detection system for real-time applications using wavelet decomposition method based on Gabor features. Firstly, skin candidate regions are extracted from the given image by skin color filtering and projection method. Then Gabor-feature based template matching is performed to choose face cadidate from the skin candidate regions. The chosen face candidate region is transformed into 2-level wavelet decomposition images, from which feature vectors are extracted for classification. Based on the extracted feature vectors, the face candidate region is finally classified into either face or nonface class by the Levenberg-Marguardt back-propagation neural network.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.666-670
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• 2006

This paper presents the novel feature selection method for Emotion Recognition, which may include a lot of original features. Specially, the emotion recognition in this paper treated speech signal with emotion. The feature selection has some benefits on the pattern recognition performance and 'the curse of dimension'. Thus, We implemented a simulator called 'IFS' and those result was applied to a emotion recognition system(ERS), which was also implemented for this research. Our novel feature selection method was basically affected by Reinforcement Learning and since it needs responses from human user, it is called 'Interactive feature Selection'. From performing the IFS, we could get 3 best features and applied to ERS. Comparing those results with randomly selected feature set, The 3 best features were better than the randomly selected feature set.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.11
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• pp.148-158
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• 1995

This paper describes a hierarchical structure for feature definition and classification, and feature representation method based on frame structure for process planning of prismatic machined components of injection mold. The concept of Volume Removal Directions and Vertical Faces is proposed to develop a method to define and to classify features for components of injection mold systematically. A method for classifying features by the combination of volume removal directions and vertical faces is developed, and also a feature representation method by using frame structure to represent design and manufacturing information is presented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.733-744
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• 2016

The legacy methods for converting a business model to a feature model make it difficult to support an automatic transformation due to a dependence on a domain analyzers' intuitions, which hinders the feature oriented development for the integration of feature modeling in business modeling. This paper proposes a method for converting a BPMN business model into a feature model based on syntax. To allow the conversion between the heterogeneous models from BPMN to the FM(Feature Model), it defines the grouping mechanism based activities' syntax, and then makes translation rules and a method based on the element (represent business function) and structure (relationships and process among elements), which are common constructs of their models. This method was applied to an online shopping mall system as a case study. With this mechanism, it will help develop a mechanical or automated structure transformation from the BPMN model to the FM.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.2
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• pp.44-49
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• 2015

When the scene has less texture or when camera pose largely changes, the existing texture-based feature tracking methods are not reliable. Topology-based feature description methods, which use the geometric relationship between features such as LLAH, is a good alternative. However, they require feature detection methods with high performance. As a basic study on developing an effective feature detection method for topology-based feature description, this paper aims at examining their applicability to topology-based feature description by analyzing the repeatability of several feature detection methods that are included in the OpenCV library. Experimental results show that FAST outperforms the others.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.69-72
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• 2001

This paper proposes a positional estimation method for extracted line features by stereo vision. Based on given reference plane, planar surfaces corresponding the given plane are first extracted. Then, features in the planar surfaces are selected. Using the Extended Kalman Filter the feature positions are estimated by combining area- and feature-based stereo matching results. Experimental results show the proposed method is feasible.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.6
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• pp.1279-1288
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• 1997

In this paper, we propose a fast fractal coding method based on LMSE analysis and subblock feature. In the proposed method, scaling paarameter is calculated and whether search for each domain block should be done or not is determined based on the LMSE analysis of fractal approximation, and isometry parameter is chosen based on subblock feature. To investigate the efficiency of the proposed method, we compared it with Jacquin's method on image quality and encoding time. Experimental results show the proposed method yields nearly the same performance as that of Jacquin method in PSNR, and its encoding time is reduced by about 1/7 times.