• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.5
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• pp.696-702
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• 2014

Recently, researches on analyzing relationship between the state of emotion and musical stimuli using EEG are increasing. A selection of feature vectors is very important for the performance of EEG pattern classifiers. This paper proposes a comparison of EEG feature vectors for emotion classification according to music listening. For this, we extract some feature vectors like DAMV, IAV, LPC, LPCC from EEG signals in each class related to music listening and compare a separability of the extracted feature vectors using Bhattacharyya distance. So more effective feature vectors are recommended for emotion classification according to music listening.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.4
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• pp.217-221
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• 2013

Recently, researches for methods of generating binary feature descriptors have been actively done. Since matching of binary feature descriptors uses Hamming distance which is based on bit operations, it is much more efficient than that of previous general feature descriptors which uses Euclidean distance based on real number operations. However, since increase in the number of features linearly drops matching speed, in applications such as object tracking where real-time applicability is a must, there has been an increasing demand for methods of further improving the matching speed of binary feature descriptors. In this regard, this paper proposes a method that improves the matching speed greatly while maintaining the matching accuracy by splitting high dimensional binary feature descriptors to several low dimensional ones and sequentially analyzing their partial Hamming distances. To evaluate the efficiency of the proposed method, experiments of comparison with previous matching methods are conducted. In addition, this paper discusses schemes of generating binary feature descriptors for maximizing the performance of the proposed method. Based on the analysis on the performance of several generation schemes, we try to find out the most effective scheme.

• Genomics & Informatics
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• v.18 no.1
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• pp.7.1-7.7
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• 2020

In this paper, we present few technical notes about the distance distribution paradigm for Mosaab-metric using 1, 2, and 3 grams feature extraction techniques to analyze composite data points in high dimensional feature spaces. This technical analysis will help the specialist in bioinformatics and biotechnology to deeply explore the biodiversity of influenza virus genome as a composite data point. Various technical examples are presented in this paper, in addition, the integrated statistical learning pipeline to process segmented genomes of influenza virus is illustrated as sequential-parallel computational pipeline.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.6
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• pp.10-21
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• 2000

The Cellular Noninear Networks structure for Distance Transform(DT) and the robust optical flow detection algorithm based on the DT are proposed. For some applications of optical flows such as target tracking and camera ego-motion computation, correct optical flows at a few feature points are more useful than unreliable one at every pixel point. The proposed algorithm is for detecting the optical flows on the trajectories only of the feature points. The translation lengths and the directions of feature movements are detected on the trajectories of feature points on which Distance Transform Field is developed. The robustness caused from the use of the Distance Transform and the easiness of hardware implementation with local analog circuits are the properties of the proposed structure. To verify the performance of the proposed structure and the algorithm, simulation has been done about various images under different noisy environment.

• Asia Marketing Journal
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• v.17 no.1
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• pp.97-121
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• 2015

In this research, we examine whether and why temporal distance influences evaluations of two different types of brand extensions: concept-based extensions, defined as extensions primarily based on the importance or relevance of brand concepts to extension products; and similarity-based extensions, defined as extensions primarily based on the amount of feature similarity at the product-category level. In Study 1, we test the hypothesis that concept-based extensions are evaluated more favorably when they are framed to launch in the distant rather than in the near future, whereas similaritybased extensions are evaluated more favorably when they are framed to launch in the near rather than in the distant future. In Study 2, we confirm that this time-dependent differential evaluation is driven by the difference in construal level between the bases of the two types of extensions - i.e., brand-concept consistency and product-category feature similarity. As such, we find that conceptbased extensions are evaluated more favorably under the abstract than concrete mindset, whereas similarity-based extensions are evaluated more favorably under the concrete than abstract mindset. In Study 3, we extend to the case for a broad brand (i.e., brands that market products across multiple categories), finding that making accessible a specific product category of a broad parent brand influences evaluations of near-future, but not distant-future, brand extensions. Combined together, our findings suggest that temporal distance influences brand extension evaluation through its effect on the importance placed on brand concepts and feature similarity. That is, consumers rely on different bases to evaluate brand extensions, depending on their perception of when the extensions take place and on under what mindset they are placed. This research makes theoretical contributions to the brand extension research by identifying one important determinant to brand extension evaluation and also uncovering its underlying dynamics. It also contributes to expanding the scope of the construal level theory by putting forth a novel interpretation of two bases of perceived fit in terms of construal level. Marketers who are about to launch and advertise brand extensions may benefit by considering temporal-distance information in determining what content to deliver about extensions in their communication efforts. Conceptual relation of a parent brand to extensions needs to be emphasized in the distant future, whereas feature similarity should be highlighted in the near future.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.3
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• pp.21-26
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• 2003

A new method to realize 3-dimensional object pattern recognition system using Fourier-based feature extractor has been proposed. The procedure to obtain the invariant feature vector is as follows ; A closed surface is generated by tracing the surface of object using the 3-dimensional polar coordinate. The centroidal distances between object's geometrical center and each closed surface points are calculated. The distance vector is translation invariant. The distance vector is normalized, so the result is scale invariant. The Fourier spectrum of each normalized distance vector is calculated, and the spectrum is rotation invariant. The Fourier-based feature generating from above procedure completely eliminates the effect of variations in translation, scale, and rotation of 3-dimensional object with closed-surface. The experimental results show that the proposed method has a high accuracy.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.4
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• pp.266-274
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• 2003

Distance Fourier descriptors of local target boundary and feature information fusion using multiple MLPs (Multilayer perceptrons) are proposed. They are used to identify nonoccluded and partially occluded targets in natural FLIR (forward-looking infrared) images. After segmenting a target, radial Fourier descriptors as global shape features are defined from the target boundary. A target boundary is partitioned into four local boundaries to extract local shape features. In a local boundary, a distance function is defined from boundary points and a line between two extreme points. Distance Fourier descriptors as local shape features are defined by using distance function. One global feature vector and four local feature vectors are used as input data for multiple MLPs to determine final identification result of the target. In the experiments, we show that the proposed method is superior to the traditional feature sets with respect to the identification performance.

• Journal of the Korea Society of Computer and Information
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• v.15 no.6
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• pp.73-80
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• 2010

Vocabulary recognition system is providing inaccurate vocabulary and similar phoneme recognition due to reduce recognition rate. It's require method of similar phoneme recognition unrecognized and efficient feature extraction process. Therefore in this paper propose phoneme likelihood error correction improvement system using based on phoneme feature Bhattacharyya distance measurement. Phoneme likelihood is monophone training data phoneme using HMM feature extraction method, similar phoneme is induced recognition able to accurate phoneme using Bhattacharyya distance measurement. They are effective recognition rate improvement. System performance comparison as a result of recognition improve represent 1.2%, 97.91% by Euclidean distance measurement and dynamic time warping(DTW) system.

• Journal of Korea Multimedia Society
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• v.18 no.9
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• pp.1039-1046
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• 2015

We propose a feature generation method for low-resolution face recognition. For this, we first generate new features from the input features (pixels) of a low-resolution face image by adding the higher-order terms. Then, we evaluate the separability of both of the original input features and new features by computing the discriminant distance of each feature. Finally, new data sample used for recognition consists of the features with high separability. The experimental results for the FERET, CMU-PIE and Yale B databases show that the proposed method gives good recognition performance for low-resolution face images compared with other method.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.11
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• pp.103-111
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• 2002

In this study, the natural flaws in welding parts are classified using the signal pattern classification method. The storage digital oscilloscope including FFT function and enveloped waveform generator is used and the signal pattern recognition procedure is made up the digital signal processing, feature extraction, feature selection and classifier design. It is composed with and discussed using the distance classifier that is based on euclidean distance the empirical Bayesian classifier. feature extraction is performed using the class-mean scatter criteria. The signal pattern classification method is applied to the signal pattern recognition of natural flaws.