• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.643-646
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• 1999

In this paper, we propose a feature extraction method based on Bhattacharyya distance for multiclass problems. The Bhattacharyya distance provides a valuable information in determining the effectiveness of a feature set and has been used as separability measure for feature selection. Recently, a feature extraction algorithm hat been proposed for two normally distributed classes based on Bhattacharyya distance. In this paper, we propose to expand the previous approach to multiclass cases. Experiment results show that the proposed method compares favorably with the conventional methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.6
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• pp.38-47
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• 2000

In pattern classification, the Bhattacharyya distance has been used as a class separability measure. Furthemore, it is recently reported that the Bhattacharyya distance can be used to estimate error of Gaussian ML classifier within 1-2% margin. In this paper, we propose a feature extraction method utilizing the Bhattacharyya distance. In the proposed method, we first predict the classification error with the error estimation equation based on the Bhauacharyya distance. Then we find the feature vector that minimizes the classification error using two search algorithms: sequential search and global search. Experimental reslts show that the proposed method compares favorably with conventional feature extraction methods. In addition, it is possible to determine how man, feature vectors arc needed for achieving the same classification accuracy as in the original space.

• Journal of the Optical Society of Korea
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• v.15 no.4
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• pp.357-361
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• 2011

Photon-counting sensing is a widely used technique for low-light-level imaging applications. This paper proposes a distance information extraction method with photon-counting passive sensing under low-lightlevel conditions. The photo-counting passive sensing combined with integral imaging generates a photon-limited elemental image array. Maximum-likelihood estimation (MLE) is used to reconstruct the photon-limited image at certain depth levels. The distance information is extracted at the depth level that minimizes the sum of the standard deviation of the corresponding photo-events in the elemental image array. Experimental and simulation results confirm that the proposed method can extract the distance information of the object under low-light-level conditions.

• Journal of the Optical Society of Korea
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• v.20 no.5
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• pp.579-585
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• 2016

Integral imaging combined with photon-counting detection has been researched for three-dimensional information sensing under low-light-level conditions. This paper addresses the extraction of distance information with photon-counting integral imaging. The longitudinal distance to the object is obtained utilizing photon-counting elemental images. The pixel disparity is estimated by maximizing the nonlinear correlation of photocounts. The first- and second-order statistical properties of the nonlinear correlation are theoretically derived. In the experiments, these properties are verified by varying the mean number of photocounts in the scene. The average distance is compared to that from the intensity information, showing the robustness of the proposed system even at low photocounts.

• The korean journal of orthodontics
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• v.21 no.1 s.33
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• pp.223-238
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• 1991

This study was carried out in order to findout the amount of tooth movement, the changes arch size and the changes in arch morphology following orthodontic treatment and to provide a guideline for to predict post-treatment arch morphology. The sample group for this study consists of 15 males and 22 females, totalling in 37 persons, who received orthodontic treatment at Orthodontic Department of Dankook Univ. Dental Hospital. They are classified into Extraction Class I treatment group (E I), Non-extraction Class I treatment group (N I), and Non-extraction Class III treatment group (N III), according to their pre-treatment malocclusion state and methods of treatment. Following conclusions and averaged dental arch form for each group were obtained by cephalometric linear measurements and dental arch measurements using pre- and post-treatment lateral cephalograms and plaster study models. 1. Intercanine width were reduced in max. of both EI and NI during the period of treatment, 2. Intermolar width were reduced in max. of EI and increased in max. of NI. Therefore although there was no difference between these two groups before the treatment, intermolar width of the max, of NI was wider than that of E1 after the treatment. 3. PMV-incisor distance and PMV-canine distance were decreased in both max. and mand. of EI and that of NI, during the period of treatment. PMV-molar distance was decreased in both max. and mand. of NI and in mand. of NIII. 4. Items that showed stability during the treatment were: max. & mand. PMV-molar distance, mand. intercanine and intermolar width in EI; mand. intercanine and intermolar width in NI; mand. & max. PMV-incisor distance, PMV-canine distance, max. PMV-molar distance and max. & mand. intercanine and intermolar width in NIII. 5. The differences in averaged canine and molar variances to post-treatment dental arch form were present only in EI and in NI. There was no variance between maxilla and mandible in each group.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2470-2491
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• 2018

Web-scale open information extraction (Open IE) plays an important role in NLP tasks like acquiring common-sense knowledge, learning selectional preferences and automatic text understanding. A large number of Open IE approaches have been proposed in the last decade, and the majority of these approaches are based on supervised learning or dependency parsing. In this paper, we present a novel method for web scale open information extraction, which employs cosine distance based on Google word vector as the confidence score of the extraction. The proposed method is a purely unsupervised learning algorithm without requiring any hand-labeled training data or dependency parse features. We also present the mathematically rigorous proof for the new method with Bayes Inference and Artificial Neural Network theory. It turns out that the proposed algorithm is equivalent to Maximum Likelihood Estimation of the joint probability distribution over the elements of the candidate extraction. The proof itself also theoretically suggests a typical usage of word vector for other NLP tasks. Experiments show that the distance-based method leads to further improvements over the newly presented Open IE systems on three benchmark datasets, in terms of effectiveness and efficiency.

• Smart Media Journal
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• v.2 no.1
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• pp.31-36
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• 2013

A robust and efficient text extraction is very important for an accuracy of Optical Character Recognition (OCR) systems. Natural scene images with degradations such as uneven illumination, perspective distortion, complex background and multi color text give many challenges to computer vision task, especially in text extraction. In this paper, we propose a method for extraction of the text in signboard images based on a combination of mean shift algorithm and weighting scheme of hue and saturation in HSI color space for clustering algorithm. The number of clusters is determined automatically by mean shift-based density estimation, in which local clusters are estimated by repeatedly searching for higher density points in feature vector space. Weighting scheme of hue and saturation is used for formulation a new distance measure in cylindrical coordinate for text extraction. The obtained experimental results through various natural scene images are presented to demonstrate the effectiveness of our approach.

• Journal of the Korean Society of Safety
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• v.26 no.5
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• pp.17-22
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• 2011

This study proposes feature extraction method of pattern recognition by evaluation of weld defects in weld zone of titanium. For this purpose, analysis objectives in this study are features of attractor quadrant and fractal dimension. Trajectory changes in the attractor indicated a substantial difference in fractal characteristics resulting from distance shifts such as porosity of weld zone. These differences in characteristics of weld defects enables the evaluation of unique characteristics of defects in the weld zone. In quantitative fractal feature extraction, feature values of 0.87 and 1.00 in the case of part of 0.5 skip distance and 0.72 and 0.93 in the case of part of 1.0 skip distance were proposed on the basis of fractal dimensions. Attractor quadrant point, feature values of 1.322 and 1.172 in the case of ${\phi}1{\times}3mm$ porosity and 2.264 and 307 in the case of ${\phi}3{\times}3mm$ porosity were proposed on the basis of distribution value. The Proposed feature extraction of pattern recognition in this study can be used for safety evaluation of weld zone in titanium.

• Journal of Korea Multimedia Society
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• v.5 no.3
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• pp.272-280
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• 2002

In this paper, we present a method to extract a moving object and to measure the distance to it by using the stereo vision system. The moving factor is to be extracted through a match of a pixel unit for the moving object where the adaptive threshold is effectively dealt with to remove changes in the brightness of the image. The distance to moving object is measured by using a stereo vision system which employs a parallel camera. The experimental results show that the proposed algorithm could be effectively applied to distance measurement to moving object because it has an average error of one percent.

• 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.