• Journal of Information Processing Systems
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• v.8 no.3
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• pp.421-436
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• 2012

Different types of fingerprint detection algorithms that are based on extraction of minutiae points are prevalent in recent literature. In this paper, we propose a new algorithm to locate the virtual core point/centroid of an image. The Euclidean distance between the virtual core point and the minutiae points is taken as a random variable. The mean, variance, skewness, and kurtosis of the random variable are taken as the statistical parameters of the image to observe the similarities or dissimilarities among fingerprints from the same or different persons. Finally, we verified our observations with a moment parameter-based analysis of some previous works.

• Korean Journal of Remote Sensing
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• v.11 no.1
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• pp.47-64
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• 1995

In this paper, an algorithm that finds man-made structures in a praylevel aerial images is proposed to perform stereo matching. An extracted contour of buildings must have a high accuracy in order to get a good feature-based stereo matching result. Therefore this study focuses on the use of edge following in the original image rather than use of ordinary edge filters. The Algorithm is composed of two main categories; one is to find candidate regions in the whole image and the other is to extract exact contours of each building which each candidate region.. The region growing method using the centroid linkage method of variance value is used to find candidate regions of building and the contour line tracing algorithm based on an adge following method is used to extract exact contours. The result shows that the almost contours of building composed of line segments are extracted.

• The Journal of the Acoustical Society of Korea
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• v.23 no.7
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• pp.532-539
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• 2004

In this Paper, we propose a content-based music information retrieval (MIR) system base on the query-by-example (QBE) method. The proposed system is implemented to retrieve queried music from a dataset where 60 music samples were collected for each of the four genres in Classical, Hiphop. Jazz. and Reck. resulting in 240 music files in database. From each query music signal, the system extracts 60 dimensional feature vectors including spectral centroid. rolloff. flux base on STFT and also the LPC. MFCC and Beat information. and retrieves queried music from a trained database set using Euclidean distance measure. In order to choose optimum features from the 60 dimension feature vectors, SFS method is applied to draw 10 dimension optimum features and these are used for the Proposed system. From the experimental result. we can verify the superior performance of the proposed system that provides success rate of 84% in Hit Rate and 0.63 in MRR which means near 10% improvements over the previous methods. Additional experiments regarding system Performance to random query Patterns (or portions) and query lengths have been investigated and a serious instability problem of system Performance is Pointed out.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.11
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• pp.147-155
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• 2015

For the image processing system to be classified the selected object in the nature, the rotation, scale and transition invariant features is to be necessary. There are many investigations to get the information for the object processing system and the log-polar transform which is to be get the invariant feature for the scale and rotation is used. In this paper, we suggested the 2 points polar coordinate transform methods to measure the selected object position out of the center in input image including the centroid method. In this proposed system, the position results of objects are very good, and we obtained the similarity ratio 99~104% for the object coordinate values.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.553-559
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• 2020

In order to prevent subsidence in urban areas, which is a major cause of damage to aging underground facilities, an integrated underground space map is being produced for systematic management of underground facilities. However, there is a problem of delaying the update time because an unupdated underground facility object is included in the process of updating the underground space integrated map. In this paper, we proposed a method to shorten the update time of the integrated map by selecting only the updated objects required for the update process of the underground space integrated map based on the central point of the underground facilities. Through the comparison of the centroid, the number of search targets is greatly reduced to shorten the search speed, and the distance of the actual location values between the two objects is calculated whether or not the objects are the same. The proposed method shows faster performance as the number of data increases, and the updated object can be reflected in the underground space integrated map about four times faster than the existing method.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.4
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• pp.551-559
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• 2017

In this paper, a real-time training method to improve the performance of bearing fault diagnosis. The traditional bearing fault diagnosis cannot classify a condition which is not trained by the classifier. The proposed 4-step method trains and recognizes new condition in real-time, thereby it can classify the condition accurately. In the first step, we calculate the maximum distance value for each class by calculating a Euclidean distance between a feature vector of each class and a centroid of the corresponding class in the training information. In the second step, we calculate a Euclidean distance between a feature vector of new acquired data and a centroid of each class, and then compare with the allowed maximum distance of each class. In the third step, if the distance between a feature vector of new acquired data and a centroid of each class is larger than the allowed maximum distance of each class, we define that it is data of new condition and increase count of new condition. In the last step, if the count of new condition is over 10, newly acquired 10 data are assigned as a new class and then conduct re-training the classifier. To verify the performance of the proposed method, bearing fault data from a rotating machine was utilized.

• The Journal of Engineering Geology
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• v.32 no.3
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• pp.377-388
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• 2022

Recently, several studies have utilized machine learning to efficiently and accurately analyze seismic data that are exponentially increasing. In this study, we expand earthquake information such as occurrence time, hypocentral location, and magnitude to produce a dataset for applying to machine learning, reducing the dimension of the expended data into dominant features through principal component analysis. The dimensional extended data comprises statistics of the earthquake information from the Global Centroid Moment Tensor catalog containing 36,699 seismic events. We perform data preprocessing using standard and max-min scaling and extract dominant features with principal components analysis from the scaled dataset. The scaling methods significantly reduced the deviation of feature values caused by different units. Among them, the standard scaling method transforms the median of each feature with a smaller deviation than other scaling methods. The six principal components extracted from the non-scaled dataset explain 99% of the original data. The sixteen principal components from the datasets, which are applied with standardization or max-min scaling, reconstruct 98% of the original datasets. These results indicate that more principal components are needed to preserve original data information with even distributed feature values. We propose a data processing method for efficient and accurate machine learning model to analyze the relationship between seismic data and seismic behavior.

• Journal of Korea Multimedia Society
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• v.5 no.3
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• pp.255-262
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• 2002

A boundary sequence can be a good representation of arbitrary shapes, because it can represent them simply and precisely. However, boundary sequences have not been used as a representation of arbitrary shapes, because the pixel-based shape-features such as area, centroid, orientation, projection and so forth, could not be computed directly from them. In this paper, we show that the shape-features can be easily computed from the boundary sequences by introducing the cross-sections that are defined as vertical (or horizontal) line segments in a shape. A cross-section generation method is proposed, which generates cross-sections of the shape efficiently by tracing the boundary sequence of the shape once. Furthermore, a boundary sequence extraction method is also proposed, which generates a boundary sequence for each shape in a binary image automatically The proposed methods work well even if a shape has holes. Eventually, we show that a boundary sequence can be used effectively for representing arbitrary shapes.

• Current Optics and Photonics
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• v.8 no.1
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• pp.38-44
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• 2024

Under the influence of atmospheric turbulence, a star's point image will shake back and forth erratically, and after exposure the originally small star point will spread into a huge spot, which will affect the ground calibration of the star sensor. To analyze the impact of atmospheric turbulence on the positioning accuracy of the star's center of mass, this paper simulates the atmospheric turbulence phase screen using a method based on a sparse spectrum. It is added to the static-star-simulation device to study the transmission characteristics of atmospheric turbulence in star-point simulation, and to analyze the changes in star points under different atmospheric refractive-index structural constants. The simulation results show that the structure function of the atmospheric turbulence phase screen simulated by the sparse spectral method has an average error of 6.8% compared to the theoretical value, while the classical Fourier-transform method can have an error of up to 23% at low frequencies. By including a simulation in which the phase screen would cause errors in the center-of-mass position of the star point, 100 consecutive images are selected and the average drift variance is obtained for each turbulence scenario; The stronger the turbulence, the larger the drift variance. This study can provide a basis for subsequent improvement of the ground-calibration accuracy of a star sensitizer, and for analyzing and evaluating the effect of atmospheric turbulence on the beam.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.10
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• pp.520-529
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• 2005

A feature-based 3D modeling algorithm is presented in this paper. Since conventional methods use depth-based techniques, they need much time for the image matching to extract depth information. Even feature-based methods have less computation load than that of depth-based ones, the calculation of modeling error about whole pixels within a triangle is needed in feature-based algorithms. It also increase the computation time. Therefore, the proposed algorithm consists of three phases, which are an initial 3D model generation, model evaluation, and model refinement phases, in order to acquire an efficient 3D model. Intensity gradients and incremental Delaunay triangulation are used in the Initial model generation. In this phase, a morphological edge operator is adopted for a fast edge filtering, and the incremental Delaunay triangulation is modified to decrease the computation time by avoiding the calculation errors of whole pixels and selecting a vertex at the near of the centroid within the previous triangle. After the model generation, sparse vertices are matched, then the faces are evaluated with the size, approximation error, and disparity fluctuation of the face in evaluation stage. Thereafter, the faces which have a large error are selectively refined into smaller faces. Experimental results showed that the proposed algorithm could acquire an adaptive model with less modeling errors for both smooth and abrupt areas and could remarkably reduce the model acquisition time.