• 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.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.643-654
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• 2000

In this paper, we review some class of t-norms on which fuzzy arithmetic operations preserve the shapes of fuzzy numbers and the Hausdorff-distance between fuzzy numbers as the measure of distance between fuzzy numbers. And we suggest the least Hausdorff-distance square method for fuzzy linear regression model using shape preserving fuzzy arithmetic operations.

• The Journal of the Acoustical Society of Korea
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• v.11 no.2
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• pp.73-84
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• 1992

This paper studies noise subtraction methods and distance measures for speech recognition in a noisy environment, and investigates noise robustness of the distance measures applied to the problem of isolated word recognition in white Gaussian and colored noise (vehicle noise) environments. Noise subtraction methods which can be used as a pre-processor for the speech recognition system, such as the spectral subtraction method, autocorrelation subtraction method, adaptive noise cancellation and acoustic beamforming are studied, and distance measures such and Log Likelihood Ratio ($d_{LLR}$), cepstral distance measure ($d_{CEP}$), weighted cepstral distance measure ($d_{WCEP}$), spectral slope distance measure ($d_{RPS}$) and cepstral projection distance measure ($d_{CP},\;d_{BCP},\;d_{WCP},\;d_{BWCP}$) are also investigated. Testing of the distance measures for speaker-dependent isolated word recognition in a noisy environment indicate that $d_{RPS}\;and\;d_{WCEP}$ which weigh higher order cepstral coefficients more heavily give considerable performance improvement over $d_{CEP}and\;d_{LLR}$. In addition, when no pre-emphasis is performed, the recognizer can maintain higher performance under high noise conditions.

• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.74-80
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• 2015

In cognitive radios, spectrum sensing plays an important role in accurately detecting the presence or absence of a licensed user. However, the intervention of malicious users (MUs) degrades the performance of spectrum sensing. Such users manipulate the local results and send falsified data to the data fusion center; this process is called spectrum sensing data falsification (SSDF). Thus, MUs degrade the spectrum sensing performance and increase uncertainty issues. In this paper, we propose a method based on the Hausdorff distance and a similarity measure matrix to measure the difference between the normal user evidence and the malicious user evidence. In addition, we use the Dempster-Shafer theory to combine the sets of evidence from each normal user evidence. We compare the proposed method with the k-means and Jaccard distance methods for malicious user detection. Simulation results show that the proposed method is effective against an SSDF attack.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.343-347
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• 2010

This paper presents an efficient image recognition method using the hybrid coefficient measure of correlation and distance. The correlation coefficient is applied to measure the statistical similarity by using Pearson coefficient, and distance coefficient is also applied to measure the spacial similarity by using city-block. The total similarity among images is calculated by extending the similarity between the feature vectors, then the feature vectors can be extracted by PCA and ICA, respectively. The proposed method has been applied to the problem for recognizing the 960(30 persons * 4 expressions * 2 lights * 4 poses) facial images of 40*50 pixels. The experimental results show that the proposed method of ICA has a superior recognition performances than the method using PCA, and is affected less by the environmental influences so as lighting.

• Journal of Korea Multimedia Society
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• v.18 no.9
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• pp.1008-1018
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• 2015

In this paper, we propose a method that constructs a saliency map in which important regions are accurately specified and the colors of the regions are less influenced by the similar surrounding colors. Our method utilizes LBP(Local Binary Pattern) histogram information to compare and analyze texture information of surrounding regions in order to reduce the effect of color information. We extract the saliency of stereoscopic images by integrating a 2D saliency map with depth information of stereoscopic images. We then measure the distance between two different sizes of the LBP histograms that are generated from pixels. The distance we measure is texture difference between the surrounding regions. We then assign a saliency value according to the distance in LBP histogram. To evaluate our experimental results, we measure the F-measure compared to ground-truth by thresholding a saliency map at 0.8. The average F-Measure is 0.65 and our experimental results show improved performance in comparison with existing other saliency map extraction methods.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.4
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• pp.679-684
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• 2019

We propose a method to measure the number of specific species of class using CAFFE model and a method to measure length and width of object using stereo vision. To obtain the width of an object, the location coordinates of objects appearing on the left and right sensor is compared and the distance from the sensor to the object is obtained. Then the length of the object in the image by using the distance and the approximate value of the actual length of the object is calculated.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.625-627
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• 2005

Existent distance measurement that use camera is method that use both Stereo Camera and Monocular Camera, There is shortcoming that method that use Stereo Camera is sensitive in effect of a lot of expenses and environment variables, and method that use Monocular Camera are big computational complexity and error. In this study, reduce expense and error using Monocular Camera and I suggest algorithm that measure distance, Extract features using scale Invariant features Transform(SIFT) for distance measurement, and this measures distance through features matching and geometrical analysis, Proposed method proves measuring distance with wall by geometrical analysis free wall through feature point abstraction and matching.

• Journal of the Korean Institute of Navigation
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• v.18 no.4
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• pp.137-146
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• 1994

In this paper, a new algorithm to measure the ultrasonic travel time is proposed, which is fundamental to estimate distance depth and volume in several media. Pulse wave has been used to measure travel time of transmitted signal. However, due to the characteristic of transducer and propagation, the received signal is so distorted that it is difficult to measure travel time, which is propagation, the received signal is so distorted that it is difficult to measure travel time, which is to be time difference between transmitted and received signals. In this proposed method, transmitted and received signal are transformed respectively into norm phase newly designed by this paper and displayed on phase-time curve. And travel time is simply determined by the arithmetic numerical mean of time difference at the identical norm phase on the phase-time curves of transmitted and received signals. This method has several features; firstly, travel time is calculated analytically with high accuracy by least square error method, secondly, it is useful to compare the difference of signal magnitude for time information, thirdly, noise and discrete errors are relatively small, finally, the measurement accuracy is not influenced by D.C. bias. In particular, this method is useful and applicable to measuring very short distance and sound speed with high accuracy.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.71-76
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• 2019

Research on indoor location tracking technology using smartphone is actively being carried out. Especially, the movement distance of the smartphone should be accurately measured and the movement route of the user should be displayed on the map. Location tracking technology using sensors mounted on smart phones has been used for a long time, but accuracy is not good enough to measure the moving distance of the user using only the sensor. Therefore, when the user moves the smartphone in a certain posture, it must research and develop an appropriate algorithm to measure the distance accurately. In this paper, we propose a method to reduce moving distance estimation error by removing user 's foot shape by limiting the screen of smartphone in pyramid - based optical flow estimation method.