• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.263-266
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• 2000

We proposes a new fingerprint minutia matching algorithm which matches the fingerprint minutiae by using adaptive distance. In general, fingerprint is deformed by pressure and orientation when a user press his fingerprint to sensor. These nonlinear deformations change the distance between minutiae and reduce verification rate. We define the adaptive distance using ridge frequency. Adaptive distance normalizes the distance between minutiae and compensates for nonlinear deformation. Our algorithm can distinguish two different fingerprints better and is more robust. Experimental results show that the performance of the proposed algorithm is superior to using Euclidean distance.

• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.298-304
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• 2021

Currently, to prevent the spread of COVID-19 virus infection, gathering of more than 5 people in the same space is prohibited. The purpose of this paper is to measure the distance between objects using the Yolov5 model for processing real-time images with OpenCV in order to restrict the distance between several people in the same space. Also, Utilize Euclidean distance calculation method in DeepSORT and OpenCV to minimize occlusion. In this paper, to detect the distance between people, using the open-source COCO dataset is used for learning. The technique used here is using the YoloV5 model to measure the distance, utilizing DeepSORT and Euclidean techniques to minimize occlusion, and the method of expressing through visualization with OpenCV to measure the distance between objects is used. Because of this paper, the proposed distance measurement method showed good results for an image with perspective taken from a higher position than the object in order to calculate the distance between objects by calculating the y-axis of the image.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.352-354
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• 2022

Position measurement using a camera has been studied for a long time. This is being studied for distance recognition or object recognition in autonomous vehicles, and it is being studied in the field of indoor navigation, which is a limited space where GPS is difficult to apply. In general, in a method of measuring the distance using a camera, the distance is measured using a distance between the cameras using two stereo cameras and a value measured through a captured image or photo. In this paper, we propose a method of measuring the distance of an object using a single camera. The proposed method measures the distance by using the distance between cameras, such as a stereo camera, and the value measured by the photographed picture through the gap of the photographing time and the distance between photographing.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.321-322
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• 2008

CSS(Chirp Spread Spectrum) technology adopt SDS-TWR algorithms of TOA(Time of Arrival) using velocity of specific medium and ToF(Time of Flight) to measure a distance, but this method always has a regular error on distance regardless of a real distance, as a result, in far distance, it decrease a error on distance relatively, but in near distance, it increase a error on distance relatively. in this paper, we propose and test new method measuring a distance more precisely in near distance using CSS and RSSI

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.137-145
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• 2014

This paper suggests an LDA-based long distance face recognition algorithm for intelligent surveillance system. The existing face recognition algorithm using single distance face image as training images caused a problem that face recognition rate is decreased with increasing distance. The face recognition algorithm using face images by actual distance as training images showed good performance. However, this also causes user inconvenience as it requires the user to move one to five meters in person to acquire face images for initial user registration. In this paper, proposed method is used for training images by using single distance face image to automatically create face images by various distances. The test result showed that the proposed face recognition technique generated better performance by average 16.3% in short distance and 18.0% in long distance than the technique using the existing single distance face image as training. When it was compared with the technique that used face images by distance as training, the performance fell 4.3% on average at a close distance and remained the same at a long distance.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.3
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• pp.1-7
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• 2022

In this study, inaccurate RSSI values of beacons are corrected using extended Kalman filter. For the experiment, the beacon was manufactured using Arduino Uno board and HM-10 Bluetooth module. RSSI values according to the distance between beacon and the viewer were measured at intervals of 1m, 1.5m, 2m, 2.5m, 3m, 3.5m, 4m, 4.5m, and 5m. To remove the irregular signal pattern of the beacon, the extended Kalman filter was applied to obtain the average and standard deviation of the actual distance and the measured distance, and it was confirmed that more than 76.6% of the irregular signal pattern was removed after using the extended Kalman filter.In addition, through the smartphone app, it was confirmed that the distance accuracy between the beacon and the measurer was less than the actual distance and the measured distance within 2m, and the standard deviation was small.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.3
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• pp.183-192
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• 2003

This paper presents the real-time implemented distance relay algorithm which the fault distance is estimated with only local terminal information. When a single-phase-to-earth fault on a two-parallel transmission line occurs, the reach accuracy of distance relay is considerably affected by the unknown variables which are fault resistance, fault current at the fault point and zero- sequence current of sound line The zero-sequence current of sound line is estimated by using the zero sequence voltage which is measured by relaying location Also. the fault resistance is removed at the Process of numerical formula expansion. Lastly, the fault current through a fault point is expressed as a function of the zero-sequence current of fault line, zero-sequence current of sound line, and line, and fault distance. Therefore, the fault phase voltage can be expressed as the quadratic equation of the fault distance. The solution of this Quadratic equation is obtained by using a coefficient of the modified quadratic equation instead of using the square root solution method. After tile accurate fault distance is estimated. the mote accurate impedance is measured by using such an information.

• Korean Journal of Optics and Photonics
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• v.17 no.6
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• pp.537-543
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• 2006

A method of estimating the distance using single zoom camera limits a distance range(only optical axis) in field of view. So, in this paper, we propose a method of estimating the distance information in Stereoscopic display using the stereo zoom lens module for estimating the distance in the wide range. The binocular stereo zoom lens system is composed using a horizontal moving camera module. The left and right images are acquired in polarized stereo monitor for getting the conversion and estimating a distance. The error distance is under 10mm which has difference between optically a traced distance and an estimated distance in left and right range $(0mm{\sim}500mm)$ at center. This presents the system using a function of the zoom and conversion has more precise distance information than that of conversion control. Also, a method of estimating a distance from horizontal moving camera is more precise value than that from toe-in camera by comparing the error distance of the two camera methods.

• Korean Journal of Computational Design and Engineering
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• v.15 no.2
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• pp.115-123
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• 2010

We present an efficient algorithm for computing the Hausdorff distance between two planar curves. The algorithm is based on an efficient trimming technique that eliminates the curve domains that make no contribution to the final Hausdorff distance. The input curves are first approximated with biarcs within a given error bound in a pre-processing step. Using the biarc approximation, the distance map of an input curve is then approximated and stored into the graphics hardware depth-buffer by rendering the distance maps (represented as circular cones) of the biarcs. We repeat the same procedure for the other input curve. By sampling points on each input curve and reading the distance from the other curve (stored in the hardware depth-buffer), we can easily estimate a lower bound of the Hausdorff distance. Based on the lower bound, the algorithm eliminates redundant curve segments where the exact Hausdorff distance can never be obtained. Finally, we employ a multivariate equation solver to compute the Hausdorff distance efficiently using the remaining curve segments only.

• Proceedings of the Optical Society of Korea Conference
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• 2007.02a
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• pp.125-126
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• 2007

This paper discussed a method for estimating the distance using the stereo zoom lens module. A method for estimating the distance using single zoom lens is limited in convergence angle. So, we composed horizontal moving camera and estimated the distance. In the experiment, we get the wider convergence angle and more precise distance than single camera's.