• Korean Journal of Remote Sensing
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• v.16 no.4
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• pp.347-353
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• 2000

The purpose of this paper is to extract DEM (Digital Elevation Model) using KOMPSAT images. DEM extraction consists of three parts. First part is the modeling of satellite position and attitude, second part is the matching of two images to find corresponding points of them and third part is to calculate the elevation of each point by using the result of the first and second part. The position and attitude modeling of satellite is processed by using GCPs. Area based matching method is used to find the corresponding points between the stereo satellite images. The elevation of each point is calculated using the exterior orientation information obtained from sensor modeling and the disparity from the stereo matching. In experiment, the KOMPSAT images, 2592$\times$2796 panchromatic images are used to extract DEM. The experiment result show the DEM using KOMPSAT images.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.3
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• pp.471-479
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• 2019

This research proposes a indoor magnetic map matching algorithm that improves the position accuracy by employing multiple magnetic sensors and probabilistic candidate weighting function. Since the magnetic field is easily distorted by the surrounding environment, the distorted magnetic field can be used for position mapping, and multiple sensor configuration is useful to improve mapping accuracy. Nevertheless, the position error is likely to increase because the external magnetic disturbances have repeated pattern in indoor environment and several points have similar magnetic field distortion characteristics. Those errors cause large position error, which reduces the accuracy of the position detection. In order to solve this problem, we propose a method to reduce the error using multiple sensors and likelihood boundaries that uses human walking characteristics. Also, to reduce the maximum position error, we propose an algorithm that weights according to their importance. We performed indoor walking tests to evaluate the performance of the algorithm and analyzed the position detection error rate and maximum distance error. From the results we can confirm that the accuracy of position detection is greatly improved.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.5
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• pp.453-463
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• 2001

This paper presents an absolute position estimation method using Indian remote sensing (IRS) satellite images, which is a part of a position estimation (PE) system. The accumulated buffer (AB) matching method is proposed, in which a set of accumulator cells is employed for fast edge-based matching. By computer simulations with two sets of veal aerial image sequences, the performance of the AB matching method is analyzed and its effectiveness is shown in terms of the position error in the hybrid PE system.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.1
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• pp.94-99
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• 2007

In this paper, a 2-dimensional edge-based matching algorithm is proposed that combines the generalized Hough transform (GHT) and the Chamfer matching to complement weakness of either method. First, the GHT is used to find approximate object positions and orientations, and then these positions and orientations are used as starling parameter values to find more accurate position and orientation using the Chamfer matching. Finally, matching accuracy is further refined by using a subpixel algorithm. The algorithm was implemented and successfully tested on a number of images containing various electronic components.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.17-20
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• 2000

This paper describes a shape-based object recognition algorithm using multiple distance images. For the images containing dense edge points and noise, previous Hausdorff distance (HD) measures yield a high ms error for object position and many false matchings for recognition. Extended version of HD measure considering edge position and orientation simultaneously is proposed for accurate matching. Multiple distance images are used to calculate proposed matching measure efficiently. Results are presented for visual images and infrared images.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.9
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• pp.54-63
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• 1993

Quantization error, generated by the quantization process of an image, is inherent in computer vision. Because, especially in stereo vision, the quantization error in a 2-D image results in position errors in the reconstructed 3-D scene, it is necessary to analyze it mathematically. In this paper, the analysis of the probability density function (pdf) of quantization error for a line-based stereo matching scheme is presented. We show that the theoretical pdf of quantization error in the reconstructed 3-D position information has more general form than the conventional analysis for pixel-based stereo matching schemes. Computer simulation is observed to surpport the theoretical distribution.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.170-173
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• 2001

To apply stereo vision system to obstacle recognition system of AGV, we constructed algorithm of stereo matching and distance measuring with stereo image for positioning of object in area. And using this system, we look into the error between real position and measured position, and studied relationship of compensation.

• Radiation Oncology Journal
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• v.37 no.2
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• pp.134-142
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• 2019

Purpose: The aim is to study the dependence of deformable based auto-segmentation of head and neck organs-at-risks (OAR) on anatomy matching for a single atlas based system and generate an acceptable set of contours. Methods: A sample of ten patients in neutral neck position and three atlas sets consisting of ten patients each in different head and neck positions were utilized to generate three scenarios representing poor, average and perfect anatomy matching respectively and auto-segmentation was carried out for each scenario. Brainstem, larynx, mandible, cervical oesophagus, oral cavity, pharyngeal muscles, parotids, spinal cord, and trachea were the structures selected for the study. Automatic and oncologist reference contours were compared using the dice similarity index (DSI), Hausdroff distance and variation in the centre of mass (COM). Results: The mean DSI scores for brainstem was good irrespective of the anatomy matching scenarios. The scores for mandible, oral cavity, larynx, parotids, spinal cord, and trachea were unacceptable with poor matching but improved with enhanced bony matching whereas cervical oesophagus and pharyngeal muscles had less than acceptable scores for even perfect matching scenario. HD value and variation in COM decreased with better matching for all the structures. Conclusion: Improved anatomy matching resulted in better segmentation. At least a similar setup can help generate an acceptable set of automatic contours in systems employing single atlas method. Automatic contours from average matching scenario were acceptable for most structures. Importance should be given to head and neck position during atlas generation for a single atlas based system.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.208-211
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• 2004

Landmark matching is one of an important algorithm for navigation of satellite images. This paper proposes a fast landmark matching algorithm using a MGLI (Moving Guide-Line Image). For searching the matched point between the landmark chip and a part of image, correlation matrix is used generally, but the full-sized correlation matrix has a drawback requiring plenty of time for matching point calculation. MGLI includes thick lines for fast calculation of correlation matrix. In the MGLI, width of the thick lines should be determined by satellite position changes and navigation error range. For the fast landmark matching, the MGLI provides guided line for a landmark chip we want to match, so that the proposed method should reduce candidate areas for correlation matrix calculation. This paper will show how much time is reduced in the proposed fast landmark matching algorithm compared to general ones.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.493-498
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• 2007

In this paper, a 2-dimensional accurate edge-based matching algorithm using subpixel edges is proposed that combines the Generalized Hough Transform(GHT) and the Chamfer matching to complement the weakness of either method. First, the GHT is used to find the approximate object positions and orientations, and then these positions and orientations are used as starting parameter values to find more accurate position and orientation using the Chamfer matching with distance interpolation. Finally, matching accuracy is further refined by using a subpixel algorithm. Testing results demonstrate that greater matching accuracy is achieved using subpixel edges rather than edge pixels.