• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.297-300
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• 2003

In this paper, fingerprint minutiae mosaicking algorithm using minutiae of fingerprint is proposed. First, minutiae map is generated from minutiae of fingerprint and minutiae constellation is generated from fingerprint minutiae map. Minutiae constellation is constellation-shaped structure generated from Voronoi Diagram and Delaunay Triangulation using information of minutiae. Secondly, common region is detected by similarity of minutiae constellation of fingerprint minutiae map and minutiae map of individual fingerprint image is composed. Consequently composite minutiae map by mosaicking of fingerprint minutiae improve the performance of the fingerprint matching system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.2C
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• pp.121-128
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• 2003

In this paper, we propose a new disparity estimation method called object based block matching algorithm(OBMA) for stereoscopic images which is able to reduce the blocking artifact. In the proposed algorithm, edge information of the given image is first extracted and then we estimate the disparity of each segmented object to remove the blocking artifact. In the experimental results, it is proven that the proposed algorithm has about the same performance as the old BMA algorithm while it achieves much better subjective quality.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.545-548
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• 2008

본 논문은 지도정보를 이용하여 옥내측위 결과를 보정하는 방법을 제안한다. 다양한 옥내측위 관련 연구 결과가 소개되었으나, 지금까지 발표되고 진행되는 연구는 측위의 정확도를 제고하는 방안에 중점을 두고 있다. 그러나 정확도를 아무리 제고하여도 오차를 완전히 제거하기는 불가능하다. 따라서 본 연구는 지도정보를 이용하여 옥내측위 결과를 보정하는 방법을 제안하는 것이다. GPS로 예측한 자동차 궤적을 보정하는 방법으로 map-matching 방법이 널리 연구되었다. 제안하는 방법은 두 선분이 교차하는지 검사하는 함수를 이용하여, 이동단말기가 장애물을 통과하여 움직이는 상황을 나타내는 측위 결과를 실시간으로 즉시 보정한다는 점에서 map-matching 방법과 다르다. 제안하는 실시간 보정 방법은 기존의 map-matching 방법과 함께 측위의 정확도를 제고하기 위하여 사용될 수 있다.

• Journal of Auto-vehicle Safety Association
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• v.12 no.2
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• pp.33-39
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• 2020

This paper presents an Around View Monitoring (AVM) stop-line detection based longitudinal position correction algorithm for automated driving on urban roads. Poor positioning accuracy of low-cost GPS has many problems for precise path tracking. Therefore, this study aims to improve the longitudinal positioning accuracy of low-cost GPS. The algorithm has three main processes. The first process is a stop-line detection. In this process, the stop-line is detected using Hough Transform from the AVM camera. The second process is a map matching. In the map matching process, to find the corrected vehicle position, the detected line is matched to the stop-line of the HD map using the Iterative Closest Point (ICP) method. Third, longitudinal position of low-cost GPS is updated using a corrected vehicle position with Kalman Filter. The proposed algorithm is implemented in the Robot Operating System (ROS) environment and verified on the actual urban road driving data. Compared to low-cost GPS only, Test results show the longitudinal localization performance was improved.

• 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.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.1
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• pp.43-48
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• 2016

We propose plain fingerprint classification based on a core stochastic algorithm that effectively uses a core stochastic model, acquiring more fingerprint minutiae and direction, in order to increase matching performance. The proposed core stochastic algorithm uses core presence/absence and contains a ridge direction and distribution map. Simulations show that the fingerprint classification accuracy is improved by more than 14%, on average, compared to other algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1403-1406
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• 1997

Generally, G.P.S(Global Positioning System) is using for the car navigation system but it has some restrictions such as the discontinuity of earth satellites and SA (Selective Availability). Recently, the hybrid navigation system combining with G.P.S and Dead-reckoning are much attractuve for improving the accuracy of a vehicle positioning. G.P.S called satellite navigation system, can measure its position by using satellites. Dead-Reckoning is the self-contained navigatioin system using a wheel sensor for the vehicle velocity and a gyro sensor for the vehicle angular velocity. Some algorithm could be generated for finding the vehicle position and orientation. In this paper, we developed a hybrid algotithm wiht G.P.S DR and Map-Matching.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.920-923
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• 1996

This paper presents a new algorithm for the self-localization of a mobile robot using perspective invariant(Cross Ratio). Most of conventional model-based self-localization methods have some problems that data structure building, map updating and matching processes are very complex. Use of the simple cross ratio can be effective to the above problems. The algorithm is based on two basic assumptions that the ground plane is flat and two parallel walls are available. Also it is assumed that an environmental map is available for matching between the scene and the model. To extract an accurate steering angle for a mobile robot, we take advantage of geometric features such as vanishing points(V.P). Point features for computing cross ratios are extracted robustly using a vanishing point and the intersection points between floor and the vertical lines of door frames. The robustness and feasibility of our algorithms have been demonstrated through experiments in indoor environments using an indoor mobile robot, KASIRI-II(KAist SImple Roving Intelligence).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.1
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• pp.1-9
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• 2013

This paper proposes a matching algorithm to find corresponding polygon feature sets between heterogeneous digital maps. The algorithm finds corresponding sets in terms of optimizing their shape similarities based on the assumption that the feature sets describing the same entities in the real world are represented in similar shapes. Then, by using a binary code, it is represented that a polygon feature is chosen for constituting a corresponding set or not. These codes are combined into a binary string as a candidate solution of the matching problem. Starting from initial candidate solutions, a genetic algorithm iteratively optimizes the candidate solutions until it meets a termination condition. Finally, it presents the solution with the highest similarity. The proposed method is applied for the topographical and cadastral maps of an urban region in Suwon, Korea to find corresponding polygon feature sets for block areas, and the results show its feasibility. The results were assessed with manual detection results, and showed overall accuracy of 0.946.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.4
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• pp.387-396
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• 2008

An integration between construction CAD data and GIS data needs geo-referencing processes of construction CAD data whose coordinate systems are their own native or even unknown. Generally, these processes are based on manually detected conjugate-vertices. In this study, we proposed an semi-automated conjugate -vertices detection method for building features between construction CAD data and a digital map using a vertex attributed string matching algorithm. A geo-referencing function for construction CAD data based on the similarity transform could be derived with those conjugate-vertices. Using our proposed method, we overlaid geo-referenced CAD data to a digital map of the College of Engineering, Seoul National University and evaluated our method.