• Journal of Korean Society of Disaster and Security
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• v.12 no.3
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• pp.13-24
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• 2019

The concept of shape similarity has been applied to verify the accuracy of the SIND model, the real-time prediction model for disaster risk. However, the CRITIC method, one of the most widely used in geometric methodology, is definitely limited to apply to complex shape such as hazard map for coastal disaster. Therefore, we suggested the modified CRITIC method of which we added the shape factors such as RCCI and TF to consider complicated shapes. The matching pairs were manually divided into exact-matching pairs and mis-matching pairs to evaluate the applicability of the new method for shape similarity into hazard maps for storm surges. And the shape similarity of each matching pair was calculated by changing the weights of each shape factor and criteria. Newly proposed methodology and the calculated weights were applied to the objects of the existent hazard map and the results from SIND model. About 90% of exact-matching pairs had the shape similarity of 0.5 or higher, and about 70% of mis-matching pairs were it below 0.5. As future works, if we would calibrate narrowly and adjust carefully multi-objects corresponding to one object, it would be expected that the shape similarity of the exact-matching pairs will increase overall while it of the mis-matching pairs will decrease.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.1
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• pp.133-142
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• 1993

This paper prrsents shape matching of two-dimensional object. This shape matching is applied to two-dimensional simple c10sedcurves represented by polygons. A large number of shape matching procedures have proposed baseed on teh view that shape can be represented by a vector of numerical features, and that this representation can be matched using techniques from statical pattern recognition. The varieties of features that have been extracted from shapes and used to represent them are numerous. But all of these feature-based approches suffer from the shortcoming that the descriptor of a segment of a shape do not ordinarily bear any simple relations hip to the description for the entire shape. We solve the segment matching problem of shape matching, defined as the recognition of a piece of a shape as approximate match to a part of large shape, by using relaxation labeling technique.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2009.04a
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• pp.23-25
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• 2009

Nowadays, owing to the development of techniques for collecting geographic information, an increasing need is thus appearing: integrating heterogeneous databases. This paper proposes an algorithm for finding matching relationship between two node sets in road network data. We found the corresponding node pair using link shape linked with them as well as their location. The accuracy of matching was grown by this process. Result then can be used to reflect the topological information in performing link matching.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.1
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• pp.59-65
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• 2012

In this paper, we proposed a method for automatic detection of a updating object from spatial data sets of different scale and updating cycle by using areal feature matching based on shape similarity. For this, we defined a updating object by analysing matching relationships between two different spatial data sets. Next, we firstly eliminated systematic errors in different scale by using affine transformation. Secondly, if any object is overlaid with several areal features of other data sets, we changed several areal features into a single areal feature. Finally, we detected the updating objects by applying areal feature matching based on shape similarity into the changed spatial data sets. After applying the proposed method into digital topographic map and a base map of Korean Address Information System in South Korea, we confirmed that F-measure is highly 0.958 in a statistical evaluation and that significant updating objects are detected from a visual evaluation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.2
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• pp.113-121
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• 2011

In this paper, we proposed the method automatically to match areal feature based on similarity using spatial information. For this, we extracted candidate matching pairs intersected between two different spatial datasets, and then measured a shape similarity, which is calculated by an weight sum method of each matching criterion automatically derived from CRITIC method. In this time, matching pairs were selected when similarity is more than a threshold determined by outliers detection of adjusted boxplot from training data. After applying this method to two distinct spatial datasets: a digital topographic map and street-name address base map, we conformed that buildings were matched, that shape is similar and a large area is overlaid in visual evaluation, and F-Measure is highly 0.932 in statistical evaluation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.1
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• pp.49-55
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• 2013

In this paper, we proposed a good classifier to match different spatial data sets by applying evaluation of classifiers performance in data mining and biometrics. For this, we calculated distances between a pair of candidate features for matching criteria, and normalized the distances by Min-Max method and Tanh (TH) method. We defined classifiers that shape similarity is derived from fusion of these similarities by CRiteria Importance Through Intercriteria correlation (CRITIC) method, Matcher Weighting method and Simple Sum (SS) method. As results of evaluation of classifiers performance by Precision-Recall (PR) curve and area under the PR curve (AUC-PR), we confirmed that value of AUC-PR in a classifier of TH normalization and SS method is 0.893 and the value is the highest. Therefore, to match different spatial data sets, we thought that it is appropriate to a classifier that distances of matching criteria are normalized by TH method and shape similarity is calculated by SS method.

• 한국지형공간정보학회:학술대회논문집
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• 2004.10a
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• pp.25-31
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• 2004

To obtain the 3-D coordinates of the urban roads from aerial images, the accurate matching technique in road areas is required. In this paper, we suggest the relational matching method that is performed by comparison of relationships of road pavement markings after they are extracted from aerial images using geometric properties and spatial relationships of the pavement markings. Relational matching requires not only high level description of features but also the solution for inexact matching problems. In addition, it needs a lot of tests for the reliable final result. In this research, we described features as calculating geometric properties of the pavement markings, suggested the solution for inextact matching problems, and performed tests to decide whether the result is acceptable or not, which use the property that road areas are flat. In order to evaluate the accuracy of matching, we made a visual evaluation and compared the result of this technique with those measured by analytical photogrammetry.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.89-98
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• 2016

In this paper, we proposed an automated areal feature matching method based on geometric similarity without user intervention and is applied into areal features of many-to-many relation, for confusion of spatial data-sets of different scale and updating cycle. Firstly, areal feature(node) that a value of inclusion function is more than 0.4 was connected as an edge in adjacency matrix and candidate corresponding areal features included many-to-many relation was identified by multiplication of adjacency matrix. For geometrical matching, these multiple candidates corresponding areal features were transformed into an aggregated polygon as a convex hull generated by a curve-fitting algorithm. Secondly, we defined matching criteria to measure geometrical quality, and these criteria were changed into normalized values, similarity, by similarity function. Next, shape similarity is defined as a weighted linear combination of these similarities and weights which are calculated by Criteria Importance Through Intercriteria Correlation(CRITIC) method. Finally, in training data, we identified Equal Error Rate(EER) which is trade-off value in a plot of precision versus recall for all threshold values(PR curve) as a threshold and decided if these candidate pairs are corresponding pairs or not. To the result of applying the proposed method in a digital topographic map and a base map of address system(KAIS), we confirmed that some many-to-many areal features were mis-detected in visual evaluation and precision, recall and F-Measure was highly 0.951, 0.906, 0.928, respectively in statistical evaluation. These means that accuracy of the automated matching between different spatial data-sets by the proposed method is highly. However, we should do a research on an inclusion function and a detail matching criterion to exactly quantify many-to-many areal features in future.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.1
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• pp.23-28
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• 2013

The process of a feature matching for two different spatial data sets is similar to the process of classification as a binary class such as matching or non-matching. In this paper, we calculated a threshold by applying an equal error rate (EER) which is widely used in biometrics that classification is a main topic into spatial data sets. In a process of discriminating what's a matching or what's not, a precision and a recall is changed and a trade-off appears between these indexes because the number of matching pairs is changed when a threshold is changed progressively. This trade-off point is EER, that is, threshold. To the result of applying this method into training data, a threshold is estimated at 0.802 of a value of shape similarity. By applying the estimated threshold into test data, F-measure that is a evaluation index of matching method is highly value, 0.940. Therefore we confirmed that an accurate threshold is calculated by EER without person intervention and this is appropriate to matching different spatial data sets.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1037-1043
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• 2006

본 논문에서는 유비쿼터스 컴퓨팅 오피스 환경에서 가장 직관적인 HCI 수단인 손 제스처를 사용하여 대형 스크린 상의 응용 프로그램들을 쉽게 제어할 수 있는 시스템을 제안한다. 손 제스처는 손 영역의 정보, 손 중심점의 위치 변화값과 손가락 형상을 이용하여 시스템 제어에 필요한 종류들을 미리 정의해 둔다. 먼저 효율적으로 손 영역 획득을 위해 적외선 카메라를 사용하여 연속된 영상을 획득한다. 획득된 영상 프레임으로부터 구조적 템플레이트 매칭 방법을 사용하여 손의 중심(centroid) 및 손가락끝(fingertip)을 검출한다. 인식과정에서는 양손의 Euclidean distance와 손가락 형상 정보를 이용하여 미리 정의된 제스처와 비교하여 인식을 행한다. 본 논문에서 제안한 비전 기반 hand gesture 제어 시스템은 인간과 컴퓨터의 상호작용을 이해하는데 많은 이점을 제공할 수 있다. 실험 결과를 통해 본 논문에서 제안한 방법의 효율성을 입증한다.