• Journal of KIISE:Software and Applications
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• v.26 no.8
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• pp.964-973
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• 1999

본 논문에서는 퍼지 트리플을 사용하는 내용 기반 이미지 검색 방법을 제안한다. 이미지 내 객체들 사이의 공간 관계는 내용 기반 이미지 검색을 위해 사용되는 주요한 속성들 중의 하나이다. 그러나, 기존의 트리플을 이용한 이미지 검색 시스템들은 개념 기반 검색 방법을 지원하지 못하고, 방향들 사이의 근사 정합을 처리하지 못하는 문제점을 가지고 있다. 이 문제를 해결하기 위하여 본 논문에서는 개념 기반 정합과 근사 정합을 지원하는 퍼지 트리플을 이용한 이미지 검색 방법을 제안한다. 개념 기반 정합을 위해서는 퍼지 소속성 집합으로 이루어진 시소러스가 사용되며, 근사 정합을 위해서는 방향들 사이의 관계를 정량화 하기 위한 k-weight 함수가 각각 이용된다. 이 두 가지 정합은 퍼지 트리플 간의 퍼지 정합을 통하여 균일하게 지원될 수 있다. 본 논문에서는 또한, 개념 기반 정합과 근사 정합에 대한 검색 효과를 정량적으로 평가하는 작업을 수행한다. Abstract This paper proposes an inexact and a concept-based image match technique based on fuzzy triples. The most general method adopted to index and retrieve images based on this spatial structure may be triple framework. However, there are two significant drawbacks in this framework; one is that it can not support a concept-based image retrieval and the other is that it fails to deal with an inexact match among directions. To compensate these problems, we develope an image retrieval technique based on fuzzy triples to make the inexact and concept-based match possible. For the concept-based match, we employ a set of fuzzy membership functions structured like a thesaurus, whereas for the inexact match, we introduce k-weight functions to quantify the similarity between directions. In fuzzy triples, the two facilities are uniformly supported by fuzzy matching. In addition, we analyze the retrieval effectiveness of our framework regarding the degree of the conceptual matching and the inexact matching.

• Korean Journal of Remote Sensing
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• v.12 no.1
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• pp.43-59
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• 1996

The objective of this research is to investigate the potential of relational matching in one of the fundamental photogrammetric processes, that is initial approximation problem. The automatic relative orientation procedures of aerial stereopairs have been investigated. The fact that the existing methods suffer from approximations, distortions (geometric and radiometric), occlusions, and breaklines is the motivation to investigate relational matching which appears to be a much more general solution. An elegant way of solving the initial approximation problem by using distinct(special) relationship from relational description is suggested and experimented. As for evaluation function, the cost function was implemented. The detection of erroneous matching is incorporated as a part of proposed relational matching scheme. Experiments with real urban area images where large numbers of repetitive patterns, breaklines, and occluded areas are present prove the feasibility of implementation of the proposed relational matching scheme. The investigation of relational matching in the domain of image matching problem provides advantages and disadvantages over the existing image matching methods and shows the future area of development and implementation of relational matching in the field of digital photogrammetry.

• 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 Institute of Control, Robotics and Systems
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• v.12 no.10
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• pp.1044-1049
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• 2006

Multiple sequence alignment is a method to compare two or more DNA or protein sequences. Most of multiple sequence alignment tools rely on pairwise alignment and Smith-Waterman algorithm to generate an alignment hierarchy. Therefore, in the existing multiple alignment method as the number of sequences increases, the runtime increases exponentially. In order to remedy this problem, we adopted a parallel processing suffix tree algorithm that is able to search for common subsequences at one time without pairwise alignment. Also, the cross-matching subsequences triggering inexact-matching among the searched common subsequences might be produced. So, the cross-matching masking process was suggested in this paper. To identify the function of the clusters generated by suffix tree clustering, BLAST and CDD (Conserved Domain Database)search were combined with a clustering tool. Our clustering and annotating tool consists of constructing suffix tree, overlapping common subsequences, clustering gene sequences and annotating gene clusters by BLAST and CDD search. The system was successfully evaluated with 36 gene sequences in the pentose phosphate pathway, clustering 10 clusters, finding out representative common subsequences, and finally identifying functional domains by searching CDD database.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.10
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• pp.851-856
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• 2005

The DNA and protein data of diverse species have been daily discovered and deposited in the public archives according to each established format. Database systems in the public archives provide not only an easy-to-use, flexible interface to the public, but also in silico analysis tools of unidentified sequence data. Of such in silico analysis tools, multiple sequence alignment [1] methods relying on pairwise alignment and Smith-Waterman algorithm [2] enable us to identify unknown DNA, protein sequences or phylogenetic relation among several species. However, in the existing multiple alignment method as the number of sequences increases, the runtime increases exponentially. In order to remedy this problem, we adopted a parallel processing suffix tree algorithm that is able to search for common subsequences at one time without pairwise alignment. Also, the cross-matching subsequences triggering inexact-matching among the searched common subsequences might be produced. So, the cross-matching masking process was suggested in this paper. To identify the function of the clusters generated by suffix tree clustering, BLAST was combined with a clustering tool. Our clustering and annotating tool is summarized as the following steps: (1) construction of suffix tree; (2) masking of cross-matching pairs; (3) clustering of gene sequences and (4) annotating gene clusters by BLAST search. The system was successfully evaluated with 22 gene sequences in the pyrubate pathway of bacteria, clustering 7 clusters and finding out representative common subsequences of each cluster

• Journal of Korea Water Resources Association
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• v.38 no.10 s.159
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• pp.851-859
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• 2005

Normalized cross-correlation (correlation coefficient) is a useful measure for pattern matching in PIV (Particle Image Velocimetry) analysis. Because it does not have a corresponding simple expression in frequency domain, several fast but inexact measures have been used. Among them, three measures of correlation for PIV analysis and the normalized cross-correlation were evaluated with a sample calculation. The test revealed that all other proposed correlation measures sometimes show inaccurate results, except the normalized cross-correlation. However, correlation coefficient method has a weakpoint that it requires so long time for calculation. To overcome this shortcoming, a fast and exact method for calculating normalized cross-correlation is suggested. It adopts Fast Fourier Transform (FFT) for calculation of covariance and the successive-summing method for the denominator of correlation coefficient. The new algorithm showed that it is really fast and exact in calculating correlation coefficient.

• Journal of International Society for Simulation Surgery
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• v.2 no.1
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• pp.26-32
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• 2015

Purpose When a surgeon examines the morphology of skull of patient, locations of craniometric landmarks of 3D computed tomography(CT) volume are one of the most important information for surgical purpose. The locations of craniometric landmarks can be found manually by surgeon from the 3D rendered volume or 2D sagittal, axial, and coronal slices which are taken by CT. Since there are many landmarks on the skull, finding these manually is time-consuming, exhaustive, and occasionally inexact. These inefficiencies raise a demand for a automatic localization technique for craniometric landmark points. So in this paper, we propose a novel method through which we can automatically find these landmark points, which are useful for surgical purpose. Materials and Methods At first, we align the experimental data (CT volumes) using Frankfurt Horizontal Plane (FHP) and Mid Sagittal Plane(MSP) which are defined by 3 and 2 cranial landmark points each. The target landmark of our experiment is the anterior nasal spine. Prior to constructing a statistical cubic model which would be used for detecting the location of the landmark from a given CT volume, reference points for the anterior nasal spine were manually chosen by a surgeon from several CT volume sets. The statistical cubic model is constructed by calculating weighted intensity means of these CT sets around the reference points. By finding the location where similarity function (squared difference function) has the minimal value with this model, the location of the landmark can be found from any given CT volume. Results In this paper, we used 5 CT volumes to construct the statistical cubic model. The 20 CT volumes including the volumes, which were used to construct the model, were used for testing. The range of age of subjects is up to 2 years (24 months) old. The found points of each data are almost close to the reference point which were manually chosen by surgeon. Also it has been seen that the similarity function always has the global minimum at the detection point. Conclusion Through the experiment, we have seen the proposed method shows the outstanding performance in searching the landmark point. This algorithm would make surgeons efficiently work with morphological informations of skull. We also expect the potential of our algorithm for searching the anatomic landmarks not only cranial landmarks.