• Journal of Institute of Control, Robotics and Systems
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• v.18 no.3
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• pp.208-217
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• 2012

This paper presents two adaptive fingerprint matching methods. First, we experiment an adaptive threshold selection of 1:N matching system in order to raise the reliability of the matching score. Second, we propose a adaptive threshold selection using fitting algorithm for high speed matching. The experiment was conducted on the NITZEN database, which has 5247 samples. Consequently, this paper shows that our suggested method can perform 1.88 times faster matching speed than the bidirectional matching speed. And, we prove that FRR of our suggested method decreases 1.43 % than that of the unidirectional matching.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.06a
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• pp.661-662
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• 2007

• International Journal of Advanced Culture Technology
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• v.5 no.1
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• pp.76-82
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• 2017

An image hash, a discriminative and robust summary of an image, should be robust against quality-preserving signal processing steps, while being pairwise independent for perceptually different inputs. In order to improve the hash matching performance, this paper proposes a probabilistic dissimilarity matching. Instead of extracting the binary hash from the query image, we compute the probability that the intermediate hash vector of the query image belongs to each quantization bin, which is referred to as soft quantization binning. The probability is used as a weight in comparing the binary hash of the query with that stored in a database. A performance evaluation over sets of image distortions shows that the proposed probabilistic matching method effectively improves the hash matching performance as compared with the conventional Hamming distance.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.144-145
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• 2020

본 논문에서는 정밀한 3차원 복원 및 시점 합성을 위해 매칭 비용을 반복적으로 업데이트하는 Generalized Soft 3D Reconstruction (GenSoft3D) 알고리즘을 제안한다. 먼저 다시점 영상들과 카메라 자세정보가 주어지면 GenSoft3D는 볼륨 기반의 다시점 스테레오 매칭 알고리즘으로 시점별 초기 매칭 비용 볼륨 및 시차 맵을 계산한다. 그 후 정제 과정에서 각 시점은 모든 시차 맵을 이용하여 표면 확률 및 가시 확률을 계산한다. 표면 확률은 초기 매칭 비용 업데이트에 사용하며, 가시 확률은 폐색 영역의 정확한 시차를 계산하기 위해 사용된다. 해당 정제 과정을 일정 횟수 반복할 경우 시점별 고정밀의 시차 맵 획득이 가능하다. 또한 시차 맵의 정확도가 향상됨에 따라 정확한 시점 합성이 가능하다.

• Geomechanics and Engineering
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• v.8 no.5
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• pp.707-726
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• 2015

This paper investigates the time dependent behaviour of Haarajoki test embankment on soft structured clay deposit. Half of the embankment is constructed on an area improved with prefabricated vertical drains, while the other half is constructed on the natural deposit without any ground improvement. To analyse the PVD-improved subsoil, axisymmetric vertical drains were converted into equivalent plane strain conditions using three different approaches. The construction and consolidation of the embankment are analysed with the finite element method using a recently developed anisotropic model for time-dependent behaviour of soft clays. The constitutive model, namely ACM-S accounts for combined effects of plastic anisotropy, interparticle bonding and degradation of bonds and creep. For comparison, the problem is also analysed with isotropic Soft Soil Creep and Modified Cam Clay models. The results of the numerical analyses are compared with the field measurements. The results show that neglecting effects of anisotropy, destructuration and creep may lead to inaccurate predictions of soft clay response. Additionally, the numerical results show that the matching methods accurately predict the consolidation behaviour of the embankment on PVD improved soft clays and provide a useful tool for engineering practice.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.10a
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• pp.475-483
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• 2008

It is necessary to develop a national design method for surface reinforcement of very soft ground because most current design works rely on crude empirical correlations. In this paper, the mechanical behavior of very soft ground that is surficially reinforced was investigated with the aid of a sents of numerical analysis. Several material properties of each dredged soft ground, reinforcement and backfill sand mat have been exercised the numerical analysis in order to compare the result of numerical analysis with those of the laboratory model test. Through the matching process between the numerical and experimental result, it is possible to find the appropriate material properties of the dredged soft ground, reinforcements and backfill sand mat. These verified material properties permit to show the effect of the stiffness of reinforcement and the thickness of sand mat on the overall deformation.

• Journal of IKEEE
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• v.17 no.4
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• pp.517-524
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• 2013

Stereo matching has been grabbing the attention of researchers because it plays an important role in computer vision, remote sensing and photogrammetry. Although most methods perform well with small size images, experiments applying them to large-scale data sets under uncontrolled conditions are still lacking. In this paper, we present an empirical study on stereo matching for large-scale high-resolution satellite images. A new method is studied to solve the problem of huge size and memory requirement when dealing with large-scale high resolution satellite images. Integrating the tiling technique with the well-known dynamic programming and coarse-to-fine pyramid scheme as well as using memory wisely, the suggested method can be utilized for huge stereo satellite images. Analyzing 350 points from an image of size of 8192 x 8192, disparity results attain an acceptable accuracy with RMS error of 0.5459. Taking the trade-off between computational aspect and accuracy, our method gives an efficient stereo matching for huge satellite image files.

• Journal of the Korean Institute of Intelligent Systems
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• v.3 no.2
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• pp.3-17
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• 1993

The fuzzy pattern matching technique has been developed in the framework of fuzzy set and possibility theory in order to take into account the imprecision and the uncertainty pervading values which have to be compared to requirements (which may be fuzzy) in a pattern matching process. This paper restates the basic principles and extends them to situations where (sub)patterns are only required to be satisfied up to a given tolerance (which may be fuzzy), or where the different subparts of a compound pattern may have various levels of importance. Both cases correspond to a weakening of elementary patterns. which can be expressed by a fuzzy relations modelling an approximate equality or an uncertain strict equality respectively. We also study the more sophisticated case where some elementary patterns have not to be satisfied with the highest priority provided that weaker requirements remain satisfied. The fuzzy pattern matching technique applies in a variety of problems including the evaluation of soft queries with respect to a fuzzy database, the evaluation of the fuzzy condition parts of rules in approximate reasoning, or the evaluation of the belonging of an ill-known object to a flexible class in classification problems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1766-1784
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• 2014

Finger vein images contain rich oriented features. Local line binary pattern (LLBP) is a good oriented feature representation method extended from local binary pattern (LBP), but it is limited in that it can only extract horizontal and vertical line patterns, so effective information in an image may not be exploited and fully utilized. In this paper, an orientation-selectable LLBP method, called generalized local line binary pattern (GLLBP), is proposed for finger vein recognition. GLLBP extends LLBP for line pattern extraction into any orientation. To effectually improve the matching accuracy, the soft power metric is employed to calculate the matching score. Furthermore, to fully utilize the oriented features in an image, the matching scores from the line patterns with the best discriminative ability are fused using the Hamacher rule to achieve the final matching score for the last recognition. Experimental results on our database, MMCBNU_6000, show that the proposed method performs much better than state-of-the-art algorithms that use the oriented features and local features, such as LBP, LLBP, Gabor filter, steerable filter and local direction code (LDC).

• Journal of the Korean Institute of Intelligent Systems
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• v.1 no.1
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• pp.9-25
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• 1991

This tutorial paper has been written for biologists, physicians or beginners in fuzzy sets theory and applications. This field is introduced in the framework of medical diagnosis problems. The paper describes and illustrates with practical examples, a general methodology of special interest in the processing of borderline cases, that allows a graded assignment of diagnoses to patients. A pattern of medical knowledge consists of a tableau with linguistic entries or of fuzzy propositions. Relationships between symptoms and diagnoses are interpreted as labels of fuzzy sets. It is shown how possibility measures (soft matching) can be used and combined to derive diagnoses after measurements on collected data. The concepts and methods are illustrated in a biomedical application on inflammatory protein variations. In the case of poor diagnostic classifications, it is introduced appropriate ponderations, acting on the characterizations of proteins, in order to decrease their relative influence. As a consequence, when pattern matching is achieved, the final ranking of inflammatory syndromes assigned to a given patient might change to better fit the actual classification. Defuzzification of results (i.e. diagnostic groups assigned to patients) is performed as a non fuzzy sets partition issued from a "separating power", and not as the center of gravity method commonly employed in fuzzy control. It is then introduced a model of fuzzy connectionist expert system, in which an artificial neural network is designed to build the knowledge base of an expert system, from training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the connections: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through MIN-MAX fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feed forward network is described and illustrated in the same biomedical domain as in the first part.