Fuzzy Matching Research Articles

An Algorithm for Distorted Fingerprint Matching Based on Local Triangle Feature Set

Coping with nonlinear distortions in fingerprint matching is a challenging task. This paper proposes a novel method, a fuzzy feature match (FFM) based on a local triangle feature set to match the deformed fingerprints. The fingerprint is represented by the fuzzy feature set: the local triangle feature set. The similarity between the fuzzy feature set is used to characterize the similarity between fingerprints. A fuzzy similarity measure for two triangles is introduced and extended to construct a similarity vector including the triangle-level similarities for all triangles in two fingerprints. Accordingly, a similarity vector pair is defined to illustrate the similarities between two fingerprints. The FFM method maps the similarity vector pair to a normalized value which quantifies the overall image to image similarity. The proposed algorithm has been evaluated with NIST 24 and FVC2004 fingerprint databases. Experimental results confirm that the proposed FFM based on the local triangle feature set is a reliable and effective algorithm for fingerprint matching with nonlinear distortions.

Fuzzy Elastic Bunch Graph Matching 방법을 이용한 얼굴인식

본 논문은 EBGM(Elastic Bunch Graph Matching)기법을 이용한 얼굴인식에 대해 다룬다. 대용량 영상 정보에 대해 차원 축소를 이용한 얼굴인식 기법인 주성분기법이나 선형판별기법에서는 얼굴 영상 전체의 정보를 이용하는 반면 본 연구에서는 얼굴의 눈 코 입 등과 같은 얼굴 특징점에 대해 주파수와 방향각이 다른 여러 개의 가버 커널과 영상 이미지의 컨볼루션(Convolution)의 계수의 집합(Jets)을 이용한 특징 데이터를 이용한다. 하나의 얼굴 영상에 대해서 모든 영상이 같은 크기의 특질 데이터로 표현되는 Face Graph가 생성되며, 얼굴인식 과정에서는 추출된 제트의 집합에 대해서 상호 유사도(Similarity)의 크기를 비교하여 얼굴인식을 수행한다. 본 논문에서는 기존의 EBGM 방법의 Face Graph 생성 과정을 보다간략화 한 방법을 이용하여 얼굴인식 과정에서 계산량을 줄여 속도를 개선하였으며, 퍼지 매칭법을 이용한 유사도 계산을 하였다. In this paper we describe a face recognition using EBGM(Elastic Bunch Graph Matching) method. Usally, the PCA and LDA based face recognition method with the low-dimensional subspace representation use holistic image of faces, but this study uses local features such as a set of convolution coefficients for Gabor kernels of different orientations and frequencies at fiducial points including the eyes, nose and mouth. At pre-recognition step, all images are represented with same size face graphs and they are used to recognize a face comparing with each similarity for all images. The proposed algorithm has less computation time due to simplified face graph than conventional EBGM method and the fuzzy matching method for calculating the similarity of face graphs renders more face recognition results.

An algorithm for fuzzy pattern matching to allocate soil individuals to pre-existing soil classes

A numerical procedure to allocate qualitative observations of soil individuals to soil classes is proposed. It takes into account both the fuzzy nature of soil classes and the imprecision of the cursory descriptions of soil individuals. The procedure is based on possibility theory and it uses an algorithm for fuzzy pattern matching. It includes three steps: (i) translating soil class and soil individual descriptions into a set of membership functions and possibility distributions of soil attributes, respectively; (ii) assessing the similarity between soil individuals and candidate soil classes by possibility and necessity measures; and (iii) defining a threshold of possibility and necessity to allocate individuals to candidate soil classes. This allocation procedure is tested with eight major soil classes and 83 soil individual descriptions extracted from the 1:250 000 scale soil database of the Languedoc-Roussillon (south of France). The results show that a fairly good agreement with experts is possible although some allocations remain imprecise or impossible. Further improvements are suggested regarding both the procedure itself and the data used.

Fuzzy querying of incomplete, imprecise, and heterogeneously structured data in the relational model using ontologies and rules

In this paper, we present a new method, called multiview fuzzy querying, which permits to query incomplete, imprecise and heterogeneously structured data stored in a relational database. This method has been implemented in the MIEL software. MIEL is used to query the Sym'Previus database which gathers information about the behavior of pathogenic germs in food products. In this database, data are incomplete because information about all possible food products and all possible germs is not available; data are heterogeneous because they come from various sources (scientific bibliography, industrial data, etc); data may be imprecise because of the complexity of the underlying biological processes that are involved. To deal with heterogeneity, MIEL queries the database through several views simultaneously. To query incomplete data, MIEL proposes to use a fuzzy set, expressing the query preferences of the user. Fuzzy sets may be defined on a hierarchized domain of values, called an ontology (values of the domain are connected using the a kind of semantic link). MIEL also proposes two optional functionalities to help the user query the database: i) MIEL can use the ontology to enlarge the querying in order to retrieve the nearest data corresponding to the selection criteria; and ii) MIEL proposes fuzzy completion rules to help the user formulate his/her query. To query imprecise data stored in the database with fuzzy selection criteria, MIEL uses fuzzy pattern matching.

Fuzzy Image Matching for Pose Recognition of Occluded Knee Implants Using Fluoroscopy Images

Fluoroscopy images have been widely used for evaluating kinematics of the knee implant in vivo after the total knee Arthroplasty, TKA in short. The knee implant mainly consists of tibial tray, tibial insert and femoral component. Because a fluoroscopy image is a 2-D projection image of the 3-D knee implant, the tibial tray often overlaps with the femoral component on the projection image, or a part of the knee implant is outside of the field of view (FOV). In order to analyze such occluded images, this article introduces fuzzy logic into image matching of the given 2-D fluoroscopy image and 3-D geometric models of the knee implant. Based on the proposed fuzzy image matching algorithm, we present a novel computer-aided diagnosis (CAD) system for estimating 3-D kinematics of the knee implant with 2-D fluoroscopy dynamic images. To quantitatively evaluate our system, it was applied to computer-simulated images and phantom images that took the knee implant in vitro fixed with arbitrary pose by a jig. The experimental results denoted that this system could estimate the pose of the knee implant within the error of 0.86° with non-occluded images, and within the error of 1.28° with 15% occluded images. Also, the proposed system was applied to two patients after TKA to demonstrate the clinical application.

Fuzzy matching of images based on shape description matrix

In this paper, a fuzzy matching criterion of membership function based on shape description matrix (SDM) is presented to analyze and compare shape characteristi cs of images. Analysis and experimental result show that by applying this approa ch to image retrieval, more effective image retrieval can be achieved.

Image database design based on 9D-SPA representation for spatial relations

Spatial relationships between objects are important features for designing a content-based image retrieval system. We propose a new scheme, called 9D-SPA representation, for encoding the spatial relations in an image. With this representation, important functions of intelligent image database systems such as visualization, browsing, spatial reasoning, iconic indexing, and similarity retrieval can be easily achieved. The capability of discriminating images based on 9D-SPA representation is much more powerful than any spatial representation method based on minimum bounding rectangles or centroids of objects. The similarity measures using 9D-SPA representation provide a wide range of fuzzy matching capability in similarity retrieval to meet different user's requirements. Experimental results showed that our system is very effective in terms of recall and precision. In addition, the 9D-SPA representation can be incorporated into a two-level index structure to help reduce the search space of each query processing. The experimental results also demonstrated that, on average, only 0.1254 percent /spl sim/ 1.6829 percent of symbolic pictures (depending on various degrees of similarity) were accessed per query in an image database containing 50,000 symbolic pictures.

Construction of PC-based expert system for cold forging process design

Cold forging process design is a field where the theory has not been established until present, and it depends on skilled designers with long time experience and intuition. Consequently, it is difficult for designers with shallow experience to cope, since process design needs time and cost. Thus, computer-based expert system for cold forging process design targeted to beginners has been developed in this study.In this system, a case base is used as the knowledge base and the fuzzy pattern matching is used as a reasoning theory. Due to the case base, composed of geometric data, been designed by the human expert, it has uncertain factors such as the subjectivity of the human experts. Therefore, the fuzzy pattern matching can be applied to generate appropriate design results from little and uncertain knowledge included in the case base.

Content-based image retrieval based on a fuzzy approach

A typical content-based image retrieval (CBIR) system would need to handle the vagueness in the user queries as well as the inherent uncertainty in image representation, similarity measure, and relevance feedback. We discuss how fuzzy set theory can be effectively used for this purpose and describe an image retrieval system called FIRST (fuzzy image retrieval system) which incorporates many of these ideas. FIRST can handle exemplar-based, graphical-sketch-based, as well as linguistic queries involving region labels, attributes, and spatial relations. FIRST uses fuzzy attributed relational graphs (FARGs) to represent images, where each node in the graph represents an image region and each edge represents a relation between two regions. The given query is converted to a FARG, and a low-complexity fuzzy graph matching algorithm is used to compare the query graph with the FARGs in the database. The use of an indexing scheme based on a leader clustering algorithm avoids an exhaustive search of the FARG database. We quantify the retrieval performance of the system in terms of several standard measures.

Translating cross-lingual spelling variants using transformation rules

Technical terms and proper names constitute a major problem in dictionary-based cross-language information retrieval (CLIR). However, technical terms and proper names in different languages often share the same Latin or Greek origin, being thus spelling variants of each other. In this paper we present a novel two-step fuzzy translation technique for cross-lingual spelling variants. In the first step, transformation rules are applied to source words to render them more similar to their target language equivalents. The rules are generated automatically using translation dictionaries as source data. In the second step, the intermediate forms obtained in the first step are translated into a target language using fuzzy matching. The effectiveness of the technique was evaluated empirically using five source languages and English as a target language. The two-step technique performed better, in some cases considerably better, than fuzzy matching alone. Even using the first step as such showed promising results.

AN APPROACH TO NATURAL STROKE EXTRACTION FOR OFF-LINE LOOSELY-CONSTRAINED HANDWRITTEN CHINESE CHARACTERS

This paper proposes a new approach to extracting natural strokes from the skeletons of loosely-constrained, off-line handwritten Chinese characters. It admits the output substrokes from a previously proposed fuzzy substroke extractor as its inputs. By identifying a number of expected ambiguities which include mutual similarities, unstable touches and joint/cross distortions, fuzzy stroke models are constructed and a "hit-all" fuzzy stroke matching strategy is pursued. Fuzzy partitioning technique is used to generate a ranked list of consistent stroke sets from the set of fuzzy strokes being identified. With this approach, a maximum of 20 distinct natural stroke classes can be extracted from each input character, together with an estimate on the actual count of strokes which compose the character. Our system offers a number of performance tuning capabilities such as the computation of the fuzzy scores of each extracted stroke, the adjustment on the fuzzy stroke model parameters, and the potential of incorporating one's personal writing styles into our methodology.

LEARNING RULES FOR ODOUR RECOGNITION IN AN ELECTRONIC NOSE

The problem of automating the sensing and classification of odours is one which promises a wide range of industrial applications. During the INTESA project, a prototype electronic nose was developed, using sensors based on novel conducting polymer materials and also more traditional MOS materials. The software component of the prototype processes the transient resistance change signals recorded by the hardware, and classifies the odour sample into one of a number of "odour classes". This paper describes two of the soft computing methods investigated for learning classification rules in this domain. The first method builds on previous work done on the Fril data browser, using clustering, fuzzy matching, Fril rules and evidential logic rules. The second method uses a fuzzy extension of the ID3 decision tree induction method, called "mass assignment tree induction (MATI)". Some of the results of applying these methods to data obtained from the INTESA prototype are presented and discussed.

Fuzzy Matching

Fuzzy Matching

Design of a two-stage content-based image retrieval system using texture similarity

Efficacy and efficiency are two important issues in designing content-based image retrieval systems. In this paper, we present an efficient two-stage image retrieval system with high performance of efficacy based on two novel texture features, the composite sub-band gradient (CSG) vector and the energy distribution pattern (EDP)-string. Both features are generated from the sub-images of a wavelet decomposition of the original image. At the first stage, a fuzzy matching process based on EDP-strings is performed and serves as a signature filter to quickly remove a large number of non-promising database images from further consideration. At the second stage, the images passing through the filter will be compared with the query image based on their CSG vectors for detailed feature inspection. By exercising a database of 2400 images obtained from the Brodatz album, we demonstrated that both high efficacy and high efficiency can be achieved simultaneously by our proposed system.

Balancing meals using fuzzy arithmetic and heuristic search algorithms

This paper aims at showing how well-known ideas in the fields of fuzzy arithmetic and heuristic search have been combined in an educational software in nutrition in order to provide not only a better mathematical modeling, but also significant functional improvements for end-users, comparing to other nutrition programs. This software, called Nutri-Expert, helps patients to improve their nutritional habits, by analyzing in detail their food intakes, and by suggesting changes that result in well-balanced meals. Fuzzy arithmetic is used to model the input and database data, and for all computations. A fuzzy pattern matching is performed between total amounts of nutrients and different norm patterns, and the results are displayed using a galvanometer metaphor. A heuristic search algorithm is used to find out minimal sets of pertinent actions to perform on a meal in order to make it well balanced. The search is guided by an evaluation function based on fuzzy pattern matching indexes. The different versions of the algorithm have been benchmarked against a test database of real meals. Finally, the medical efficacy of Nutri-Expert and its acceptance by end-users have been demonstrated in several medical studies, the main results of which are presented.

Feature Based Fuzzy Matching of 2D Gel Electrophoresis Images.

ChemInformVolume 34, Issue 4 Other Subjects Feature Based Fuzzy Matching of 2D Gel Electrophoresis Images. K. Kaczmarek, K. Kaczmarek Inst. Chem., Silesian Univ., PL-40-006 Katowice, Pol.Search for more papers by this authorB. Walczak, B. Walczak Inst. Chem., Silesian Univ., PL-40-006 Katowice, Pol.Search for more papers by this authorS. de Jong, S. de Jong Inst. Chem., Silesian Univ., PL-40-006 Katowice, Pol.Search for more papers by this authorB. G. M. Vandeginste, B. G. M. Vandeginste Inst. Chem., Silesian Univ., PL-40-006 Katowice, Pol.Search for more papers by this author K. Kaczmarek, K. Kaczmarek Inst. Chem., Silesian Univ., PL-40-006 Katowice, Pol.Search for more papers by this authorB. Walczak, B. Walczak Inst. Chem., Silesian Univ., PL-40-006 Katowice, Pol.Search for more papers by this authorS. de Jong, S. de Jong Inst. Chem., Silesian Univ., PL-40-006 Katowice, Pol.Search for more papers by this authorB. G. M. Vandeginste, B. G. M. Vandeginste Inst. Chem., Silesian Univ., PL-40-006 Katowice, Pol.Search for more papers by this author First published: 05 March 2003 https://doi.org/10.1002/chin.200304213AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume34, Issue4January 28, 2003 RelatedInformation

Acquisition of Soft Taxonomies for Intelligent Personal Hierarchies and the Soft Semantic Web

Information overload is a problem at an individual and a corporate level. Many solutions have been proposed, including knowledge management, data warehouses, service directories and digital libraries. The semantic Web aims to unify many of these approaches by appropriate markup and agreement on the meaning of the markup. At the individual's level, these techniques partially solve the problem by classifying documents within hierarchical structures and enabling searching and browsing of the documents. However, they also contribute to the problem as there is no unique categorisation and access structure that suits every individual. Finding the right document becomes a two-stage process — first find the right place in the categorisation scheme, then find the document within that class.In addition to enterprise-wide sources, individual information sources include e-mails, electronic documents in many formats, personal and group filespaces, notes, diary entries, etc. These are unlikely to conform to the enterprise categorisation but form useful resources nevertheless.The idea of an intelligent personal hierarchy for information (iPHI) is to auto-configure access to multiple sources of information based on personal categories. This entails fuzzy matching of meta-data structure as well as content. Metadata is a powerful tool in intelligent information management; however, it is not necessarily uniform, either in label or in content. One document's ‘author’ is another's ‘creator’; ‘John Smith’, ‘Smith, John’ and ‘J.Smith’ all refer to the same individual but are syntactically different.Fusion (or intelligent integration) of information takes place in an environment where the data may be of varying quality, and some may be incomplete or inconsistent. Combining metadata (and the associated data) is not possible without knowing (or learning) the mappings between their ontologies. Such mappings are likely to be soft, i.e. approximate — different sources arise from different designers with different world views. Soft computing is vital to tackle these problems. Frequently, data sources are organised implicitly, according to an internal ontology or taxonomy. Knowing this ontology or taxonomy is a necessary first step to using it in the fusion process. The work described in this paper extracts the implicit taxonomy and enables a user's interaction with the data (e.g. searching) to be expressed in their preferred terms rather than those used by the system.

Feature Based Fuzzy Matching of 2D Gel Electrophoresis Images

Automatic alignment (matching) of two-dimensional gel electrophoresis images is of primary interest in the evolving field of proteomics. In the present study, feature-based matching techniques, in their classical and robust versions, are described, and an automatic method of fuzzy alignment (FA) is introduced. This method allows automatic matching of two gel images with different numbers of features with unknown correspondence. Performance of FA is tested on simulated and real data sets.

A region-based fuzzy feature matching approach to content-based image retrieval

This paper proposes a fuzzy logic approach, UFM (unified feature matching), for region-based image retrieval. In our retrieval system, an image is represented by a set of segmented regions, each of which is characterized by a fuzzy feature (fuzzy set) reflecting color, texture, and shape properties. As a result, an image is associated with a family of fuzzy features corresponding to regions. Fuzzy features naturally characterize the gradual transition between regions (blurry boundaries) within an image and incorporate the segmentation-related uncertainties into the retrieval algorithm. The resemblance of two images is then defined as the overall similarity between two families of fuzzy features and quantified by a similarity measure, UFM measure, which integrates properties of all the regions in the images. Compared with similarity measures based on individual regions and on all regions with crisp-valued feature representations, the UFM measure greatly reduces the influence of inaccurate segmentation and provides a very intuitive quantification. The UFM has been implemented as a part of our experimental SIMPLIcity image retrieval system. The performance of the system is illustrated using examples from an image database of about 60,000 general-purpose images.

Fuzzy logic-based matching pursuits for lossless predictive coding of still images

This paper presents an application of fuzzy-logic techniques to the reversible compression of grayscale images. With reference to a spatial differential pulse code modulation (DPCM) scheme, prediction may be accomplished in a space-varying fashion either as adaptive, i.e., with predictors recalculated at each pixel, or as classified, in which image blocks or pixels are labeled in a number of classes, for which fitting predictors are calculated. Here, an original tradeoff is proposed; a space-varying linear-regression prediction is obtained through fuzzy-logic techniques as a problem of matching pursuit, in which a predictor different for every pixel is obtained as an expansion in series of a finite number of prototype nonorthogonal predictors, that are calculated in a fuzzy fashion as well. To enhance entropy coding, the spatial prediction is followed by context-based statistical modeling of prediction errors. A thorough comparison with the most advanced methods in the literature, as well as an investigation of performance trends and computing times to work parameters, highlight the advantages of the proposed fuzzy approach to data compression.