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An efficient algorithm for fingerprint reference-point detection

An efficient algorithm for fingerprint reference-point detec...

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2009 IEEE-RIVF international conference on computing and Communication Technologies: Research, Innovation and Vision for the Future, RIVF 2009

作者： Van, Thien Hoang Le, Hoang Thai Department of Computer Sciencies Ho Chi Minh City University of Technology Ho Chi Minh City Viet Nam Department of Computer Sciencies Ho Chi Minh City University of Natural Science Ho Chi Minh City Viet Nam

ISBN: (纸本)9781424445684

The reference-point played important roles in most algorithms of fingerprint recognition. It is widely used in fingerprint classification, and fingerprint matching. There are many methods proposed for reference point detection as Poincare index technique, Geometry of region technique, Direction curvature technique and Orientation-consistency-based technique. However, the experimental results show that the present methods are less accurate on the fingerprint database which contains many poor-quality fingerprint images (the most accuracy of the present methods is 93% on the fingerprint database FVC2004, DB2, set A). This paper proposes a novel algorithm to locate fingerprint reference-point consistently and accurately for all types of fingerprints. The method computes the convex orientation consistency which describes both direction of curvature and how well the orientations over a neighborhood are consistent with the dominant orientation. Experimental results show the effectiveness and superiority of our proposed method (96.9 % on the FVC2004, DB2 set A). © 2009 IEEE.

关键词： pattern matching

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Unsupervised Change Detection of Remotely Sensed Images Using Fuzzy Clustering

Unsupervised Change Detection of Remotely Sensed Images Usin...

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international conference on Advances in pattern recognition (ICAPR)

作者： Susmita Ghosh Niladri Shekhar Mishra Ashish Ghosh Department of Computer Science and Engineering Jadavpur University Kolkata India Department of Electronics and Communication Engineering Netaji Subhash Engineering College Kolkata India Machine Intelligence Unit and Center of Soft Computing Research Indian Statistical Institute Kolkata India

In this paper two fuzzy clustering algorithms, namely fuzzy C-means (FCM) and Gustafson Kessel clustering (GKC), have been used for detecting changes in multitemporal remote sensing images. Change detection maps are obtained by separating the pixel-patterns of the difference image into two groups. To show the effectiveness of the proposed technique, experiments are conducted on three multispectral and multitemporal images. Results are compared with those of existing Markov random field (MRF) & neural network based algorithms and found to be superior. The proposed technique is less time-consuming and unlike MRF do not need any a priori knowledge of distribution of changed and unchanged pixels (as required by MRF).

关键词： Clustering algorithms Pixel Change detection algorithms Remote sensing Neural networks Object detection Remote monitoring Statistical distributions Context modeling Arithmetic

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Weak metric learning for feature fusion towards perception-inspired object recognition

Weak metric learning for feature fusion towards perception-i...

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16th international Multimedia Modeling conference on Advances in Multimedia Modeling, MMM 2010

作者： Li, Xiong Zhao, Xu Fu, Yun Liu, Yuncai Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China NY 14260 United States

ISBN: (纸本)3642113001

With extracted local features of a given image, computing its global feature under perceptual framework has shown promising performance in object recognition. However, under some tough applications with large intra-class variance, using only one kind of local feature is inadequate to build a robust classification system. To integrate the discriminability of complementary local features, in this paper, we extend the efficacy of perceptual framework to adapt to heterogeneous features. Given multiple raw global features, we propose a fusion strategy through metric learning, which is called weak metric learning in this work, for fusing high dimensional features. The fusion model is solved with the maximal kernel canonical correlation formulation with the multiple global features as outputs. Experimental results show that our method achieves significant improvements about 5% to 11% than the benchmark perceptual framework system, HMAX, on several difficult categories of object recognition with much less training samples and feature elements. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Object recognition

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Research on Bayesian Decision Theory in pattern recognition

Research on Bayesian Decision Theory in Pattern Recognition

引用

international conference on Genetic and Evolutionary computing (WGEC)

作者： Hai-ming Li Ting-lei Huang Xin Wang School of Computer and Control Guilin University of Electronic and Technology China

In this paper, Bayesian decision theory is discussed. Bayesian decision related to the basic elements and the principles as well as the Bayes optimal decision criteria is introduced briefly. Its characteristics, advantages and disavantages as well as the applicable targets are analysed in this paper, in the end, the new application situation is introduced.

关键词： Bayesian methods Decision theory pattern recognition Error analysis Probability Genetics Testing Data analysis pattern analysis pattern classification

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Wavelet soft-threshold method for determining an unknown source in a diffusion equation

Wavelet soft-threshold method for determining an unknown sou...

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international conference on Wavelet Analysis and pattern recognition (ICWAPR)

作者： Jin-Ru Wang Department of Applied Mathematics Beijing University of Technology Beijing China

We consider the problem of determining an unknown source, which depends only on the spatial variable, in a diffusion equation. This is an ill-posed problem. For a reconstruction of the solution from indirect data, the dual least squares method generated by the family of Shannon wavelet subspaces is applied. Moreover, a certain simple nonlinear modification of the method based on local refinements of the wavelet expansion of the noisy data is investigated.

关键词： Equations Wavelet analysis pattern analysis pattern recognition

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Quaternion K-L transform and Biomimetic pattern recognition approaches for color-face recognition

Quaternion K-L transform and Biomimetic pattern recognition ...

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IEEE international conference on Intelligent computing and Intelligent Systems (ICIS)

作者： Lijun Ding Hao Feng Jiaxing University Jiaxing China

ISBN: (纸本)9781424447374;9781424447541

This paper proposed a method of quaternion K-L transform and biomimetic pattern recognition (BPR) for color face recognition. The BPR aimed at optimal covering in the feature space R n using some complex geometric bodies for cover samples distribution in R n approximately in order to ¿recognize¿. We used quaternion K-L transform to extract the Eigen-faces of training samples and algebraic feature components of each sample for training by BPR. The method was applied to the face database ¿faces94¿ in Essex University, and the experiments results indicated that the correct recognition rate reached to 96.67%, and proved its efficiency and feasibility for color face recognition, and also showed it was better than the performance of SVM.

关键词： pattern recognition Quaternions Biomimetics Face recognition Color Business process re-engineering Data mining Artificial neural networks Spatial databases Support vector machines

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Multimodal real-time conversation analysis using a novel process engine

Multimodal real-time conversation analysis using a novel pro...

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international conference on Affective computing and Intelligent Interaction and Workshops, ACII

作者： Stefan Scherer Volker Fritzsch Friedhelm Schwenker Institute of Neural Information Processing Ulm University Germany

This contribution introduces a software framework enabling researchers to develop real-time pattern recognition and sensor fusion applications in an abstraction level above that of common programming languages in order to reduce and minimize programming errors and technical obstacles. Furthermore, a proof of concept using two separate instances of the process engine on different computers with audiovisual data processing is described. The scenario shows the capability of the engine to process data in realtime and synchronously on multiple machines, which are necessary features in large scale projects.

关键词： Engines Information analysis Prototypes Java pattern analysis pattern recognition Application software Data processing Emotion recognition Automatic speech recognition

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An X-Ray Computed Tomography Technique for the Measurement of Packing Density in Granular Particles

An X-Ray Computed Tomography Technique for the Measurement o...

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26th IEEE international Instrumentation and Measurement Technology conference

作者： Bloom, Michael Russell, Michael Kustau, Aliaksei Mandayam, Shreekanth Sukumaran, Beena Rowan Univ Coll Engn Glassboro NJ 08028 USA

ISBN: (纸本)9781424433520

It has been shown that the flow and shear characteristics of granular particles such as soils are significantly dependent on the shape of the particles. This is important from a practical viewpoint because a fundamental understanding of granular behavior will lead to an improved understanding of soil stability and influence the design of structural foundations. Furthermore, the calculation of soil stability and consequently structural stability is particularly useful during earthquake events. In previous work, we have demonstrated the applicability of X-ray and optical' tomography measurements for characterizing 3-D shapes of natural sands and manufactured granular particles. In this paper, we extend the work to measure the arrangement and orientation of an assemblage of such particles. A combination of X-ray CT for measuring the coordinates of the individual particles, and basic image processing techniques for computing the local variations in packing density are employed to generate density maps. Such maps can be used to gain a more fundamental understanding of the shear characteristics of granular particles. In this paper, we demonstrate the success of our technique by exercising the method on two sets of granular particles - glass beads (used as a control) and Michigan Dune sand.

关键词： image processing pattern recognition mechanical measurements material analysis

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Application of adaptive neuro fuzzy inference system to the partial discharge pattern recognition

Application of adaptive neuro fuzzy inference system to the ...

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IEEE international conference on Intelligent computing and Intelligent Systems (ICIS)

作者： Canxin Guo Li Zhang Yong Qian Chengjun Huang Hui Wang Linpeng Yao Xiuchen Jiang Department of Electrical Engineering Shanghai Jiaotong University Shanghai China Shanghai Municipal Electrical Power Company Shanghai China

ISBN: (纸本)9781424447374;9781424447541

The application of adaptive neuro fuzzy inference system (ANFIS) to the partial discharge (PD) pattern recognition is presented in this paper. Four types of defect models are made according to the main reason of insulation failures in real power system. Experiments are carried out to acquire the sample data, from which eight statistical features are extracted to construct the ANFIS. Different characteristics of the proposed defect models are compared based on the extracted features. Then the ANFIS is trained by characteristic features. Testing samples are utilized to validate the performance of the recognition system. The result shows that ANFIS reaches a successful recognition rate in the application of PD pattern classification.

关键词： Fuzzy systems Partial discharges pattern recognition Fuzzy logic Circuit testing Voltage Power system modeling Feature extraction Data mining System testing

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Practical contour segmentation algorithm for small animal digital radiography image

Practical contour segmentation algorithm for small animal di...

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7th international conference on Photonics and Imaging in Biology and Medicine

作者： Zheng, Fang Hui, Gong Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan Hubei China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan Hubei China

ISBN: (纸本)9780819475398

In this paper a practical, automated contour segmentation technique for digital radiography image is described. Digital radiography is an imaging mode based on the penetrability of x-ray. Unlike reflection imaging mode such as visible light camera, the pixel brightness represents the summation of the attenuations on the photon thoroughfare. It is not chromophotograph but gray scale picture. Contour extraction is of great importance in medical applications, especially in non-destructive inspection. Manual segmentation techniques include pixel selection, geometrical boundary selection and tracing. But it relies heavily on the experience of the operators, and is time-consuming. Some researchers try to find contours from the intensity jumping characters around them. However these characters also exist in the juncture of bone and soft tissue. The practical way is back to the primordial threshold algorithm. This research emphasizes on how to find the optimal threshold. A high resolution digital radiography system is used to provide the oriental gray scale image. A mouse is applied as the sample of this paper to show the feasibility of the algorithm. © 2009 SPIE.

关键词： Medical applications

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