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pattern recognition of EMG data subjected for further processing

Pattern recognition of EMG data subjected for further proces...

引用

2010 international conference on Image Processing, Computer Vision, and pattern recognition, IPCV 2010

作者： Mitzev, Ivan Younan, N.H. Department of Electrical and Computer Engineering Mississippi State University Mississippi State MS 39762 United States

ISBN: (纸本)9781601321541

EMG classification is widely used in electric control of mechanically developed prosthesis, robots development, clinical application etc. It has been evaluated for years and varieties of algorithms are developed to serve different goals. In this paper, we develop an easy to implement and fast to execute pattern recognition method for classifying signals used for human gait analysis. This method is based on adding two new temporal features (form factor and standard deviation) for EMG signal recognition and using them along with several popular features (area under me curve, wavelength function-pathway and zero crossing rate) to come up with a low complexity suitable feature extraction. Results are presented for EMG data and comparison with existing methods is made to validate the applicability of the foregoing method.

关键词： Feature extraction

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Possibilistic contextual skylines with incomplete preferences

Possibilistic contextual skylines with incomplete preference...

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international conference of soft computing and pattern recognition (SOCPAR)

作者： A. Hadjali O. Pivert H. Prade ENSSAT IRISA University of Rennes I Lannion France IRIT CNRS University of Toulouse III Toulouse France

We propose a possibility theory-based approach to the treatment of missing user preferences in skyline queries. To compensate this lack of knowledge, we show how a set of plausible preferences suitable for the current context can be derived either in a case-based reasoning manner, or using an extended possibilistic logic setting. Uncertain dominance relationships are defined in a possibilistic way and the notion of possibilistic contextual skyline is introduced. This kind of skyline allows us to return the tuples that are non-dominated with a high certainty. The paper also includes a structured overview of the different types of “fuzzy” skylines.

关键词： Context Uncertainty Databases Business pattern recognition Possibility theory Probabilistic logic

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An efficient method for real-time activity recognition

An efficient method for real-time activity recognition

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international conference of soft computing and pattern recognition (SOCPAR)

作者： Samy Sadek Ayoub Al-Hamadi Bernd Michaelis Usama Sayed Institute of Electronics Signal Processing and Communications (IESK) Otto-von-Guericke University of Magdeburg Magdeburg Germany Department of Electrical Engineering Assiut University Egypt

Real-time feature extraction is a key component for any action recognition system that claims to be truly real-time. In this paper we present a conceptually simple and computationally efficient method for real-time human activity recognition based on simple statistical features. Such features are very cheap to compute and form a relatively low dimensional feature space in which classification can be carried out robustly. On the Weizmann dataset, the proposed method achieves encouraging recognition results with an average rate up to 97.8%. These results are in a good agreement with the literature. Further, the method achieves real-time performance, and thus can offer timing guarantees to real-time applications.

关键词： Feature extraction Humans Real time systems Video sequences pattern recognition Shape Support vector machines

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Providing PRTools with Fuzzy Rule-Based Classifiers

Providing PRTools with Fuzzy Rule-Based Classifiers

引用

2010 IEEE World Congress on Computational Intelligence

作者： Cococcioni, Marco D'Andrea, Eleonora Lazzerini, Beatrice Dipartimento Ingn Informaz Elettron I-56122 Pisa Italy

ISBN: (纸本)9781424469208

This paper first reviews the state-of-the-art of fuzzy rule-based classifiers (FRBCs), then it discusses how to implement an FRBC under the pattern recognition Toolbox (PRTools), the de-facto standard toolbox for classification in Matlab. Such an implementation, called frbc, allows for a straightforward comparison of frbc with other classifiers already available under the PRTools. Furthermore, frbc can easily be used and combined with any other general-purpose function already available in PRTools. In this way, e. g., it becomes really easy to perform many types of feature selection, based on the accuracy achieved by frbc on the subset of features at hand. Another useful feature is the capability to export each FRBC generated by frbc as a standard Fuzzy Inference System (FIS) structure used within the Matlab Fuzzy Logic Toolbox (FLT): this allows comparisons/validations, visual inspection of the rule base, etc. In the experimental part we first assess the correctness of the implementation, by reproducing results existing in the literature. Then we show some examples of usage of frbc, combined with existing PRTools functions.

关键词： Matlab Fuzzy Logic Toolbox pattern recognition Toolbox feature selection fuzzy inference system structure fuzzy logic fuzzy reasoning fuzzy rule-based classifier mathematics computing pattern classification

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Feature extraction based on fuzzy bidirectional maximum margin criterion for face recognition

Feature extraction based on fuzzy bidirectional maximum marg...

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2010 IEEE international conference on Intelligent computing and Intelligent Systems, ICIS 2010

作者： Du, Haishun Guo, Xiaobo Zhang, Fan Zhao, Jianjun Institute of Image Processing and Pattern Recognition Henan University Kaifeng Henan China School of Mechanical and Power Engineering Hennan Polytechnic University Jiaozuo Henan China

ISBN: (纸本)9781424465835

This paper proposes a new method for feature extraction and recognition, namely, the fuzzy bidirectional maximum margin criterion (FBMMC) based on the maximum margin criterion and fuzzy set theory. In FBMMC, a membership grade matrix is calculated by the FKNN algorithm, and then the membership grades are incorporated into the formulation of the scatter matrices to formulate the row and column directional fuzzy scatter matrices. Using the fuzzy scatter matrix, FBMMC performs fuzzy image MMC in the row and column directions simultaneously. Experiments on the ORL and Yale face databases show the effectiveness of the proposed FBMMC based feature extraction method. ©2010 IEEE.

关键词： Face recognition

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Iris features extraction using dual-tree complex wavelet transform

Iris features extraction using dual-tree complex wavelet tra...

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international conference of soft computing and pattern recognition (SOCPAR)

作者： Waheeda Almayyan Hala S. Own Hussein Zedan Software Technology Research Laboratory De Montfort University Leicester UK Department of Solar and Space Research National Research Institute of Astronomy and Geophysics Cairo Egypt

This paper presents an iris recognition method based on the two dimensional dual-tree complex wavelet transform (2D-CWT) and the support vector machines (SVM). 2D-CWT has such significant properties as the approximate shift-invariance, high directional selectivity and computationally much more efficient. These properties are very useful in invariant iris recognition. SVM is used as a classifier and several kernel functions are tested in the experiments. The obtained experimental results showed that the proposed approach enhanced the classification accuracy. The experimental results were also compared with the k-NN and Naïve Bayes classifiers to demonstrate the efficacy of the proposed technique.

关键词： Iris recognition Support vector machines Kernel Wavelet transforms pattern recognition

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Describing acceptable objects by means of Sugeno integrals

Describing acceptable objects by means of Sugeno integrals

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international conference of soft computing and pattern recognition (SOCPAR)

作者： Henri Prade Agnès Rico Institut de Recherche en Informatique de Toulouse (IRIT) Université Paul Sabatier Toulouse France Équipe de Recherche en Ingénierie des Connaissances (ERIC) Université Claude Bemard Lyon 1 Lyon France

Objects are usually described by combinations of properties. Logic-based descriptions offer compact representations for binary properties. Besides, Sugeno integrals are well-known as a powerful qualitative aggregation tool in multiple-criteria decision, which is applicable to gradual properties, and takes into account positive synergies between properties. The paper proposes to investigate the potential use of Sugeno integrals as a representation tool, to lay bare their relation with possibilistic logic representations, and to discuss the handling of negative synergies in this setting using a pair of Sugeno integrals.

关键词： pattern recognition Semantics Lattices Context Polynomials Complexity theory

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Fast Polar Harmonic Transforms

Fast Polar Harmonic Transforms

引用

11th international conference on Control, Automation, Robotics and Vision (ICARCV 2010)

作者： Yang, Zhuo Kamata, Sei-ichiro Waseda Univ Grad Sch Informat Prod & Syst Tokyo Japan

ISBN: (纸本)9781424478132

Polar Harmonic Transform (PHT) is termed to represent a set of transforms those kernels are basic waves and harmonic in nature. PHTs consist of Polar Complex Exponential Transform (PCET), Polar Cosine Transform (PCT) and Polar Sine Transform (PST). They are proposed to represent invariant image patterns for two dimensional image retrieval and pattern recognition tasks. They are demonstrated to show superiorities comparing with other methods on describing rotation invariant patterns for images. Kernel computation of PHTs is also simple and has no numerical stability issue. However in order to increase the computation speed, fast computation method is needed especially for real world applications like limited computing environments, large image databases and realtime systems. This paper presents Fast Polar Harmonic Transforms (FPHTs) including Fast Polar Complex Exponential Transform (FPCET), Fast Polar Cosine Transform (FPCT) and Fast Polar Sine Transform (FPST) that are deduced based on mathematical properties of trigonometric functions. The proposed FPHTs are averagely over 6 similar to 8 times faster than PHTs that significantly boost computation process. The experimental results on both synthetic and real data are given to illustrate the effectiveness of the proposed fast transforms.

关键词： Fast Polar Harmonic Transform Fast Polar Complex Exponential Transform Fast Polar Cosine Transform Fast Polar Sine Transform image retrieval pattern recognition

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Using NCL, an effective way to improve combination methods of neural classifiers

Using NCL, an effective way to improve combination methods o...

引用

international conference of soft computing and pattern recognition (SOCPAR)

作者： Arash Iranzad Saeed Masoudnia Fatemeh Cheraghchi Abbas Nowzari Reza Ebrahimpour Computer Science Department University of Tehran Tehran Iran ECE Department Shahid Rajaee University Tehran Iran

This paper investigates the effect of diversity caused by Negative Correlation Learning (NCL) in the combination of neural classifiers and presents an efficient way to improve combining performance. Decision Templates and Averaging, as two non-trainable combining methods and Stacked Generalization as a trainable combiner are investigated in our experiments. Utilizing NCL for diversifying the base classifiers leads to significantly better results in all employed combining methods. Experimental results on five datasets from UCI repository indicate that by employing NCL, the performance of the ensemble structure can be more favorable compared to that of an ensemble use independent base classifiers.

关键词： Diversity reception Classification algorithms Training Artificial neural networks Correlation Sonar pattern recognition

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recognition of signed expressions using visually-Oriented subunits obtained by an immune-Based optimization

Recognition of signed expressions using visually-Oriented su...

引用

international conference of soft computing and pattern recognition (SOCPAR)

作者： Mariusz Oszust Marian Wysocki Department of Computer and Control Engineering Rzeszow University of Technology Rzeszow Poland

The paper considers automatic visual recognition of signed expressions. The proposed method is based on modeling gestures with subunits, which is similar to modeling speech by means of phonemes. To define the subunits a data-driven procedure is applied. The procedure consists in partitioning time series, extracted from video, into subsequences which form homogeneous groups. The cut points are determined by an immune optimization procedure based on quality assessment of the resulting clusters. In the paper the problem is formulated, its solution method is proposed and experimentally verified on a database of 100 Polish words. The results show that our subunit-based classifier outperforms its whole-word-based counterpart, which is particularly evident when new words are recognized on the basis of a small number of examples.

关键词： Optimization Hidden Markov models pattern recognition Handicapped aids Training Feature extraction Clustering algorithms

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