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检索条件"任意字段=International Conference on Soft Computing and Pattern Recognition, SoCPaR 2009"

共 250 条记录，以下是101-110 订阅

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On approach to vision based fire detection based on type-2 fuzzy clustering

On approach to vision based fire detection based on type-2 f...

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international conference of soft computing and pattern recognition (socpar)

作者： Ha Dai Duong Dinh Dung Nguyen Long Thanh Ngo Dao Thanh Tinh Department of Information Systems Faculty of Information Technology Le Quy Don Technical University Hanoi Vietnam

During the last ten years computer vision techniques have shown a great potential in solving the problem of automatic fire detection. Vision-based fire detection offers many advantages over the conventional methods that use smoke and heat detectors. This paper presents a novel approach for fire detection by modeling the structure of spatial of fire, this structure is considered in terms of the color intensity of fire pixels. Furthers the type-2 fuzzy clustering technique is applied to separate fire-color pixels into some clusters, then these clusters are used to model structure of fire. Experimental results show that our method is capable of detecting fire in early state of fire and weak light-intensity environment; and this method uses only information on a single image so it can be integrated into the surveillance system that used dynamic camera.

关键词： Clustering algorithms Image color analysis Histograms pattern recognition Fuzzy sets Fires Shape

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Variational stereo matching with left right consistency constraint

Variational stereo matching with left right consistency cons...

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international conference of soft computing and pattern recognition (socpar)

作者： Wenqiao Zhu Dongming Lu Changyu Diao Jingzhou Huang College of Computer Science Zhejiang University Hangzhou China

Variational methods are one of the most useful techniques for stereo matching. Those methods usually take the following pipeline: first, the disparity is embedded in a functional; second, minimizing the functional is converted to solve an Euler-Lagrange(EL) function; third, fix point algorithm or other numerical algorithms are used to solve the EL function in a digital computer. If the functional is not convex, the solution easily bias towards a local minimal solution. Our work in this paper is to alleviate these biases. We model the disparity function in a maximum a posteriori(MAP) continuous Markov random field(MRF) framework and a symmetric functional is then deduced. In such a functional, more constraints can be applied to restrict the solution space. Left-right consistency constraints is introduced as a prior energy in our functional. Experiments on the test images from the Middlebury website show that the proposed functional gives less biases than the previously used one.

关键词： Stereo vision Computer vision Optical imaging Mathematical model pattern recognition Equations conferences

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Background modeling using mixture of Gaussians and Laplacian pyramid decomposition

Background modeling using mixture of Gaussians and Laplacian...

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international conference of soft computing and pattern recognition (socpar)

作者： Minyong Wan Xing Qin Lenian He Institute of VLSI Design Zhejiang University Hangzhou Zhejiang China Institute of micro-electronic CAD center Hangzhou Dianzi University Hangzhou Zhejiang China

We propose an effective background model using mixture of Gaussians and Laplacian pyramid decomposition for foreground object segmentation from complex scene containing stationary and moving objects. The Laplacian pyramid is employed to decompose the input image into a low-frequency big scale image and a high-frequency image. We build two mixtures of Gaussians in each pixel to represent the statistical characteristics of the stationary and moving points with proper feature vectors. Big scale foreground objects are obtained by fusing the results from stationary and dynamic models. Original foreground objects are then restored using the established model, low-frequency and high-frequency images. The experiments we performed here on complex scenes containing dynamic background objects have showed better performance and less memory cost compared.

关键词： Image color analysis Laplace equations Adaptation models Computational modeling pattern recognition Analytical models Real time systems

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A study on the effect of Vmax in Artificial physics optimization algorithm with high dimension

A study on the effect of Vmax in Artificial physics optimiza...

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international conference of soft computing and pattern recognition (socpar)

作者： Liping Xie Ying Tan Jianchao Zeng Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Taiyuan Shanxi China

Velocity threshold v max is an important parameter of Artificial physics optimization. Different from other parameters, it affects the algorithm performance by restricting the moving size and direction of each particle. Because of the complex optimisation problems, the proper v max setting may provide a reasonable solution within an allowed generation. However, up to now, there are only few scholars who are concerned in this problem. Therefore, in this paper, the authors investigate two selection principles of v max (a canstant v max and an adaptive v max ) with high dimension on numerical optimisation problems. To make a deep insight, the test suit consists of three different type benchmarks: unimodel, multi-modal functions with a few local optima and multi-modal functions with many local optima. Simulation results show an adaptive v max may generally obtain the satisfied solution within the allowed iterations.

关键词： Force Optimization Vectors Benchmark testing pattern recognition Algorithm design and analysis

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QoS-oriented Service Management in clouds for large scale industrial activity recognition

QoS-oriented Service Management in clouds for large scale in...

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international conference of soft computing and pattern recognition (socpar)

作者： Athanasios S. Voulodimos Dimosthenis P. Kyriazis Spyridon V. Gogouvitis Anastasios D. Doulamis Dimitrios I. Kosmopoulos Theodora A. Varvarigou National Technical University of Athens Greece Technical University of Crete Greece University of Texas Arlington USA

Motivated by the need of industrial enterprises for supervision services for quality, security and safety guarantee, we have developed an Activity recognition Framework based on computer vision and machine learning tools, attaining good recognition rates. However, the deployment of multiple cameras to exploit redundancies, the large training set requirements of our time series classification models, as well as general resource limitations together with the emphasis on real-time performance, pose significant challenges and lead us to consider a decentralized approach. We thus adapt our application to a new and innovative real-time enabled framework for service-based infrastructures, which has developed QoS-oriented Service Management mechanisms in order to allow cloud environments to facilitate real-time and interactivity. Deploying the Activity recognition Framework in a cloud infrastructure can therefore enable it for large scale industrial environments.

关键词： Monitoring Computer vision Hidden Markov models Cameras Real time systems pattern recognition Quality of service

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Machine learning from imbalanced data sets for astronomical object classification

Machine learning from imbalanced data sets for astronomical ...

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international conference of soft computing and pattern recognition (socpar)

作者： Jorge de la Calleja Antonio Benitez Ma. Auxilio Medina Olac Fuentes Departamento de Informática Universidad Politécnica de Puebla Puebla Mexico Department of Computer Science University of Texas El Paso USA

In this paper we present an experimental study of machine learning from imbalanced data sets applied to the difficult problem of astronomical object classification in multi-spectral wide-field images. The imbalanced data set problem is very common in several domains, and occurs when there are many more examples of some classes than others; therefore, classifiers perform poorly on these data sets. In order to improve the performance of machine learning algorithms over minority class examples, we propose to create new instances using a modification of the well-known SMOTE technique, but only of those misclassified examples given by an ensemble of classifiers. Our preliminary experimental results show that the proposed approach obtain above. 700 using recall, precision and f-measure as metrics for evaluation; using small data sets.

关键词： Machine learning Accuracy Training Measurement Neural networks pattern recognition Linear regression

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Using voronoi grouping to solve set covering problem - Building an Integrated Delivering System for Taichung City

Using voronoi grouping to solve set covering problem - Build...

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international conference of soft computing and pattern recognition (socpar)

作者： Dah-Ming Shiah Chin-Tun Hung Kuang-Hua Huang Graduate Institute of Architecture and Urban Design Chao Yang University of Technology Taichung Taiwan Department of Healthcare Administration Central Taiwan University of Sciences and Technology Taichung Taiwan Department of Health Services Administration China Medical University Taichung Taiwan

The traditional set covering models did not consider the equity of users that carried by each set. We developed a new method to solve that for the Integrated Delivering System (IDS) in medical service in Taichung city. This method requires grouping the clinics in equal market share in a way that satisfy the equity conditions given. It uses Voronoi blocks as market share for each clinic within the study area and calculates the house and clinic ratios as the equity conditions. For each scan, the selected block merges with one of the adjacent blocks using five conditions with different weights. This process continues until the objective functions are met. This model opens up a new way of thinking for set covering by grouping the block objects to obtain optimal result. The result is workable and can be reproduced by computer program if the same data set existed.

关键词： Mathematical model Cities and towns Computational modeling Urban areas Hospitals pattern recognition

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Logical handling of analogical proportions in commonsense and transductive reasoning

Logical handling of analogical proportions in commonsense an...

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international conference of soft computing and pattern recognition (socpar)

作者： Henri Prade Gilles Richard Institut de Recherche en Informatique de Toulouse-IRIT Université de Toulouse Toulouse France

Analogies play an important role in many reasoning tasks. The presentation will introduce a recently proposed modeling for analogical proportions, i.e. statements of the form A is to B as C is to D, which put four situations into comparative relations. The logical representation used for encoding such proportions takes both into account what the four situations have in common and how they differ. Thanks to the use of a propositional logic modeling extended with suitable fuzzy logic connectives, the approach can deal with situations described by features that may be binary- or multiple-valued. Moreover, it will be shown that analogical proportions are related to other logically expressible proportions that are also of interest. The presentation will emphasize the interest of the approach for handling a large variety of forms of reasoning ranging from the solving of IQ tests, to case-based reasoning, interpolative and extrapolative reasoning, and transductive reasoning for classification. Potential for analogical argumentation will be also briefly discussed. The approach does not just rely on the exploitation of similarities between two cases, but rather takes advantage of the parallel made between a situation to evaluate or complete and three other situations.

关键词： Cognition Equations Vectors Encoding pattern recognition Interpolation Semantics

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A principled discussion of information combination rules in different representation settings

A principled discussion of information combination rules in ...

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international conference of soft computing and pattern recognition (socpar)

作者： Didier Dubois Weiru Liu Jianbing Ma Henri Prade Institut de Recherche en Informatique de Toulouse-IRIT Université de Toulouse Toulouse France School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast UK

Depending on the representation setting, different combination rules have been proposed for fusing information from distinct sources. Moreover in each setting, different sets of axioms that combination rules should satisfy have been advocated, thus justifying the existence of alternative rules (usually motivated by situations where the behavior of other rules was found unsatisfactory). These sets of axioms are usually purely considered in their own settings, without in-depth analysis of common properties essential for all the settings. This paper introduces core properties that, once properly instantiated, are meaningful in different representation settings ranging from logic to imprecise probabilities. The following representation settings are especially considered: classical set representation, possibility theory, and evidence theory which is rich enough to include as particular cases i) sets (when there is one focal element), ii) probabilities (when focal elements are singletons), and iii) possibilities (when focal elements are nested). This unified discussion of combination rules across different settings is expected to provide some fresh look on some old but basic issues in information fusion.

关键词： Merging Possibility theory Reliability Frequency modulation pattern recognition Distance measurement Uncertainty

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Cellular Learning Automata with External Input and Its Applications in pattern recognition

Cellular Learning Automata with External Input and Its Appli...

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5th international conference on soft computing, computing with Words and Perceptions in System Analysis, Decision and Control

作者： Ahangaran, Meysam Beigy, Hamid Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9781424434299

Cellular learning automata (CLA) which has been introduced recently, is a combination of cellular automata (CA) and learning automata (LA). A CLA is a CA in which a LA is assigned to its every cell. The LA residing in each cell determines the state of the cell on basis of its action probability vector. Like CA, there is a local rule that CLA operates under it. In this paper we introduce a new model of CLA in which each cell gets an external input vector from the environment in addition to reinforcement signal, so this model can work in non-stationary environments. Then two applications of the new model on image segmentation and clustering are given, and the results show that the proposed algorithm outperforms the similar algorithms.

关键词： Cells

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