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检索条件"机构=Pattern Analysis and Machine Intelligence Laboratory Department of Systems Design Engineering"

共 91 条记录，以下是51-60 订阅

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Increasing Object Recognition Rate using Reinforced Segmentation

Increasing Object Recognition Rate using Reinforced Segmenta...

引用

IEEE International Conference on Image Processing

作者： Farhang Sahba Hamid R. Tizhoosh Magdy M.A. Salama Systems Design Engineering University of Waterloo Waterloo ONT Canada Electrical and Computer Engineering Pattern Analysis and Machine Intelligence Laboratory Waterloo ONT Canada

In this paper a new approach to object extraction and recognition based on reinforcement learning is presented. We use this novel idea as a method to optimally segment the image and increase the recognition rate. The ... 详细信息

关键词： Object recognition Learning Image segmentation Data mining design engineering pattern analysis machine intelligence Laboratories Image recognition Object detection

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A novel computational intelligence approach for multiple-point routing in communication networks

A novel computational intelligence approach for multiple-poi...

引用

IEEE International Conference on systems, Man and Cybernetics

作者： S. Al-Sharhan F. Karray W. Gueaieb Department of Systems Design Engineering University of Waterloo Ontario Canada Pattern Analysis and Machine Intelligence (PAMI) Lab Department of Systems Design Engineering University of Waterloo Waterloo ONT Canada

A new evolutionary algorithm for the constrained multiple destination routing problem is presented. The constrained multicast problem is characterized by a minimum cost multicast tree and a bounded end-to-end delay. It has been proven that this problem is NP-complete. The proposed algorithm is based on a soft computing technique that integrates in an efficient manner the merits of genetic algorithms and concepts of the competitive learning in the artificial neural networks literature. A population based learning algorithm is utilized, among other techniques, to construct a delay bounded multicast tree. A salient feature of the algorithm is the adaptive learning concept that achieves an efficient trade-off between the exploration and exploitation of the search space.

关键词： Computational intelligence Routing Communication networks Multicast algorithms Delay Evolutionary computation Costs Computer networks Genetic algorithms Artificial neural networks

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Improving gradient-based learning algorithms for large scale feedforward networks

Improving gradient-based learning algorithms for large scale...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： M. Ventresca H. R. Tizhoosh Faculty member of the Pattern Analysis and Machine Intelligence laboratory and Opposition-Based Learning Groups in the Systems Design Engineering Department University of Waterloo Waterloo ONT Canada

Large scale neural networks have many hundreds or thousands of parameters (weights and biases) to learn, and as a result tend to have very long training times. Small scale networks can be trained quickly by using second-order information, but these fail for large architectures due to high computational cost. Other approaches employ local search strategies, which also add to the computational cost. In this paper we present a simple method, based on opposite transfer functions which greatly improve the convergence rate and accuracy of gradient-based learning algorithms. We use two variants of the backpropagation algorithm and common benchmark data to highlight the improvements. We find statistically significant improvements in both convergence speed and accuracy.

关键词： Large-scale systems Transfer functions Neural networks Feedforward neural networks Computer architecture Computational efficiency Convergence Backpropagation algorithms Computer networks design engineering

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Quasi-global oppositional fuzzy thresholding

Quasi-global oppositional fuzzy thresholding

引用

IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： Hamid R. Tizhoosh Farhang Sahba Pattern Analysis and Machine Intelligence Laboratory Systems Design Engineering University of Waterloo Waterloo ONT Canada University of Toronto Toronto ONT Canada

Opposition-based computing is the paradigm for incorporating entities along with their opposites within the search, optimization and learning mechanisms. In this work, we introduce the notion of "opposite fuzzy sets" in order to use the entropy difference between a fuzzy set and its opposite to carry out object discrimination in digital images. A quasi-global scheme is used to execute the calculations, which can be employed by any other existing thresholding technique. Results for prostate ultrasound images have been provided to verify the performance whereas expert's markings have been used as gold standard.

关键词： Fuzzy sets Image segmentation Fuzzy systems machine intelligence Ultrasonic imaging Neural networks pattern analysis Laboratories System analysis and design design engineering

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Urban land-cover classification based on swarm intelligence from high resolution remote sensing imagery

Urban land-cover classification based on swarm intelligence ...

引用

International Conference on Remote Sensing, Environment and Transportation engineering (RSETE)

作者： Safaa M. Bedawi Mohamed S. Kamel Pattern analysis and Machine Intelligence Laboratory Department of Electrical and Computer Engineering University of Waterloo Waterloo ONT Canada

Urban land-cover classification is one of the most challenging problems in pattern analysis and machine intelligence systems in remote sensing. Dense urban environment sensed by very high-resolution (VHR) optical sensors is even more challenging. Occlusions and shadows due to buildings and trees hide some objects in the scene. Despite its simplicity and usefulness, conventional classification methods have failed to have a high classification accuracy in dense urban areas. The objective of this study is to improve the quality of the land-cover classification. We propose using a Particle Swarm Optimization (PSO) based algorithm for the classification of VHR (0.1 to 1 m) remote sensing data over urban areas. This method is to discover classification rules through simulating the social behavior of animals such as the behaviours of bird flocking. The results for aerial images classification show the significance of this method. PSO-based classifier has been applied to the classification of remote sensing data in dense urban district of Kitchener-Waterloo and has achieved high predictive accuracy of 90%.

关键词： Remote sensing Classification algorithms Particle swarm optimization Training Accuracy Urban areas Artificial neural networks

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Search Difficulty of Two-Connected Ring-based Topological Network designs

Search Difficulty of Two-Connected Ring-based Topological Ne...

引用

IEEE Symposium on Foundations of Computational intelligence, FOCI

作者： Beatrice Ombuki-Berman Mario Ventresca Computer Science Department Brock University Saint Catharines ONT Canada Pattern Analysis and Machine Intelligence (PAMI) laboratory in the Systems Design Engineering Department University of Waterloo Waterloo ONT Canada

Ring-based network design problems have many important applications, especially in the fields of logistics and telecommunications. This paper focuses on the recently proposed two-connected network with bounded rings problem. We investigate the search difficulty of both the Euclidean edge and unit edge length ring size bounds flavors of this problem by performing an information-theoretic fitness landscape analysis for several instances of the problem. Our results further confirm the hypothesis that the unit edge length version of the problem is indeed more difficult. The investigation also further reveals that smaller sized ring bounds lead to harder problems for Euclidean edge lengths. However, for unit edge lengths we did not establish such a relationship

关键词： Logistics Performance analysis Telecommunication network topology Costs Telecommunication traffic Computational intelligence Information analysis Helium Abstracts design optimization

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Learning social networks using multiple resampling method

Learning social networks using multiple resampling method

引用

IEEE International Conference on systems, Man and Cybernetics

作者： Masoud Makrehchi Mohamed S. Kamel Pattern Analysis and Machine Intelligence Laboratory Department of Electrical and Computer Engineering University of Waterloo Waterloo ONT Canada

Automatic building of social networks requires extracting pair-wise relations of the individuals. In this paper, supervised learning of social networks from a set of documents is proposed. Given a small subset of relations between the individuals, the problem of learning social network is translated into a text classification problem. Relation between each pair of individuals is represented by a vector of words produced from merging all documents associated with these two individuals. The known relation is used as a label for the relation vector. The merged documents and their given labels, are used as training data. By this transformation, a text classifier such as SVM can be used for learning the unknown relations. We show that there is a link between the intrinsic sparsity of social networks and class distribution imbalance of the training data. In order to re-balance the unbalanced training data, a multiple resampling method, including undersampling of the majority and oversampling of the minority class, is employed. The proposed framework is applied to a friend of a friend (FOAF) data set and evaluated by the macro-averaged F-measure.

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A sensor model registration technique for mobile robot localization

A sensor model registration technique for mobile robot local...

引用

IEEE International Symposium on Intelligent Control (ISIC)

作者： S.Y.T. Lang A.K.C. Wong Autonomous Systems Laboratory Institute of In formation Technology National Research Council Canada Ottawa ONT Canada Pattern Analysis and Machine Intelligence Laboratory Department of Systems Design Engineering University of Waterloo Waterloo ONT Canada

A method for mobile robot localization using natural environmental features is presented. An attributed graph boundary representation of free space is used. A local sensor model is registered with a global model by a subgraph isomorphism technique. Geometric and structural knowledge is used to search for mappings. The set of possible mappings is incrementally determined. Simultaneously, the associated coordinate transformation between the global and local models is refined. The model registration technique is implemented in software, and two examples are shown.< >

关键词： Mobile robots Sensor phenomena and characterization Dead reckoning Sonar navigation Councils Wires Laboratories Information technology Space technology Calibration

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Random finite set theoretic based soft/hard data fusion with application for target tracking

Random finite set theoretic based soft/hard data fusion with...

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International Conference on Multisensor Fusion and Integration for Intelligent systems (MFI)

作者： Bahador Khaleghi Alaa Khamis Fakhreddin Karray Pattern Analysis and Machine Intelligence (PAMI) Laboratory Department of Electrical and Computer Engineering University of Waterloo Waterloo ONT Canada

This paper reports on ongoing research on development of data fusion systems capable of processing soft as well as hard data. Such fusion systems are distinguished from the conventional systems where input data are assumed to be provided by typically well-characterized electronic sensor systems. The incorporation of soft human-generated data into fusion process is an emerging trend in fusion community majorly motivated by asymmetric warfare situations where observational opportunities for traditional hard sensors is restricted. Random finite set theory is a mathematical framework with powerful representational and computational abilities making it a promising approach to address several fundamental challenges in soft/hard fusion systems. In this paper the first prototype soft/hard fusion system based on random finite set theory is described. Experimental results obtained using the developed system prove the plausibility as well as efficiency of a random finite set theoretic approach to fusion of soft/hard data.

关键词： Humans Target tracking Data models Observers Robot sensing systems

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Modular neural network architectures for classification

Modular neural network architectures for classification

引用

International Conference on Neural Networks

作者： G. Auda M. Kamel H. Raafat Pattern Analysis And Machine Intelligence Laboratory Systems Design Eneering Department University of Waterloo Canada University of Waterloo Waterloo ON CA Department of Mathematics and Computer Science Kuwait University Kuwait

One of the major drawbacks of the current neural network generation is the inability to cope with the increase of size/complexity of classification tasks. Modular neural network classifiers attempt to solve this problem through a "divide and conquer" approach. However. The performance of the modular neural network classifiers is sensitive to efficiency of the "task decomposition" technique and the "multi-module decision-making" strategy. After a brief review of previous work with emphasis on five published modular classifiers-decoupled nets, ART-BP, hierarchical network, multiple experts, and multiple identical networks (majority vote and average output decisions)-this paper introduces the cooperative modular neural network (CMNN). The CMNN classifier outperforms the surveyed nets due to its novel task decomposition and multi-module decision-making techniques.

关键词： Neural networks Decision making Multi-layer neural network Computer architecture pattern analysis machine intelligence System analysis and design Mathematics Computer science Voting

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