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Image registration using virtual circles and edge direction

Image registration using virtual circles and edge direction

引用

International Conference on pattern Recognition

作者： H.S. Alhichri M. Kamel Pattern Analysis and Machine Intelligence Laboratory Systems Design Engineering University of Waterloo Waterloo ONT Canada

In this paper we combine a novel set of image features called virtual circles with edge direction, to provide an efficient alignment algorithm for image registration under similarity transformations. Virtual circles can be extracted efficiently from many types of images with the help of the distance transform. They can also have extra information, such as their radius and the dominant edge direction in the neighborhood, which can be used for efficient registration. An heuristic called the smoothness criterion is used to choose virtual circles which are less likely to be corrupted by background noise, and to increases the efficiency of the algorithm. Experimental results have shown that this method is highly automatic and has linear complexity in terms of the number of virtual circles extracted.

关键词： Image registration Layout Data mining Switches pattern analysis machine intelligence System analysis and design Design engineering Systems engineering and theory Computer vision

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A coordination mechanism for model-based multi-sensor planning

A coordination mechanism for model-based multi-sensor planni...

引用

IEEE International Symposium on Intelligent Control (ISIC)

作者： M. Kamel L. Hodge Department of Systems Design Engineering Pattern Analysis and Machine Intelligence Laboratory University of Waterloo Waterloo ONT Canada

This paper presents a multi-agent system for coordinating the deployment of multiple sensors in a modeled environment. The sensing task is the maximal sensor coverage of one or more targets in a scene and the position of each sensor is controlled by an autonomous agent. The agents rely on negotiation to achieve the level of coordination necessary to accomplish the given sensing task. Currently, the system focuses on the use of cameras for visual inspection tasks in which a single camera may be inadequate due to occluding objects in the scene, the number of targets to be observed or the sheer size of the target. The paper presents the negotiation mechanism developed for the multi-agent planning of the camera positions and illustrates its effectiveness by an example. Results show that the agents were able to autonomously position the cameras so as to allow for an acceptable level of coverage of the targets being observed.

关键词： Cameras Sensor systems Layout Inspection Intelligent sensors Sensor phenomena and characterization Control systems Art pattern analysis machine intelligence

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Weighted combination of neural network ensembles

Weighted combination of neural network ensembles

引用

International Joint Conference on Neural Networks (IJCNN)

作者： N.M. Wanas M.S. Kamel Pattern Analysis and Machine Intelligence Laboratory Department of Systems Design Engineering University of Waterloo Waterloo ONT Canada

There exist numerous schemes and methods to determine the output of an ensemble of classifiers. The most common approach being the majority vote. Furthermore, we might expect that an improvement can be achieved if there is a method by which we may weigh the members of the ensemble according to their individual performance. The feature based approach presented an architecture that tries to approach this target. However, if there is a way that the final classification may influence these weights we should expect an increased performance in the overall classification task. In this paper we present a new training algorithm that utilizes a feedback mechanism to iteratively improve the classification capability of the feature based approach. This approach is compared with the standard training method as well as standard aggregation schemes for combining classifier ensembles. Empirical results show that this architecture improved on classification accuracy.

关键词： Neural networks Voting Detectors pattern analysis machine intelligence System analysis and design Design engineering Systems engineering and theory Iterative algorithms Jacobian matrices

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Shape representation using concavity graphs

Shape representation using concavity graphs

引用

作者： El Badawy, Ossama Kamel, Mohamed Pattern Analysis and Machine Intelligence Laboratory Systems Design Engineering University of Waterloo 200 University Ave. W. Waterloo Ont. N2L 3G1 Canada

In this paper, a new graph data structure for 2-D shape representation is proposed. The new structure is called a concavity graph, and is an evolution from the already known "concavity tree". Even though a concavity graph bears a fundamental resemblance to a concavity tree, the former is able to describe the shape of multiple objects in an image and their spatial configuration, and is hence inherently more complex. The aim of concavity graphs is two-fold: first we want to analyze the patterns in a multi-object image in a way that will (1) provide better representation of their shapes, and (2) convey useful information about how they "interact" together. Second, we want our analysis technique to facilitate similarity matching behveen two images. This paper introduces the new structure and outlines how it can be used for shape representation as well as similarity matching. © 2002 IEEE.

关键词： pattern recognition

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Shape representation using concavity graphs

Shape representation using concavity graphs

引用

International Conference on pattern Recognition

作者： O. El Badawy M. Kamel Pattern Analysis and Machine Intelligence Laboratory Systems Design Engineering University of Waterloo Waterloo ONT Canada Systems Design Engineering University of Waterloo Waterloo ON Canada

In this paper, a new graph data structure for 2-D shape representation is proposed. The new structure is called a concavity graph, and is an evolution from the already known "concavity tree". Even though a concavity graph bears a fundamental resemblance to a concavity tree, the former is able to describe the shape of multiple objects in an image and their spatial configuration, and is hence inherently more complex. The aim of concavity graphs is two-fold: first we want to analyze the patterns in a multi-object image in a way that will (1) provide better representation of their shapes, and (2) convey useful information about how they "interact" together. Second, we want our analysis technique to facilitate similarity matching between two images. This paper introduces the new structure and outlines how it can be used for shape representation as well as similarity matching.

关键词： Shape Tree graphs Image analysis pattern analysis Data structures machine intelligence Laboratories System analysis and design Design engineering Systems engineering and theory

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A hierarchical genetic algorithm for the design of beta basis function neural network

A hierarchical genetic algorithm for the design of beta basi...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： C. Aouiti A.M. Alimi F. Karray A. Maalej Faculty of Sciences of Bizerta Universite de Carthage Tunisia Department of Electrical Engineering REGIM: Research Group on Intelligent Machines University of Sfax Sfax Tunisia Systems design Engineering Department Pattern Analysis and Machine Intelligence Laboratory University of Waterloo Waterloo ONT Canada Department of Mechanical Engineering University of Sfax ENIS Sfax Tunisia

We propose an evolutionary neural network-training algorithm for beta basis function neural networks (BBFNN). Classic training algorithms for neural networks start with a predetermined network structure. Generally the network resulting from learning applied to a predetermined architecture is either insufficient or over-complicated. This paper describes a hierarchical genetic learning model of the BBFNN. In order to examine the performance of the proposed algorithm, they were used for the approximation problems. The results obtained are very satisfactory with respect to the relative error.

关键词： Genetic algorithms Algorithm design and analysis Neural networks Artificial neural networks Biological cells Neurons Chaos machine intelligence Laboratories Convergence

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Decision fusion in neural network ensembles

Decision fusion in neural network ensembles

引用

International Joint Conference on Neural Networks (IJCNN)

作者： N.M. Wanas M.S. Kamel Pattern Analysis and Machine Intelligence Laboratory Department of Systems Design Engineering University of Waterloo Waterloo ONT Canada

ISBN: (纸本)0780370449

We present a comparison between different combining techniques in neural network ensembles. The main focus of this paper is on a new architecture that can be used in combining neural network ensembles. This architecture is based on training two neural networks to perform the aggregation. One network is trained to establish a confidence factor for each member of the ensemble for every training entry. The other network performs the aggregation of the ensemble to present the final decision. Both these networks evolve together during training. This approach is compared with standard fixed and trained combining schemes.

关键词： Intelligent networks Neural networks Testing Voting Clouds Image databases Glass Gaussian distribution Satellites Remote sensing

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An adaptive training algorithm for an ensemble of networks

An adaptive training algorithm for an ensemble of networks

引用

International Joint Conference on Neural Networks (IJCNN)

作者： N. Wanas L. Hodge M. Kamel Pattern Analysis and Machine Intelligence Laboratory Department of Systems Design Engineering University of Waterloo Waterloo ONT Canada

ISBN: (纸本)0780370449

An ensemble of neural networks offers several advantages over classical single classifier systems when applied to complex pattern classification problems. However, the performance of the ensemble as a unit depends not only on the effective aggregation of the modules decisions, but also on the accuracy of the individual classification decisions of each module. The accuracy at the modular level is a result of the quality of training received by each module. This paper presents an adaptive training algorithm that can be used to direct the training of the individual modules so as to improve the classification accuracy and training efficiency of the ensemble.

关键词： Voting Neural networks Training data pattern analysis machine intelligence System analysis and design Design engineering Systems engineering and theory pattern classification Crosstalk

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Neural networks: the state of the art

Neural networks: the state of the art

引用

International Conference on Microelectronics, ICM

作者： M.S. Kamel Pattern Analysis and Machine Intelligence Laboratory Department of Systems Design Engineering University of Waterloo Waterloo ONT Canada

Artificial neural networks (ANNs) have demonstrated their success in many applications due to their ability to solve some problems with relative ease of use and the model-free property they enjoy. ANNs can solve problems without the need to understand or learn the analytical and statistical properties of the problem nor the solution steps. Research in ANNs has resulted in a variety of models and learning algorithms. In this paper, a brief review of recent advances in the field is presented. The paper then focuses on the recent work conducted by the author's group on modular neural networks. In particular, the paper discusses the different modular structures, modes of interactions, capabilities, co-operation among modules and fusion of their decisions. Performance of these models has proven to be superior to nonmodular neural networks.

关键词： Neural networks Artificial neural networks Computer errors Multilayer perceptrons Supervised learning Feedback pattern analysis machine intelligence System analysis and design Design engineering

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Multiple classifier hierarchical architecture for handwritten Arabic character recognition

Multiple classifier hierarchical architecture for handwritte...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： N.M. Wanas M.R. El-Sakka M.S. Kamel Pattern Analysis and Machine Intelligence Laboratory Department of Systems Design Engineering University of Waterloo Waterloo ONT Canada

Combining decisions from several classifiers can be used to improve on the results of handwritten characters recognition. There are different methods to combine these decisions, most of which are static. We present an architecture that integrates learning into the voting scheme used to aggregate individual decisions. The focus of the work is to make the decision fusion a more adaptive process. This approach makes use of feature detectors responsible for gathering information about the input to perform adaptive decision aggregation. The approach is tested on handwritten Arabic character recognition. The results showed an improvement over any individual classifier, as well as different static classifier combining schemes.

关键词： Character recognition Voting Handwriting recognition Neural networks Bayesian methods pattern recognition Aggregates Optical character recognition software Decision making Multi-layer neural network

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