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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31-40 订阅

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An information theoretic image-quality measure

An information theoretic image-quality measure

引用

Canadian Conference on Electrical and Computer engineering (CCECE)

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

Lossy image compression techniques aim at encoding images with a minimal representation. During this process, some visually useful information may be lost. Assessing the information loss in decompressed images is not an easy task. In this paper, a new quantitative image-quality measure is introduced. This new measure incorporates information theory into the most commonly used objective criterion (the mean square error). The new measure has been tested by experiments performed on a wide variety of images. The results show an increase in the correlation between subjective rating by human observers and the normalized mean square error after applying the new measure.

关键词： Image coding Testing Mean square error methods Humans Image quality Information theory Layout pattern analysis machine intelligence Laboratories

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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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Shape retrieval using concavity trees

Shape retrieval using concavity trees

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

Concavity trees are well-known abstract structures. This paper proposes a new shape-based image retrieval method based on concavity trees. The proposed method has two main components. The first is an efficient (in terms of space and time) contour-based concavity tree extraction algorithm. The second component is a recursive concavity-tree matching algorithm that returns a distance between two trees. We demonstrate that concavity trees are able to boost the retrieval performance of two feature sets by at least 15% when tested on a database of 625 silhouette images.

关键词： Shape Image retrieval Information retrieval Testing Image databases pattern recognition pattern analysis machine intelligence Laboratories System analysis and design

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EVOL: ensembles voting online

EVOL: ensembles voting online

引用

International Joint Conference on Neural Networks (IJCNN)

作者： G. Auda M. Kamel Pattern Analysis And Machine Intelligence Lab Systems Design Engineering Department University of Waterloo Canada

Cooperation by voting is one of the popular modular neural network decision-making strategies. Ensemble classifiers are multiple identical modules which use voting for post-learning classification. This paper suggests a new cooperation scheme for ensembles which utilizes voting in the learning process itself. According to the suggested scheme, different modules would, automatically, focus on different regions in the input space. Hence, temporal crosstalk decreases and decision boundaries are drawn accurately in complex overlapping regions of the input space.

关键词： Voting Neural networks Decision making Multi-layer neural network Filtering Crosstalk pattern analysis machine intelligence System analysis and design design engineering

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Simulated Annealing with Opposite Neighbors

Simulated Annealing with Opposite Neighbors

引用

IEEE Symposium on Foundations of Computational intelligence, FOCI

作者： Mario Ventresca Hamid R. Tizhoosh Pattern Analysis and Machine Intelligence (PAMI) laboratory in the Systems Design Engineering Department University of Waterloo Waterloo ONT Canada

This paper presents an improvement to the vanilla version of the simulated annealing algorithm by using opposite neighbors. This new technique, is based on the recently proposed idea of opposition based learning, as such our proposed algorithm is termed opposition-based simulated annealing (OSA). In this paper we provide a theoretical basis for the algorithm as well as its practical implementation. In order to examine the efficacy of the approach we compare the new algorithm to SA on six common real optimization problems. Our findings confirm the theoretical predictions as well as show a significant improvement in accuracy and convergence rate over traditional SA. We also provide experimental evidence for the use of opposite neighbors over purely random ones

关键词： Simulated annealing Convergence Computational intelligence Computational modeling pattern analysis Intelligent networks Competitive intelligence Laboratories System analysis and design design engineering

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Numerical condition of feedforward networks with opposite transfer functions

Numerical condition of feedforward networks with opposite tr...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Mario Ventresca Hamid Reza Tizhoosh Pattern Analysis and Machine Intelligence (PAMI) laboratory in the Systems Design Engineering Department University of Waterloo Waterloo ONT Canada

Numerical condition affects the learning speed and accuracy of most artificial neural network learning algorithms. In this paper, we examine the influence of opposite transfer functions on the conditioning of feedforward neural network architectures. The goal is not to discuss a new training algorithm nor error surface geometry, but rather to present characteristics of opposite transfer functions which can be useful for improving existing or to develop new algorithms. Our investigation examines two situations: (1) network initialization, and (2) early stages of the learning process. We provide theoretical motivation for the consideration of opposite transfer functions as a means to improve conditioning during these situations. These theoretical results are validated by experiments on a subset of common benchmark problems. Our results also reveal the potential for opposite transfer functions in other areas of, and related to neural networks.

关键词： Transfer functions Neurons Artificial neural networks Jacobian matrices Training Feedforward neural networks Erbium

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Improving the Convergence of Backpropagation by Opposite Transfer Functions

Improving the Convergence of Backpropagation by Opposite Tra...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： M. Ventresca H.R. Tizhoosh Pattern Analysis and Machine Intelligence (PAMI) laboratory in the Systems Design Engineering Department University of Waterloo Waterloo ONT Canada

The backpropagation algorithm is a very popular approach to learning in feed-forward multi-layer perceptron networks. However, in many scenarios the time required to adequately learn the task is considerable. Many existing approaches have improved the convergence rate by altering the learning algorithm. We present a simple alternative approach inspired by opposition-based learning that simultaneously considers each network transfer function and its opposite. The effect is an improvement in convergence rate and over traditional backpropagation learning with momentum. We use four common benchmark problems to illustrate the improvement in convergence time.

关键词： Convergence Transfer functions Neural networks Backpropagation algorithms Genetic algorithms pattern analysis machine intelligence Laboratories System analysis and design design engineering

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Opposite Transfer Functions and Backpropagation Through Time

Opposite Transfer Functions and Backpropagation Through Time

引用

IEEE Symposium on Foundations of Computational intelligence, FOCI

作者： Mario Ventresca Hamid R. Tizhoosh Pattern Analysis and Machine Intelligence (PAMI) laboratory Systems Design Engineering Department University of Waterloo Waterloo ONT Canada

Backpropagation through time is a very popular discrete-time recurrent neural network training algorithm. However, the computational time associated with the learning process to achieve high accuracy is high. While many approaches have been proposed that alter the learning algorithm, this paper presents a computationally inexpensive method based on the concept of opposite transfer functions to improve learning in the backpropagation through time algorithm. Specifically, we will show an improvement in the accuracy, stability as well as an acceleration in learning time. We will utilize three common benchmarks to provide experimental evidence of the improvements

关键词： Transfer functions Backpropagation algorithms Signal processing algorithms Recurrent neural networks Acceleration Convergence Computational intelligence Stability Nonlinear dynamical systems Computational complexity

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Representing uncertainty in robot task planning

Representing uncertainty in robot task planning

引用

IEEE International Conference on Robotics and Automation (ICRA)

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

A representation for uncertain states within the context of robot task planning is described. This includes the probabilistic description of objects, relationships between objects, and operators. It is one component of a task planner designed for use in uncertain task domains. The planner is based on a game theoretic-approach which views the environment as an opponent (often fallible). Multiple outcomes arising from operator application in an uncertain world are attributed to moves of the opponent. These are called reaction operators. Their representation and effect on the planning process are discussed.< >

关键词： Uncertainty Robot sensing systems Process planning pattern analysis machine intelligence System analysis and design design engineering systems engineering and theory Intelligent robots Game theory

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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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