Soft Computing for Recognition based on Biometrics (Studies in Computational Intelligence, 312)

✍ Scribed by Patricia Melin (editor), Witold Pedrycz (editor)

• Publisher: Springer
• Year: 2010
• Tongue: English
• Leaves: 449
• Category: Library

✦ Synopsis

We describe in this book, bio-inspired models and applications of hybrid intel- gent systems using soft computing techniques for image analysis and pattern r- ognition based on biometrics and other information sources. Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful hybrid intelligent systems. The book is organized in five main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of classification methods and applications, which are basically papers that propose new models for classification to solve general pr- lems and applications. The second part contains papers with the main theme of modular neural networks in pattern recognition, which are basically papers using bio-inspired techniques, like modular neural networks, for achieving pattern r- ognition based on biometric measures. The third part contains papers with the theme of bio-inspired optimization methods and applications to diverse problems. The fourth part contains papers that deal with general theory and algorithms of bio-inspired methods, like neural networks and evolutionary algorithms. The fifth part contains papers on computer vision applications of soft computing methods. In the part of classification methods and applications there are 5 papers that - scribe different contributions on fuzzy logic and bio-inspired models with appli- tion in classification for medical images and other data.

✦ Table of Contents

Title
Preface
Contents
Part I Classification Algorithms and Applications
Soft Computing Approaches to the Problem of Infant Cry Classification with Diagnostic Purposes
Introduction
The Infant Cry Automatic Recognition Process
Logical-Combinatorial Approach
The Connectionist Approach
Genetic-Neural Approach
Development of a Hybrid Classifier
Classification Results Analysis
Statistic Measures for Reducing Input Vectors
The Fuzzy Approach
Results
Compressing the Cry Features
Implementation and Experiments
Conclusions
Recommendations
References
Neural Networks and SVM-Based Classification of Leukocytes Using the Morphological Pattern Spectrum
Introduction
Image Morphology
Image Morphology Definitions
Morphological Pattern Spectrum
Composed Feature Vector
Multilayer Perceptron
Support Vector Machines (SVM)
Experimental Description and Results
Conclusions
References
Hybrid System for Cardiac Arrhythmia Classification with Fuzzy K-Nearest Neighbors and Neural Networks Combined by a Fuzzy Inference System
Introduction
Problem Statement and Outline of our Proposal
Background Theory
Fuzzy K-Nearest Neighbors Algorithm
Multi Layer Perceptron
Backpropagation Training for a Multilayer Perceptron
Gradient Descent with Momentum (GDM)
Gradient Scaled Conjugate (SC)
Experiments
MIT-BIH Arrhythmia Database
Experimental Setup
Classifiers
Statistical Analysis
Conclusions
References
A Comparative Study of Blog Comments Spam Filtering with Machine Learning Techniques
Introduction
Background and Basic Concepts
Naïve Bayes
K Nearest Neighbors
Neural Networks
Support Vector Machines
Architecture
Corpus
Preprocessing PP
A1 Naïve Bayes
A2 Support Vector Machines
A3 Neural Networks
A4 K-nearest Neighbors
Models
Results
Results of A1 Naïve
Results of A2 Support Vector Machines
Results of A3 Neural Networks
Results of A4 K-Nearest Neighbors
Global Results
Statistical Analysis Mean Match Pairs
Conclusions
References
Distributed Implementation of an Intelligent Data Classifier
Introduction
Related Work
Architecture for Building a Distributed Data Classifier
Building the Global Classifier
ID3 Global Data Classifier
Implementation
Experimental Evaluations
Conclusions
References
Part II Pattern Recognition
Modular Neural Network with Fuzzy Integration and Its Optimization Using Genetic Algorithms for Human Recognition Based on Iris, Ear and Voice Biometrics
Introduction
Background
Iris
Ear
Voice
Basic Concepts
Modular Neural Network
Fuzzy Logic
Genetic Algorithms
Proposed Method and Results
Methodology
Databases and Pre-processing
Architecture and Results of the Modular Neural Network
Genetic Algorithm for MNN Optimization and Results
Fuzzy Integration
Genetic Algorithm for Fuzzy Integrator and Results
Conclusions
References
Comparative Study of Type-2 Fuzzy Inference System Optimization Based on the Uncertainty of Membership Functions
Introduction
Preliminaries
Modular Neural Networks
Type-2 Fuzzy Logic
Genetic Algorithms
Optimization Method Description
Fuzzy Systems Optimization Based on the Level of Uncertainty
Simulation Results
Adaptive Noise Cancellation
MPG Benchmark Problem
Conclusions
References
Modular Neural Network for Human Recognition from Ear Images Using Wavelets
Introduction
Background
Related Work
Ear
Ear Recognition Process
Data Acquisition
Image Pre-processing
Neural Network Structure
Neural Network Training
Modular Integration
Conclusions
References
Modular Neural Networks for Person Recognition Using the Contour Segmentation of the Human Iris Biometric Measurement
Introduction
Backgroun and Basic Concepts
Modular Neural Network
Historical Development
Iris Properties
Proposed Method and Problem Description
Problem Description
Image Pre-processing
Modular Neural Network Architecture
Simulation Results
Results with the Initial Modular Neural Network Architecture
First Modification of the Modular Neural Network Architecture
Second Modification of the Modular Neural Network Architecture (Extended)
Conclusions
References
Real Time Face Identification Using a Neural Network Approach
Introduction
Background and Basic Concepts
Historical Development
Proposed Method and Problem Description
Image Acquisition (Database)
Preprocessing
Feature Extraction
Edge Extraction
Eigenface
Discrete Wavelet Transform (dwt)
Modular Neural Network Architecture
Simulation Results
Results Using Only the Pre-processed Database
Results Using the Pre-processed Database with Edges
Conclusions
References
Comparative Study of Feature Extraction Methods of Fuzzy Logic Type 1 and Type-2 for Pattern Recognition System Based on the Mean Pixels
Introduction
Fuzzy Image Enhancement Based Pixels Brightness
Architecture Pattern Recognition with Fuzzy Extraction Features
Simulation Results Type-1 and Type-2 with Blur Motion
Comparison of the Results Type-1 and Type-2 Fuzzy Logic Extraction Features
Conclusions
References
Part III Optimization Methods
Application of the Bee Swarm Optimization BSO to the Knapsack Problem
Introduction
Knapsack Problem
Types of Knapsack Problem Instances
Bee Algorithm (BA)
Particle Swarm Optimization (PSO)
Bee Swarm Optimization (BSO)
Experiments
Results
Uncorrelated Knapsack Problem
Weakly Correlated
Strongly Correlated
Inverse Strongly Correlated
Almost Strongly Correlated
Subset-sum
Uncorrelated Similar Weight
Conclusions
References
An Approach Based on Neural Networks for Gas Lift Optimization
Introduction
Strategy of Optimization Based on a Neural Network
Results and Discussions
First Case: Produced Oil Rate by a Single Well
Second Case: Produced Oil Rate by a Production System Based on Two Wells
Comparative Analysis
Conclusions
References
A New Evolutionary Method with Particle Swarm Optimization and Genetic Algorithms Using Fuzzy Systems to Dynamically Parameter Adaptation
Introduction
Genetic Algorithm for Optimization
Particle Swarm Optimization
Full Model of FPSO+FGA
FPSO (Fuzzy Particle Swarm Optimization)
FGA (Fuzzy Genetic Algorithm)
Definition of the Fuzzy Systems Used in FPSO+FGA
Benchmark Mathematical Functions
Simulations Results
Simulation Results with the Genetic Algorithm (GA)
Simulation Results with Particle Swarm Optimization
Simulation Results with FPSO+FGA
Comparison Results between GA, PSO and FPSO+FGA
Conclusions
References
Local Survival Rule for Steer an Adaptive Ant-Colony Algorithm in Complex Systems
Introduction
Background
Graph Theory
Structural Characterization
SQRP Search Strategies
SQRP Description
SQRP Algorithms
AdaNAS Model
The General Model
Behavior Rule
AdaNAS Algorithm
Experiments
Generation of the Test Data
Parameters
Results
Conclusions
References
Using Consecutive Swaps to Explore the Insertion Neighborhood in Tabu Search Solution of the Linear Ordering Problem
Introduction
Related Work
Proposed Method
Main Idea
Improving the Neighborhood Exploration
Example
Experimental Results
Conclusions and Future Work
References
A New Optimization Method Based on a Paradigm Inspired by Nature
Introduction
Chemical Paradigm
Modeling the Chemical Paradigm
Preliminary Experimental Results
Conclusions
References
Part IV Theory and Algorithms
Improvement of the Backpropagation Algorithm Using (1+1) Evolutionary Strategies
Introduction
Background and Basic Concepts
Artificial Neural Networks
The Backpropagation Algorithm (BP)
Main Shortcomings of Backpropagation Algorithm
Improvements to the Backpropagation Algorithm
Problem Description and Proposed Method
Evolutionary Computation and ANNs
Evolutionary Strategies
Evolutionary Strategies for Backpropagation Learning
Experimental Results
Summary and Conclusions
References
Parallel Genetic Algorithms for Architecture Optimization of Neural Networks for Pattern Recognition
Introduction
Theoretical Concepts
Neural Networks
Genetic Algorithms
Parallel Genetic Algorithms
Problem Statement
Neural Network Structure
Parallel Genetic Algorithm for Optimization
Experimental Results
Conclusions
References
Scene Recognition Based on Fusion of Color and Corner Features
Introduction
Corner Detection Method
Edge Detection
Corner Detection Windows
Corner Detection
Scene Segmentation
RGB to HSV Color Space
HSV Component Analysis
Door Segmentation
Recognition of Scenarios
Results
Conclusions
References
Improved Tabu Solution for the Robust Capacitated International Sourcing Problem (RoCIS)
Introduction
Related Work
Problem Formulation
Improved Tabu Solution
Improving the Initial Solution Construction
Improving the Neighborhood Construction
Experimental Results
Conclusions and Future Work
References
Variable Length Number Chains Generation without Repetitions
Introduction
Linear Congruential Method
Calculation of n$_0$, a, c and m Parameters
Variable Length Integer Number Chain Generation without Repetitions
Variable Length Number Chains without Repetitions in the Interval (0,1), (0,1] and [0,1]
References
Comparative Analysis of Hybrid Techniques for an Ant Colony System Algorithm Applied to Solve a Real-World Transportation Problem
Introduction
Vehicle Routing Problem (VRP) Related Works
VRP Variants
The Ant Colony System Algorithm (ACS)
State of Art
RoSLoP: A Real-World Industrial Application
Routing-Scheduling Constrains
Loading Constrains
A Reduction Technique for the Loading Elements
The Exact Approach of RoSLoP
The Heuristic Solution of RoSLoP
Experimentation and Results
The Dataset of Solomon
Experimentation with RoSLoP Instances
Conclusions and Future Contributions
References
Part V Computer Vision Applications
Comparison of Fuzzy Edge Detectors Based on the Image Recognition Rate as Performance Index Calculated with Neural Networks
Introduction
Overview of the Tested Edge Detectors
Sobel Edge Detector Improved with Fuzzy Systems
Morphological Gradient Detector Improved with Fuzzy Systems
Design of the Experiment
General Algorithm Used for the Experiment
Parameters Depend on the Database of Images
The Monolithic Neural Network
Results
Conclusion
References
Intelligent Method for Contrast Enhancement in Digital Video
Introduction
General Concepts for an Image Quality in TFT-LCD Consumer Applications
Analysis of Brightness and Contrasts in an Image
Linear Point Operations in Images
Transformation Function
Design of the Intelligent Method for Contrast Enhancement in Digital Video
Contrast Ratio and Light Leakage in TFT-LCD Devices
Light Perception and Simultaneous Contrasts
Brightness Perception
To Preserve Level of Original Brightness
Description of the ProposedMethod
Proposed Neural Network and Its Training
Description of the Evaluation Method and Analysis of Results
Evaluation Method Using Test Pattern Image
EvaluationMethod Using Real Images
Conclusions
References
Method for Obstacle Detection and Map Reconfiguration in Wheeled Mobile Robotics
Introduction
General System Description
Stereoscopic Vision and Obstacles Detection
Surface Ground Extraction and Obstacle Detection Using Luminance and Hue
Stereoscopic Vision System Module and FPGA Implementation
Design of the Stereoscopic Vision Module
Depth Measure from Stereo Image
Map Building and Map Reconfiguration
Conclusion
References
Automatic Dust Storm Detection Based on Supervised Classification of Multispectral Data
Introduction
An Overview of MODIS Data
Selection and Analysis of Spectral Bands for Feature Extraction
Dust Storm Detection Using the Maximum Likelihood Classifier
Neuro-Probabilistic Modeling: The Probabilistic Neural Network
The PNN Large Sample Size Problem
Results and Discussion
Conclusion
References
Author Index