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E-grāmata: Computer-Aided Intelligent Recognition Techniques and Applications [Wiley Online]

Edited by (King Fahd University of Petroleum and Minerals, Saudi Arabia)
  • Formāts: 520 pages
  • Izdošanas datums: 20-May-2005
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 470094168
  • ISBN-13: 9780470094167
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  • Wiley Online
  • Cena: 210,78 €*
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Computer-Aided Intelligent Recognition Techniques and ApplicationsPalielināt
  • Formāts: 520 pages
  • Izdošanas datums: 20-May-2005
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 470094168
  • ISBN-13: 9780470094167
Citas grāmatas par šo tēmu:
Wiley Online E-booksMore info about Wiley Online
Rokasgrāmatas mājas lapa: https://onlinelibrary.wiley.com/doi/book/10.1002/0470094168
As you probably well know, intelligent recognition systems go far beyond your ID badge at the cube farm. Character recognition, natural language processing, computer vision, robotics, medical imaging, visualization and the media are all applications in which these systems are common or essential. In these 24 articles describing cutting-edge research, contributors describe their work in offline Arabic-character recognition, a license plate recognition system, algorithms for extracting textual characters in color video, recognizing handwriting, logo detection in complex backgrounds, signature and fingerprint verification, palm print recognition, iris recognition using neural networks, face recognition using subspace techniques and humanoid robots, age classification, medical pattern understanding and cognitive analysis, speech recognition, data analysis, topological recognition and cryptographic communication. Annotation ©2005 Book News, Inc., Portland, OR (booknews.com)

Intelligent recognition methods have recently proven to be indispensable in a variety of modern industries, including computer vision, robotics, medical imaging, visualization and the media. Furthermore, they play a critical role in the traditional fields such as character recognition, natural language processing and personal identification.

This cutting-edge book draws together the latest findings of industry experts and researchers from around the globe. It is a timely guide for all those require comprehensive, state-of-the-art advice on the present status and future potential of intelligent recognition technology.

Computer-Aided Intelligent Recognition Techniques and Applications:

  • Provides the user community with systems and tools for application in a very wide range of areas, including: IT, education, security, banking, police, postal services, manufacturing, mining, medicine, multimedia, entertainment, communications, data visualization, knowledge extraction, pattern classification and virtual reality.
  • Disseminates information in a plethora of disciplines, for example pattern recognition, AI, image processing, computer vision and graphics, neural networks, cryptography, fuzzy logic, databases, evolutionary algorithms, shape and numerical analysis.
  • Illustrates all theory with real-world examples and case studies.

This valuable resource is essential reading for computer scientists, engineers, and consultants requiring up-to-date comprehensive guidance on the latest developments in computer-aided intelligent recognition techniques and applications. Its detailed, practical approach will be of interest to senior undergraduate and graduate students as well as researchers and industry experts in the field of intelligent recognition.

Preface xvii
List of Contributors
xix
On Offline Arabic Character Recognition
1(18)
Muhammad Sarfraz
Abdulmalek Zidouri
Syed Nazim Nawaz
Introduction
1(3)
Structure of the Proposed OCR System
4(2)
Preprocessing
6(1)
Segmentation
7(3)
Line Segmentation and Zoning
8(1)
Word Segmentation
8(1)
Segmentation of Words into Individual Characters
9(1)
Feature Extraction
10(1)
Recognition Strategy
11(4)
Recognition Using the Syntactic Approach
12(1)
Recognition Using the Neural Network Approach
13(2)
Experimental Results and Analysis
15(2)
System Training
15(1)
Experimental Set-up
15(1)
Results Achieved
15(2)
Conclusion
17(2)
Acknowledgement
17(1)
References
17(2)
License Plate Recognition System: Saudi Arabian Case
19(14)
Muhammad Sarfraz
Mohammed Jameel Ahmed
Introduction
19(1)
Structure of a Typical LPR System
20(1)
Image Acquisition
21(1)
License Plate Extraction
21(5)
Vertical Edge Detection
23(1)
Filtering
23(1)
Vertical Edge Matching
24(2)
Black to White Ratio and Plate Extraction
26(1)
License Plate Segmentation
26(1)
Character Recognition
26(1)
Normalization
26(1)
Template Matching
27(1)
Experimental Analysis and Results
27(5)
Conclusion
32(1)
References
32(1)
Algorithms for Extracting Textual Characters in Color Video
33(18)
Edward K. Wong
Minya Chen
Introduction
33(1)
Prior and Related Work
34(1)
Our New Text Extraction Algorithm
35(5)
Step 1: Identify Potential Text Line Segments
36(2)
Step 2: Text Block Detection
38(1)
Step 3: Text Block Filtering
38(1)
Step 4: Boundary Adjustments
38(1)
Step 5: Bicolor Clustering
38(1)
Step 6: Artifact Filtering
39(1)
Step 7: Contour Smoothing
39(1)
Experimental Results and Performance
40(7)
Using Multiframe Edge Information to Improve Precision
47(1)
Step 3(b): Text Block Filtering Based on Multiframe Edge Strength
47(1)
Discussion and Concluding Remarks
47(4)
References
48(3)
Separation of Handwritten Touching Digits: A Multiagents Approach
51(16)
Ashraf Elnagar
Reda Al-Hajj
Introduction
51(1)
Previous Work
52(4)
Digitizing and Processing
56(1)
Segmentation Algorithm
56(5)
Extraction of Feature Points
56(1)
The Employed Agents
57(4)
Experimental Results
61(4)
Conclusions and Future Work
65(2)
References
65(2)
Prototype-based Handwriting Recognition Using Shape and Execution Prototypes
67(22)
Miguel L. Bote-Lorenzo
Eduardo Gomez-Sanchez
Yannis A. Dimitriadis
Introduction
67(1)
A Handwriting Generation Process Model
68(2)
The First Stages of the Handwriting Recognition System
70(3)
Character Segmentation
70(1)
Feature Extraction
71(2)
The Execution of the Prototype Extraction Method
73(9)
Grouping Training Samples
74(1)
Refinement of the Prototypes
75(1)
Experimental Evaluation of the Prototype Extraction Method
76(6)
Prototype-based Classification
82(5)
The Prototype-based Classifier Architecture
82(1)
Experimental Evaluation of the Prototype Initialization
83(1)
Prototype Pruning to Increase Knowledge Condensation
84(1)
Discussion and Comparison to Related Work
85(2)
Conclusions
87(2)
Acknowledgement
87(1)
References
87(2)
Logo Detection in Document Images with Complex Backgrounds
89(10)
Tuan D. Pham
Jinsong Yang
Introduction
89(1)
Detection of Potential Logos
90(1)
Verification of Potential Logos
91(2)
Feature Extraction by Geostatistics
91(2)
Neural Network-based Classifier
93(1)
Experimental Results
93(4)
Conclusions
97(2)
References
97(2)
An Intelligent Online Signature Verification System
99(20)
Bin Li
David Zhang
Introduction
99(3)
Process and System
100(1)
The Evaluation of an Online Signature Verification System
101(1)
Literature Overview
102(5)
Conventional Mathematical Approaches
102(2)
Dynamic Programming Approach
104(1)
Hidden Markov Model-Based Methods
105(1)
The Artificial Neural Networks Approach
106(1)
Signature Verification Product Market Survey
106(1)
A Typical Online Signature Verification System
107(6)
Data Acquisition
107(3)
Feature Extraction
110(1)
Feature Matching
111(1)
Verification
112(1)
Proposed Online Signature Verification Applications
113(3)
System Password Authentication
113(1)
Internet E-commerce Application
114(2)
Conclusions
116(3)
References
116(3)
Hybrid Fingerprint Recognition using Minutiae and Shape
119(12)
Asker Bazen
Raymond Veldhuis
Sabih Gerez
Introduction
119(1)
Elastic Deformations
120(2)
Elastic Minutiae Matching
122(4)
Local Minutiae Matching
122(1)
Global Minutiae Matching
123(3)
Shape Matching
126(1)
Results
126(3)
Conclusions
129(2)
Acknowledgement
129(1)
References
129(2)
Personal Authentication Using the Fusion of Multiple Palm-print Features
131(14)
Chin-Chuan Han
Introduction
131(2)
Preprocessing
133(2)
Step 1: Image Thresholding
134(1)
Step 2: Border Tracing
134(1)
Step 3: Wavelet-based Segmentation
135(1)
Step 4: Region of Interest (ROI) Generation
135(1)
Feature Extraction
135(1)
Enrollment and Verification Processes
136(4)
Multitemplate Matching Approach
136(1)
Multimodal Authentication with PBF-based Fusion
137(2)
Adaptive Thresholding
139(1)
Experimental Results
140(2)
Experimental Environment
140(1)
Verification Using a Template Matching Algorithm
140(1)
Verification Using PBF-based Fusion
141(1)
Conclusions
142(3)
References
142(3)
Intelligent Iris Recognition Using Neural Networks
145(24)
Muhammad Sarfraz
Mohamed Deriche
Muhammad Moinuddin
Syed Saad Azhar Ali
Introduction
145(2)
Literature Review
147(1)
Some Groundbreaking Techniques
148(6)
Daugman's Method
149(1)
Boles's Method
150(1)
Method of Dyadic Wavelet Transform Zero Crossing
151(3)
Neural Networks
154(4)
Multilayer Feed-forward Neural Networks (MFNNs)
154(2)
Radial Basis Function Neural Networks (RBFNNs)
156(2)
Proposed Method
158(4)
Localizing the Iris
158(1)
Finding the Contour
159(1)
Feature Extraction
159(3)
Iris Pattern Recognition
162(1)
Experimental Results
162(2)
Results for an MFNN
162(1)
Results for an RBFNN
162(2)
Graphic User Interface (GUI)
164(2)
Concluding Remarks
166(3)
References
166(3)
Pose-invariant Face Recognition Using Subspace Techniques
169(32)
Mohamed Deriche
Mohammed Aleemuddin
Introduction
169(3)
Background
170(1)
The Problem of Pose
171(1)
Review of Biometric Systems
172(6)
Summary of the Performance of Different Biometrics
173(4)
Selecting the Right Biometric Technology
177(1)
Multimodal Biometric Systems
177(1)
Face Recognition Algorithms
178(5)
Template-based Face Recognition
180(1)
Appearance-based Face Recognition
180(1)
Model-based Face Recognition
181(2)
Linear Subspace Techniques
183(8)
Principal Component Analysis
184(1)
Linear Discriminant Analysis
185(4)
Independent Component Analysis
189(2)
A Pose-invariant System for Face Recognition
191(7)
The Proposed Algorithm
192(1)
Pose Estimation using LDA
192(2)
Experimental Results for Pose Estimation using LDA and PCA
194(1)
View-specific Subspace Decomposition
194(1)
Experiments on the Pose-invariant Face Recognition System
195(3)
Concluding Remarks
198(3)
References
198(3)
Developmental Vision: Adaptive Recognition of Human Faces by Humanoid Robots
201(40)
Hon-fai Chia
Ming Xie
Introduction
201(1)
Adaptive Recognition Based on Developmental Learning
202(3)
Human Psycho-physical Development
202(1)
Machine (Robot) Psycho-physical Development
203(1)
Developmental Learning
204(1)
Questions to Ponder
204(1)
Developmental Learning of Facial Image Detection
205(17)
Current Face Detection Techniques
205(1)
Criteria of Developmental Learning for Facial Image Detection
206(1)
Neural Networks
206(1)
Color Space Transformation
207(3)
RCE Adaptive Segmentation
210(8)
Implementation
218(1)
Questions to Ponder
218(2)
Experimental Results
220(2)
Developmental Learning of Facial Image Recognition
222(14)
Wavelets
222(1)
Wavelet Packet Analysis
223(1)
Feature Extraction by Wavelet Packet Analysis
224(3)
Hidden Markov Models
227(7)
Feature Classification by Hidden Markov Models
234(1)
Questions to Ponder
234(1)
Experimental Results
235(1)
Discussion
236(5)
References
237(4)
Empirical Study on Appearance-based Binary Age Classification
241(16)
Mohammed Yeasin
Rahul Khare
Rajeev Sharma
Introduction
242(1)
Related Works
243(1)
Description of the Proposed Age Classification System
243(4)
Database
244(1)
Segmentation of the Facial Region
245(1)
Preprocessing
246(1)
Feature Extraction
246(1)
Classifying People into Age Groups
246(1)
Empirical Analysis
247(5)
Performance of Data Projection Techniques
247(1)
The Effect of Preprocessing and Image Resolution
248(1)
The Effect of Pose Variation
248(1)
The Effect of Lighting Conditions
249(1)
The Effect of Occlusion
249(1)
The Impact of Gender on Age Classification
250(1)
Classifier Accuracies Across the Age Groups
251(1)
Conclusions
252(5)
Appendix A: Data Projection Techniques
253(1)
Principal Component Analysis (PCA)
253(1)
Non-Negative Matrix Factorization (NMF)
253(1)
Appendix B: Fundamentals of Support Vector Machines
253(1)
Acknowledgement
254(1)
References
254(3)
Intelligent Recignition in Medical Pattern Understanding and Cognitive Analysis
257(18)
Marek R. Ogiela
Ryszard Tadeusiewicz
Introduction
257(2)
Preliminary Transformation of Medical Images
259(2)
Structural Descriptions of the Examined Structures
261(2)
Coronary Vessel Cognitive Analysis
263(2)
Understanding of Lesions in the Urinary Tract
265(3)
Syntactic Methods Supporting Diagnosis of Pancreatitis and Pancreatic Neoplasm
268(3)
Semantic Analysis of Spinal Cord NMR Images
271(1)
Conclusions
272(3)
References
273(2)
The Roadmap for Recognizing Regions of Interest in Medical Images
275(22)
Sabah M.A. Mohammed
Jinan A.W. Fiaidhi
Lei Yang
Introduction
275(1)
Convolutional Primitive Segmentation
276(4)
Thresholding Primitive Segmentation
280(1)
Morphological Primitive Segmentation
280(1)
Erosion
280(1)
Dilation
281(1)
Opening and Closing
281(1)
Hybridizing the Primitive Segmentation Operators
281(4)
Region Identification Based on Fuzzy Logic
285(8)
Experimental Results
289(4)
Conclusions
293(4)
References
294(3)
Feature Extraction and Compression with Discriminative and Nonlinear Classifiers and Applications in Speech Recognition
297(22)
Xuechuan Wang
Introduction
298(2)
Standard Feature Extraction Methods
300(1)
Linear Discriminant Analysis
300(1)
Principal Component Analysis
301(1)
The Minimum Classification Error Training Algorithm
301(3)
Derivation of the MCE Criterion
301(2)
Using MCE Training Algorithms for Dimensionality Reduction
303(1)
Support Vector Machines
304(3)
Constructing an SVM
304(2)
Multiclass SVM Classifiers
306(1)
Feature Extraction and Compression with MCE and SVM
307(1)
The Generalized MCE Training Algorithm
307(1)
Reduced-dimensional SVM
307(1)
Classification Experiments
308(8)
Deterding Database Experiments
309(2)
TIMIT Database Experiments
311(5)
Conclusions
316(3)
References
317(2)
Improving Mine Recognition through Processing and Dempster--Shafer Fusion of Multisensor Data
319(26)
Nada Milisavljevic
Isabelle Bloch
Introduction
319(1)
Data Presentation and Preprocessing
320(4)
IR Data
320(1)
GPR Data
321(2)
MD Data
323(1)
Region Selection
324(3)
IR Regions
324(1)
GPR Regions
325(1)
MD Regions
325(2)
Choice of Measures and Their Extraction
327(6)
IR Measures
327(1)
GPR Measures
328(5)
MD Measures
333(1)
Modeling of Measures in Terms of Belief Functions and Their Discounting
333(5)
IR Measures
334(1)
GPR A-scan and Preprocessed C-scan Measures
335(1)
GPR B-scan (Hyperbola) Measures
336(1)
MD Measures
336(1)
Discounting Factors
337(1)
Region Association, Combination of Measures and Decision
338(3)
Region Association
338(1)
Combination of Masses
339(1)
Decision
340(1)
Results
341(1)
Conclusion
341(4)
Acknowledgement
342(1)
References
342(3)
Fast Object Recognition Using Dynamic Programming from a Combination of Salient Line Groups
345(18)
Dong Joong Kang
Jong Eun Ha
In So Kweon
Introduction
345(1)
Previous Research
346(1)
Junction Extraction
347(1)
Energy Model for the Junction Groups
348(1)
Energy Minimization
349(2)
Collinear Criterion of Lines
351(2)
Parallelism
351(1)
Normal Distance
352(1)
Energy Model for the Junction Groups
353(1)
Experiments
354(6)
Line Group Extraction
355(4)
Collinearity Tests for Random Lines
359(1)
Conclusions
360(3)
References
360(3)
Holo-Extraction and Intelligent Recognition of Digital Curves Scanned from Paper Drawings
363(26)
Ke-Zhang Chen
Xi-Wen Zhang
Zong-Ying Ou
Xin-An Feng
Introduction
363(1)
Review of Current Vectorization Methods
364(3)
The Hough Transform-based Method
365(1)
Thinning-based Methods
365(1)
The Contour-based Method
365(1)
The Sparse Pixel-based Method
365(1)
Mesh Pattern-based Methods
365(1)
Black Pixel Region-based Methods
366(1)
The Requirements for Holo-extraction of Information
366(1)
Construction of the Networks of SCRs
367(6)
Generating Adjacency Graphs of Runs
367(1)
Constructing Single Closed Regions (SCRs)
368(2)
Building Adjacency Graphs of SCRs
370(1)
Constructing the Networks of SCRs
371(2)
A Bridge from the Raster Image to Understanding and 3D Reconstruction
373(6)
Separating the Annotations and the Outlines of Projections of Parts
373(2)
Vectorization
375(2)
3D Reconstruction
377(2)
Classification of Digital Curves
379(3)
Extracting the Representative Points of Digital Curves
379(1)
Fitting a Straight line to the Set of Points
380(1)
Fitting a Circular Arc to the Set of Points
381(1)
Determining the Type
382(1)
Decomposition of Combined Lines Using Genetic Algorithms
382(4)
Initial Population
382(2)
Fitness Function
384(1)
Crossover
384(1)
Mutation
384(1)
Selection
385(1)
Convergence and Control Parameters
385(1)
Determination of the Relationships Between the Segments
385(1)
Software Prototype
386(1)
Conclusions
386(3)
References
387(2)
Topological Segmentation and Smoothing of Discrete Curve Skeletons
389(22)
Wenjie Xie
Renato Perucchio
David Sedmera
Robert P. Thompson
Introduction
389(1)
Basic Definitions
390(2)
Topological Segmentation
392(5)
Component Counting and Labeling
392(1)
Classification of Skeleton Voxels
392(1)
Local Junction Classification
393(1)
Thick Junction Resolution
394(1)
Branch Formation
395(2)
Branch Filtering
397(3)
Branch Classification
397(1)
Noise Segment Removal
398(2)
Branch Smoothing
400(3)
Polynomial Branch Representation
400(1)
Augmented Merit Functions
401(2)
Results
403(4)
Discussion
407(4)
Acknowledgement
408(1)
References
408(3)
Applications of Clifford-valued Neural Networks to Pattern Classification and Pose Estimation
411(28)
Eduardo Bayro-Corrochano
Nancy Arana-Daniel
Introduction
411(1)
Geometric Algebra: An Outline
412(4)
Basic Definitions
412(1)
The Geometric Algebra of nD Space
413(1)
The Geometric Algebra of 3D Space
414(1)
Rotors
414(1)
Conformal Geometric Algebra
415(1)
Real-valued Neural Networks
416(1)
Complex MLP and Quaternionic MLP
417(1)
Clifford-valued Feed-forward Neural Networks
418(3)
The Activation Function
418(1)
The Geometric Neuron
418(1)
Feed-forward Clifford-valued Neural Networks
419(2)
Learning Rule
421(1)
Multidimensional Back-propagation Training Rule
421(1)
Geometric Learning Using Genetic Algorithms
422(1)
Support Vector Machines in the Geometric Algebra Framework
422(4)
Support Vector Machines
422(1)
Support Multivector Machines
423(1)
Generating SMVMs with Different Kernels
424(1)
Design of Kernels Involving the Clifford Geometric Product for Nonlinear Support Multivector Machines
424(1)
Design of Kernels Involving the Conformal Neuron
425(1)
Clifford Moments for 2D Pattern Classification
426(2)
Experimental Analysis
428(8)
Test of the Clifford-valued MLP for the XOR Problem
428(1)
Classification of 2D Patterns in Real Images
429(2)
Estimation of 3D Pose
431(1)
Performance of SMVMs Using Kernels Involving the Clifford Product
432(2)
An SMVM Using Clustering Hyperspheres
434(2)
Conclusions
436(3)
References
436(3)
Intelligent Recognition: Components of the Short-time Fourier Transform vs. Conventional Approaches
439(14)
Leonid Gelman
Mike Sanderson
Chris Thompson
Paul Anuzis
Introduction
440(1)
Theoretical Analysis
440(7)
Application
447(1)
Conclusions
448(5)
Acknowledgement
450(1)
References
450(3)
Conceptual Data Classification: Application for Knowledge Extraction
453(16)
Ahmed Hasnah
Ali Jaoua
Jihad Jaam
Introduction
453(1)
Mathematical Foundations
454(5)
Definition of a Binary Context
454(1)
Definition of a Formal Concept
455(1)
Galois Connection
456(1)
Optimal Concept or Rectangle
457(2)
An Approximate Algorithm for Minimal Coverage of a Binary Context
459(3)
Conceptual Knowledge Extraction from Data
462(3)
Supervised Learning by Associating Rules to Optimal Concepts
462(1)
Automatic Entity Extraction from an Instance of a Relational Database
463(2)
Software Architecture Development
465(1)
Automatic User Classification in the Network
465(1)
Conclusion
465(4)
References
466(3)
Cryptographic Communications With Chaotic Semiconductor Lasers
469(16)
Andres Iglesias
Introduction
470(2)
Semiconductor Lasers with Optical Feedback
472(6)
Step 1: Choice of the Laser
472(1)
Step 2: Determination of the Laser Equations and Parameters
473(2)
Step 3: Choice of Some Accessible Parameter for Chaoticity
475(1)
Step 4: Synchronization of the Chaotic Transmitter and Receiver Systems
476(2)
Applications to Cryptographic Communications
478(3)
Chaotic Masking
478(1)
Chaotic Switching
479(2)
Conclusions
481(4)
References
482(3)
Index 485


Dr. Sarfraz received his B.S.c.in mathematics and statistics from Govt. College, Lahore, Pakistan in 1977, an M.S.c in mathematics and an M.S.c in Numerical Analysis from the University of Brunel. He then went on to complete his P.h.D in Computer Aided Geometric Design also at The University of Brunel. Dr. Sarfraz is currently Associate Professor at King Fahd University of Petroleum and Minerals, Saudi Arabia. He is a member of many professional bodies including the IEEE and the IEEE Computer Society. He has published three books and written numerous journal papers and reports.
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