Choras Image Processing & Communications Challenges 2

1. Auflage 2010
ISBN: 978-3-642-16295-4
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 84, 486 Seiten

Reihe: Advances in Intelligent and Soft Computing

Image Processing and Communications represents an exciting and dynamic part of the information area. This book consists of 52 scientific and technical papers from 14 Nations, after a careful selection performed by many international reviewers. The papers are conveniently grouped into 6 chapters: - Computer Vision and Image Processing - Biometric - Recognition and Classification - Biomedical Image Processing - Applications - Communications. Each chapter focuses on a specific topic, presents results, and points out challenges and future directions.

Autoren/Hrsg.

Choras, Ryszard S.

Inhaltsverzeichnis

1;Title Page;1
2;Foreword;5
3;Contents;6
4;Part I: Computer Vision and Image Processing;12
4.1;Earliest Computer Vision Systems in Poland;13
4.1.1;Introduction;13
4.1.2;The Problem of Computer Image Input;14
4.1.3;Previous Research Related to Computer Vision;17
4.1.4;Multiprocessor Systems. CESARO2;19
4.1.5;And Then There Was an Avalanche ...;20
4.1.6;References;21
4.2;VICAL: Visual Cognitive Architecture for Concepts Learning to Understanding Semantic Image Content;24
4.2.1;Introduction;24
4.2.2;Visual Cognitive Framework VICAL;25
4.2.2.1;Eye Processor;25
4.2.2.2;Cognitive Processor;26
4.2.3;Structural Abstraction of VICAL;29
4.2.3.1;Operational Agent;30
4.2.3.2;Evolutionary Associator Agent;33
4.2.4;Experimental Results;36
4.2.5;Conclusion;37
4.2.6;References;38
4.3;Implementation of Computer Vision Algorithms in DirectShow Technology;40
4.3.1;Introduction;40
4.3.2;Filter Programming;42
4.3.3;Filter Pattern Design;44
4.3.4;DS Based Application for Scene Depth Estimation;45
4.3.5;Conclusions;47
4.3.6;References;47
4.4;Implementation of Hurwitz-Radon Matrices in Shape Representation;48
4.4.1;Introduction;48
4.4.2;Contour Points Based Shape Representation;49
4.4.3;Shape Reconstruction via MHR Method;50
4.4.3.1;The Operator of Hurwitz-Radon;51
4.4.3.2;MHR Method (Basic Version);53
4.4.3.3;MHR Method with Parameter k;54
4.4.3.4;MHR Method for Equidistance Nodes;55
4.4.4;Conclusion;57
4.4.5;References;58
4.5;Video Quality Assessment Using the Combined Full-Reference Approach;60
4.5.1;Introduction;60
4.5.2;Methods of Image Quality Assessment;61
4.5.3;Combined Metric and Its Verification;63
4.5.4;Conclusions;65
4.5.5;References;66
4.6;An Improved Self-embedding Algorithm: Digital Content Protection against Compression Attacks in Digital Watermarking;68
4.6.1;Introduction;68
4.6.2;OurMethod;70
4.6.3;Image Encryption Algorithm;71
4.6.4;Results and Discussion;72
4.6.4.1;JPEG Compression Attack;73
4.6.4.2;BTC Compression Attack;73
4.6.4.3;SVD Compression Attack;74
4.6.5;Conclusion;75
4.6.6;References;75
4.7;Generation of View Representation from View Points on Spiral Trajectory;76
4.7.1;Introduction;76
4.7.1.1;View Generation Space - Basic Concepts;77
4.7.2;Uniformly Distributed View Points on View Sphere;78
4.7.2.1;View Points on Spiral Path;80
4.7.2.2;Uniformly Distributed View Points on Spiral Path;80
4.7.3;Results;82
4.7.4;References;83
4.8;Gradient Based Edge Detection in Various Color Spaces;84
4.8.1;Introduction;84
4.8.2;RGB Space;84
4.8.3;HSB Space;85
4.8.4;YUV Space;86
4.8.5;CIEXYZ Space;87
4.8.6;CIELab Space;88
4.8.7;Conclusions;89
4.8.8;References;89
4.9;Improve Vector Quantization Strategy;90
4.9.1;Introduction;90
4.9.2;Adaptive VQ-Design;91
4.9.2.1;Rotation Block;91
4.9.2.2;Mean and Mode;91
4.9.2.3;Mean Removed VQ (MRVQ);92
4.9.2.4;Block Classification;93
4.9.2.5;Random Coding;93
4.9.2.6;Pruning Found;94
4.9.2.7;Result and Conclusion;96
4.9.3;Conclusion;96
4.9.4;References;97
5;Part II: Biometric;98
5.1;Knuckle Biometrics for Human Identification;99
5.1.1;Introduction;99
5.1.2;Knuckle Biometrics System Architecture Overview;100
5.1.3;Knuckle Preprocessing Phase;101
5.1.4;Feature Extraction;101
5.1.4.1;Short Feature Vector (Basic Features);101
5.1.4.2;Knuckle Lines Model;102
5.1.4.3;Knuckle Texture Descriptors;103
5.1.5;Classification;104
5.1.6;Results;104
5.1.7;Conclusions;105
5.1.8;References;105
5.2;A New Method of Fingerprint Key Protection of Grid Credential;107
5.2.1;Introduction;107
5.2.2;Method of Fingerprint Protection of Private Keys;107
5.2.3;Method Verification;110
5.2.4;References;111
5.3;Human Vein Pattern Segmentation from Low Quality Images – A Comparison of Methods;112
5.3.1;Introduction;112
5.3.2;Dataset Collection;113
5.3.3;The Method Based on Discrete Fourier Transform;113
5.3.4;The Gradient-Based Segmentation Method;116
5.3.5;Results of the Experiment;117
5.3.6;Conclusions;119
5.3.7;References;119
5.4;A Modified Algorithm for User Identification by His Typing on the Keyboard;120
5.4.1;Introduction;120
5.4.2;Other Works on Keystroke Dynamics;121
5.4.3;Authors’ Suggessed Approach;122
5.4.4;Experimental Results;124
5.4.5;Conclusions and Future Work;126
5.4.6;References;127
5.5;Multimodal Biometric Personal Authentication Integrating Iris and Retina Images;128
5.5.1;Introduction;128
5.5.2;Iris and Retina Recognition;130
5.5.2.1;Iris Recognition;132
5.5.2.2;Retina Recognition;135
5.5.3;Conclusion;137
5.5.4;References;137
6;Part III: Recognition and Classification;139
6.1;Fusion Methods for the Two Class Recognition Problem – Analytical and Experimental Results;140
6.1.1;Introduction;140
6.1.2;Fusion Based on Values of Classifiers Discriminant Function;141
6.1.3;Analytical Characteristics of Fusion Methods;142
6.1.4;Experiments;144
6.1.4.1;Set Up of Experiment;144
6.1.5;Results;145
6.1.5.1;Experimental Results Evaluation;145
6.1.6;FinalRemarks;146
6.1.7;References;146
6.2;Feature Type and Size Selection for AdaBoost Face Detection Algorithm;148
6.2.1;Introduction;148
6.2.2;The AdaBoost Algorithm;148
6.2.3;AdaBoost Algorithm for Face Detection;150
6.2.4;Rotated Haar-Like Features;151
6.2.5;Experiments;152
6.2.6;Conclusions and Future Research;153
6.2.7;References;154
6.3;3D Morphable Models Application for Expanding Face Database Limited to Single Frontal Face Image Per Person;155
6.3.1;Introduction;155
6.3.2;Model Construction;156
6.3.2.1;3D Face Scanner;156
6.3.2.2;Generic Face Model Construction;156
6.3.3;Generation of Novel Virtual Face Samples;157
6.3.3.1;Fitting Morphable Model;157
6.3.3.2;Performance;159
6.3.4;Conclusions;160
6.3.5;References;160
6.4;A Partition of Feature Space Based on Information Energy in Classification with Fuzzy Observations;162
6.4.1;Introduction;162
6.4.2;Bayes Classifier;163
6.4.2.1;Bayes Error;164
6.4.3;Basic Notions of Fuzzy Theory;165
6.4.4;Probability Error in Bayes Classifier with Fuzzy Observations;166
6.4.4.1;Numerical Example;167
6.4.5;Conclusion;168
6.4.6;References;169
6.5;Recognition of Signed Expressions Using Cluster-Based Segmentation of Time Series;170
6.5.1;Introduction;170
6.5.2;A Data-Driven Subsequence Extraction Method;171
6.5.2.1;The Input Data;171
6.5.2.2;Sequence Partitioning Problem;172
6.5.2.3;Optimization Method;173
6.5.3;Subunit–Based Recognition;174
6.5.4;Experiments;175
6.5.5;Conclusions;176
6.5.6;References;177
6.6;Extending 3D Shape Measurement with Reflectance Estimation;178
6.6.1;Introduction;178
6.6.2;Integrated Measurement Method;179
6.6.2.1;Shape Measurement;180
6.6.2.2;Color Measurement;180
6.6.2.3;Angular Reflection Measurement;181
6.6.2.4;Merging Multiple Measurements;182
6.6.3;Experimental Setup;183
6.6.4;Measurement Results;183
6.6.5;Conclusions;185
6.6.6;References;185
6.7;Software Framework for Efficient Tensor Representation and Decompositions for Pattern Recognition in Computer Vision;187
6.7.1;Introduction;187
6.7.2;Architecture of the Software Framework;188
6.7.3;Computation of the HOSVD Tensor Decomposition;191
6.7.4;Experiments;192
6.7.5;Conclusions;194
6.7.6;References;194
6.8;Hand Shape Recognition in Real Images Using Hierarchical Temporal Memory Trained on Synthetic Data;195
6.8.1;Introduction;195
6.8.2;Hierarchical Temporal Memory Concept;196
6.8.3;Problem Definition and Proposed Solution;197
6.8.4;Results and Discussion;199
6.8.5;Conclusions and Future Work;201
6.8.6;References;202
6.9;Performance Comparison among Complex Wavelet Transforms Based Face Recognition Systems;203
6.9.1;Introduction;203
6.9.2;GABOR Wavelets;204
6.9.3;Complex Wavelet Transform;205
6.9.3.1;Dual-Tree Complex Wavelet Transform;205
6.9.3.2;Single-Tree Complex Wavelet Transform;206
6.9.4;ProposedMethod;206
6.9.5;Simulation Results and Discussions;208
6.9.6;Conclusion;209
6.9.7;References;210
7;Part IV: Biomedical Image Processing;212
7.1;The Method of Immunohistochemical Images Standardization;213
7.1.1;Introduction;213
7.1.2;Images Charcteristics;214
7.1.3;Methods;216
7.1.4;Experimental Data;218
7.1.5;Results and Discussion;218
7.1.6;Conclusion;219
7.1.7;References;220
7.2;The Usefulness of Textural Features in Prostate Cancer Diagnosis;222
7.2.1;Prostate Cancer Diagnostics;222
7.2.2;Perfusion Computed Tomography;223
7.2.3;The p-CT Images;223
7.2.4;Textural Features;224
7.2.5;Methodology and Results;226
7.2.6;Conclusion;227
7.2.7;References;227
7.3;Noise Influance Reduction in Estimation of CBF, CBV and MTT, MRI Perfusion Parameters;229
7.3.1;Introduction;229
7.3.2;MRI Data Analysis;230
7.3.3;Interpolated Pixel Sampling;232
7.3.4;Tested Methods;233
7.3.5;Tests Results;234
7.3.6;Conclusions;235
7.3.7;References;236
7.4;Interpretation of the Sequences of Magnetocardiographical Images Based on Flow of Electrical Impulses through Human Heart;237
7.4.1;Introduction;237
7.4.2;MCG Data Formats;238
7.4.2.1;Time Runs;238
7.4.2.2;Magnetic Field Maps (MF Maps);238
7.4.2.3;Pseudo Current Density Maps (PCD Maps);239
7.4.3;Novel Approach to Patient’s MCG Data Evaluation;240
7.4.4;Experiment and Discussion;241
7.4.4.1;Experimental Database;241
7.4.4.2;Test Groups Construction;241
7.4.4.3;Parameters of the Experiment;241
7.4.4.4;Results Summary;242
7.4.4.5;Results Discussion;242
7.4.5;Conclusions;244
7.4.6;References;244
7.5;Automatic Left Ventricle Segmentation in T2 Weighted CMR Images;245
7.5.1;Introduction;245
7.5.2;Automatic Left Ventricle Segmentation System;246
7.5.2.1;Pre-processing and Centre Point Detection;246
7.5.2.2;Left Ventricle Segmentation;248
7.5.3;Experimental Result;250
7.5.4;Conclusion and Remarks;251
7.5.5;References;252
7.6;Research of Muscular Activity during Gait of Persons with Cerebral Palsy;253
7.6.1;Introduction;253
7.6.2;Methodology;254
7.6.3;Results and Discussion;255
7.6.4;Conclusions;258
7.6.5;References;259
7.7;Automated Recognition of Abnormal Structures in WCE Images Based on Texture Most Discriminative Descriptors;260
7.7.1;Introduction;260
7.7.2;Capsule Endoscopy;261
7.7.3;Texture Analysis;262
7.7.4;Vector Supported Convex Hull Method;263
7.7.5;Experiment;264
7.7.6;Results Discussion and Conclusions;265
7.7.7;References;266
7.8;Augmented Reality Interface for Visualization of Volumetric Medical Data;268
7.8.1;Introduction;268
7.8.2;Augmented Reality Environment;269
7.8.3;Real Time Rendering of 3D Data;270
7.8.4;The System Performance Test;271
7.8.5;Conclusion;273
7.8.6;References;274
7.9;Biomedical Computer Vision Using Computer Algebra: Analysis of a Case of Rhinocerebral Mucormycosis in a Diabetic Boy;275
7.9.1;Introduction;275
7.9.2;Image Processing Using Convolution;276
7.9.3;Image Processing Using Deblurring via Diffusion Equation;277
7.9.4;Image Processing Using Geometric Topology;278
7.9.4.1;Image Processing Using Tutte Polynomials for Graphs;279
7.9.4.2;Image Processing Using Khovanov Polynomials for Knots;280
7.9.4.3;Image Processing Using Homology;281
7.9.5;Conclusions;282
7.9.6;References;282
8;Part V: Applications;283
8.1;Adaptive B-Spline Model Based Probabilistic Active Contour for Weld Defect Detection in Radiographic Imaging;284
8.1.1;Introduction;284
8.1.2;Probabilistic Deformable Model;285
8.1.2.1;Probabilistic Image Model;285
8.1.2.2;Bayesian Approach for Contour Estimation;285
8.1.2.3;Case with Fixed $k$;286
8.1.2.4;Case with Adaptive $k$;286
8.1.3;Experimental Results;288
8.1.4;Conclusion;290
8.1.5;References;291
8.2;FONN-Based Affine-Invariant Image Recognition;293
8.2.1;Introduction;293
8.2.2;The Structure of the FONN Classifier;294
8.2.2.1;Invariance to Affine Transformations;295
8.2.3;Experimental Validation;296
8.2.4;Conclusion;300
8.2.5;References;300
8.3;Coarse-Grained Loop Parallelization for Image Processing and Communication Applications;301
8.3.1;Introduction;301
8.3.2;Image Processing and Communication Algorithms in the UTDSP Benchmark;302
8.3.3;Parallelism Extraction Using Iteration Space Slicing;303
8.3.4;Experiments;306
8.3.5;Conclusion;307
8.3.6;References;307
8.4;SMAS - Stereovision Mobility Aid System for People with a Vision Impairment;309
8.4.1;Introduction and Motivation;309
8.4.2;System Architecture;310
8.4.3;Obstacles Detection;310
8.4.3.1;Stereo Matching;311
8.4.3.2;Depth Map Segmentation;311
8.4.3.3;Object Identification;313
8.4.3.4;Risk Management;314
8.4.3.5;Experiments;314
8.4.3.6;Conclusions;315
8.4.4;References;316
8.5;Extracting Symbolic Function Expressions by Means of Neural Networks;317
8.5.1;Introduction;317
8.5.2;Simple Network Using Logarithmic and Exponential Functions;319
8.5.3;Network Based on Reciprocal Activation Functions;321
8.5.4;Learning the Network;322
8.5.5;Conclusions;323
8.5.6;References;324
8.6;Mathematical Morphology in the Process of Musical Notation Recognition;325
8.6.1;Introduction;325
8.6.2;Staff Line Removal Method;326
8.6.3;Preparing an Image to Notes Identification;328
8.6.4;Conclusions;329
8.6.5;References;329
8.7;GPU-Accelerated Object Tracking Using Particle Filtering and Appearance-Adaptive Models;330
8.7.1;Introduction;330
8.7.2;Object Tracking Using Appearance-Adaptive Models in Particle Filter;331
8.7.2.1;Particle Filtering;331
8.7.2.2;Appearance-Adaptive Models;333
8.7.3;Implementation of Object Tracking on GPU;334
8.7.3.1;Programming in CUDA;334
8.7.3.2;Implementation Details;334
8.7.4;Experimental Results;335
8.7.5;Conclusions;336
8.7.6;References;337
8.8;Application of Epipolar Rectification Algorithm in 3D Television;338
8.8.1;Introduction;338
8.8.2;Pinhole Camera Model;339
8.8.3;Proposed Rectification Algorithm;340
8.8.3.1;Camera Calibration;340
8.8.3.2;Coordinate Systems Transposition;340
8.8.3.3;Final Camera Parameters Calculation;342
8.8.3.4;Rectifying Transform Calculation;343
8.8.4;Conclusions;343
8.8.5;References;345
8.9;Crack Detection on Asphalt Surface Image Using Local Minimum Analysis;346
8.9.1;Introduction;346
8.9.2;Proposed Algorithm;347
8.9.2.1;Local Minimum Searching;347
8.9.2.2;Verification Process;348
8.9.3;Experimental Results;349
8.9.4;Conclusions;351
8.9.5;References;351
8.10;Eye Tracking System for Human Computer Interaction;353
8.10.1;Introduction;353
8.10.2;Object Detection;355
8.10.3;Pupil Center Detection;357
8.10.4;OpenCV Functions Used in Our System;358
8.10.5;Cursor Movement;359
8.10.6;Conclusions;360
8.10.7;References;361
9;Part VI: Communications;362
9.1;Errors Nature in Indoors Low Power 433 MHz Wireless Network;363
9.1.1;Introduction;363
9.1.2;The Study in More Detail;364
9.1.3;Packet Error Analysis;365
9.1.4;Frame Error Analysis;366
9.1.5;Frame Error Analysis;367
9.1.6;Conclusions;368
9.1.7;References;368
9.2;Using Google Earth for Visualization in FTTH Network Planning;369
9.2.1;Introduction;369
9.2.2;The Network Planning Process;370
9.2.3;Google Earth and Its Data Formats;372
9.2.4;Transformation Process;374
9.2.4.1;Application Examples;374
9.2.5;Conclusion;377
9.2.6;References;378
9.3;The Development of a Platform Based on Wireless Sensors Network and ZigBee Protocol for the Easy Detection of the Forest Fire. A Case Study;380
9.3.1;Introduction;380
9.3.2;Sensors Networks (WSN) Technology and the ZigBee Communication Protocol;381
9.3.3;The Platform;381
9.3.3.1;The Communication Network;381
9.3.3.2;The Wireless Sensor Network;383
9.3.3.3;The Reception Center;384
9.3.4;Programming the Nodes;384
9.3.5;System Architecture;385
9.3.6;Presentation of Data;387
9.3.7;References;388
9.4;Mazovia Broadband Network (MBN Network). Case Study;389
9.4.1;Introduction;389
9.4.2;Estimation of Total Volume IP Traffic Carried in MBN Network;390
9.4.3;MBN Network Implementation;393
9.4.4;MBN Architecture and Traffic Transfer in the Network;396
9.4.5;MBN Network Options and Active Equipment Configuration;397
9.4.6;Construction Cost for the Various Network Options;401
9.4.7;Conclusion;403
9.4.8;References;403
9.5;The Method of GMPLS Network Reliability Evaluation;405
9.5.1;Introduction;405
9.5.1.1;The GMPLS Network Architecture;406
9.5.1.2;Reliability Evaluation for Complex Systems;406
9.5.2;GMPLS as Multistate System;407
9.5.3;Example of Reliability Evaluation;408
9.5.3.1;Example 1;409
9.5.3.2;Example 2;409
9.5.4;Conclusions;411
9.5.5;References;411
9.6;The Improved Least Interference Routing Algorithm;413
9.6.1;Introduction;413
9.6.2;LSP Choice Algorithms;414
9.6.3;Proposed Algorithm of LSPs Choice;416
9.6.4;Obtained Results;417
9.6.5;Conclusions;420
9.6.6;References;421
9.7;Comparison of Modified Degree 6 Chordal Rings;422
9.7.1;Introduction;422
9.7.2;Background;423
9.7.3;Other Modified Topologies of Chordal Rings 6th Nodal Dergree;425
9.7.4;Comparison of Sixth Nodal Degree Chordal Rings;430
9.7.5;Conclusions;431
9.7.6;References;431
9.8;Evaluation of Measurement Based Admission Control Algorithms for IEEE 802.16 Networks in Simulations with L2S Physical Layer Abstraction and nbLDPC Codes;433
9.8.1;Introduction;433
9.8.2;Previous Work;434
9.8.3;ARAC with MCS and Connection State Control (ARAC) and ARAC Without MCS and Connection State Control (nscARAC);435
9.8.3.1;Introduction;435
9.8.3.2;Simulation Parameters and Results;436
9.8.3.3;Results Discussion and Conclusions;437
9.8.4;Performance Comparison of ARAC and EMAC;440
9.8.4.1;Introduction;440
9.8.4.2;Simulation Parameters and Results;441
9.8.4.3;Results Discussion and Conclusions;442
9.8.5;Future Work;444
9.8.6;References;444
9.9;The Gap between Packet Level QoS and Objective QoE Assessment of WWW on Mobile Devices;446
9.9.1;Introduction;446
9.9.2;Related Work;447
9.9.3;Market Analysis;448
9.9.4;Regulations and Standards (QoE/QoS) in Mobile Networks;448
9.9.5;Methodology;449
9.9.6;Results;451
9.9.7;Conclusions;452
9.9.8;References;453
9.10;Evaluation of Smoothing Algorithms for a RSSI-Based Device-Free Passive Localisation;454
9.10.1;Introduction;454
9.10.2;Initial Measurements;456
9.10.3;Evaluation;458
9.10.4;Conclusion;459
9.10.5;References;460
9.11;Performance Evaluation of ADS System Based on Redundant Dictionary;462
9.11.1;Introduction;462
9.11.2;Anomaly Detection Algorithm Based on Redundant Dictionary of Base Functions;463
9.11.3;Evaluation of the Proposed ADS Methodology;465
9.11.4;Comparison of the Matching Pursuit with Standard DWT Using 15 Traffic Parameters;467
9.11.5;Conclusion;468
9.11.6;References;469
10;Index;470