Handbook of Computer Vision and Applications, V3

Computer vision architecture......Page 48
Classes of tasks......Page 51
Architecture of Computer Vision Systems......Page 54
Field Programmable Gate Array Image Processing......Page 56
Introduction......Page 57
Architectures of field programmable gate arrays......Page 58
Computing power......Page 59
FPGA-based image processing systems......Page 62
Example system: the microEnable......Page 63
Device driver and application interface library......Page 65
Modes of operation......Page 67
Programming software for FPGA image processing......Page 68
CHDL......Page 69
Optimization of algorithms for FPGAs......Page 71
Integration into a general software system......Page 72
Image preprocessing......Page 73
Convolution......Page 74
JPEG compression......Page 75
Conclusions......Page 76
References......Page 77
Multimedia Architectures......Page 78
Introduction......Page 79
Peak computing performance......Page 80
Data transfer......Page 82
History......Page 83
Multimedia instruction sets......Page 84
Registers and data types......Page 85
Instruction format and processing units......Page 86
Basic arithmetic......Page 88
Logical operations......Page 89
Comparison, minimum, and maximum operations......Page 90
Conversion, packing, and unpacking operations......Page 91
Point operations......Page 92
Convolution......Page 94
Gray-scale morphology......Page 95
Binary morphology......Page 96
Conclusions and outlook......Page 97
References......Page 99
Developing specialized components for a host system......Page 100
Requirements......Page 103
Identifying development phases......Page 104
Repository......Page 107
Statistical image analysis component......Page 110
Optimal parameter value estimation component......Page 111
Creation of graphical user interfaces......Page 113
Implementation techniques......Page 117
A sample use of the architecture......Page 118
Future work......Page 121
References......Page 122
Software Engineering for Image Processing and Analysis......Page 124
Introduction......Page 125
Object-oriented principles, analysis, and design......Page 126
Object-oriented programming......Page 128
Software engineering for image processing......Page 129
Programming languages for image processing......Page 130
Object-oriented programming languages......Page 131
Proprietary image processing programming languages......Page 133
Summary......Page 134
Data flow......Page 135
Devices and actors......Page 136
Statistical object recognition......Page 137
Data representation......Page 138
Image operator hierarchy......Page 140
Hierarchy for optimization algorithms......Page 142
Hierarchy for actors and devices......Page 143
Efficiency......Page 145
Conclusion......Page 146
References......Page 147
Reusable Software in Computer Vision......Page 150
Introduction......Page 151
Generic data structures......Page 154
Iterators......Page 156
Functors......Page 157
Data accessors......Page 158
Meta-information: traits......Page 159
Two-dimensional iterators......Page 160
Navigation......Page 161
Direct data access......Page 163
Image boundaries......Page 164
Image data accessors......Page 165
Basic image processing algorithms......Page 167
Functors for image processing......Page 170
Higher-level functions......Page 171
Performance......Page 173
Image iterator adapters......Page 174
Row and column iterators......Page 175
Iterator adapters for arbitrary shapes......Page 176
Conclusions......Page 178
References......Page 179
Introduction......Page 180
Analytical performance characterization......Page 181
Empirical performance characterization......Page 182
Application-oriented empirical algorithm assessment......Page 183
Assessment data......Page 184
Image data acquisition......Page 186
Algorithm performance characterization......Page 187
The assessment function......Page 188
Overview of the assessment procedure......Page 189
The assessment system......Page 190
Example: Assessment of a detection algorithm......Page 192
Conclusion......Page 195
References......Page 196
Introduction......Page 200
Invariances......Page 203
Building normalized representations......Page 204
The tolerance-separability problem......Page 208
Tolerant contour representations......Page 211
Recognition of normalized representations......Page 216
Introduction of color......Page 218
View-based recognition of three-dimensional objects......Page 220
Gabor-based contour representations......Page 222
Extrafoveal recognition......Page 225
Advantages and limits of the holistic system......Page 228
Advantages and limits of classical AI systems......Page 229
Modeling with holistically learned representations......Page 230
Instantiation strategies......Page 234
Recognition results......Page 236
Automatic generation of object models......Page 238
Conclusion......Page 241
References......Page 242
Introduction......Page 244
Marr's theory and its drawbacks......Page 245
Principles......Page 246
Criticism......Page 248
Basic concepts of active vision......Page 249
Attention......Page 252
Gaze control......Page 253
Hardware and software concepts......Page 254
Eye-hand coordination......Page 255
Examples for active vision environments......Page 256
Applications for active vision devices......Page 260
References......Page 262
The Global Algebraic Frame of the Perception-Action Cycle......Page 268
Introduction......Page 269
From knowledge-based to behavior-based systems......Page 270
Metaphor of biological competence......Page 271
From natural to artificial behavior-based systems......Page 272
Bottom-up approach of design......Page 273
Grounding of meaning of equivalence classes......Page 274
General principles of behavior-based system design......Page 275
Algebraic frames of higher-order entities......Page 276
Perception, action and geometry......Page 277
Behavioral modulation of geometric percepts......Page 278
Roots of geometric algebra......Page 279
From vector spaces to multivector spaces......Page 280
Properties of multivector spaces......Page 282
Functions, linear transformations and mappings......Page 284
Qualitative operations with multivectors......Page 285
Geometric algebra of the Euclidean space......Page 286
Projective and kinematic spaces in geometric algebra......Page 288
Geometric entities in geometric algebra......Page 290
Operational entities in geometric algebra......Page 293
Image analysis and Clifford Fourier transform......Page 296
Pattern recognition and Clifford MLP......Page 301
Summary and conclusions......Page 306
References......Page 307
Industrial and Technical Applications......Page 312
Introduction......Page 314
Early applications......Page 315
System architectures......Page 316
Sensor technology......Page 317
Economical situation......Page 318
Structure of suppliers......Page 319
Main fields of applications......Page 321
Products versus engineering......Page 322
Mainstream technology used today......Page 323
Future sensor technology......Page 324
Future imaging system technology......Page 325
Conclusions......Page 326
Future needs of the imaging industry......Page 327
References......Page 329
Geosciences......Page 330
Material sciences......Page 331
Biological and medical imaging......Page 333
Industrial applications......Page 334
Identification and security control......Page 335
Image coding......Page 336
Other applications......Page 337
References......Page 338
Introduction......Page 344
Problem description......Page 345
Market analysis and solutions......Page 346
Overview......Page 347
An example: the intelligent camera system Intelli-Cam......Page 348
The problem......Page 352
Data sets......Page 353
Image features for object recognition......Page 354
Performance and results......Page 356
Discussion and outlook......Page 358
References......Page 360
Introduction......Page 362
Codings......Page 363
Functional principle and system setup......Page 364
Architecture......Page 365
Identification in the automobile industry......Page 368
Identification in steel plants......Page 370
Identification of license plates......Page 371
Identification of mailing labels......Page 373
References......Page 375
Introduction......Page 376
Requirements of motion analysis......Page 377
Methods of motion analysis......Page 379
Object tracking......Page 380
Precision......Page 382
Occlusion......Page 383
Three-dimensional calibration......Page 385
System integration---the software WINanalyze......Page 386
Sports industry......Page 387
Physics......Page 388
Conclusion and remarks......Page 390
References......Page 391
Introduction......Page 392
Geometry check of wing roots......Page 394
Three-dimensional image metrology unhbox voidb @x hbox {in shipbuilding}......Page 396
Machine control and TI/$^2$ technology......Page 398
Developments......Page 401
References......Page 404
Reverse Engineering Using Optical Range Sensors......Page 406
Introduction......Page 407
Registration......Page 409
Surface reconstruction and smoothing......Page 410
Calibration......Page 411
Registration......Page 412
Coarse registration......Page 413
Surface reconstruction......Page 414
Surface modeling and smoothing......Page 416
Modeling of scattered data......Page 417
Surface smoothing......Page 418
Experiments......Page 420
Examples......Page 423
Conclusions......Page 424
References......Page 426
Topographical Maps of Microstructures......Page 428
Depth-from-focus approaches......Page 429
Setup......Page 431
Digital contrast operators......Page 433
Optical theory......Page 434
Active illumination with periodic patterns......Page 436
Aberrations......Page 438
Regression algorithm......Page 439
Optimum stepsize......Page 441
Noise-limited resolution......Page 442
Topographic edges......Page 444
Reflection edges......Page 445
Fuzzy logic error suppression......Page 446
Measurement of layer thickness......Page 448
Extensions of theoretical description......Page 449
Experimental proof and resolution......Page 451
Conclusions......Page 453
References......Page 455
Introduction......Page 458
Geodesic interpolation of contour data......Page 459
Plateau image generation......Page 460
Interpolation along steepest slope lines......Page 461
Extensions......Page 463
Drainage network detection......Page 465
Removal of irrelevant minima......Page 467
Determination of flow directions......Page 468
Flow simulation......Page 469
Watershed detection......Page 471
Concluding remarks......Page 472
References......Page 473
Introduction......Page 476
System overview......Page 478
Off-line calibration......Page 479
Online self-calibration......Page 480
Exploiting scene constraints......Page 481
Constrained block matching......Page 483
Subpel disparity estimation......Page 484
Evaluation of measurement accuracy......Page 485
Three-dimensional model building......Page 486
Intensity-based motion tracking......Page 487
Surface update and depth fusion......Page 489
Uncalibrated monocular sequences......Page 491
Three-dimensional modeling at Sagalassos: A test case......Page 492
Conclusions......Page 493
References......Page 495
Introduction......Page 498
Light-stripe scanners......Page 499
General scanner requirements......Page 500
Evaluation hardware and software......Page 501
Mechanical design......Page 502
Ranges of application......Page 503
Measurement of wooden surface profiles......Page 504
Measurement of aluminum ingots......Page 506
Inspection of tubes for the tightening of seat belts......Page 507
In-line welding seam inspection of steel wheels......Page 508
Visual inspection of applied solder paste on printed circuit boards......Page 509
Piston assembly......Page 511
References......Page 513
Introduction......Page 514
Internal model......Page 515
Geometric model......Page 516
Mapping features......Page 518
Color as a feature......Page 519
Estimating three-dimensional positions of features......Page 520
Matching......Page 522
Implementation......Page 524
Applications......Page 526
References......Page 529
Introduction......Page 532
Calibration......Page 534
Model prediction......Page 536
Feature detection......Page 537
Object recognition......Page 538
Localization......Page 539
Articulated objects......Page 541
Applications......Page 542
Conclusion......Page 544
References......Page 546
Introduction......Page 548
Formal specification of flexible face models......Page 551
Matching the flexible face model to an image......Page 553
Correspondence by optical flow algorithms......Page 554
Automated model learning......Page 555
View synthesis......Page 557
Conclusions......Page 558
References......Page 560
Introduction......Page 562
Image retrieval systems......Page 563
Video retrieval systems......Page 564
Overview......Page 567
Texture......Page 568
Object recognition......Page 569
Knowledge representation......Page 570
Strategies for modeling the domain knowledge......Page 571
Examples......Page 572
Conclusion......Page 573
References......Page 574
Introduction......Page 578
The vision part......Page 579
The processing and control part......Page 584
The virtual tactile display......Page 585
References......Page 587
The Neural Active Vision System NAVIS......Page 590
Introduction......Page 591
The Pioneer robot......Page 592
Gray-level segmentation......Page 594
Color quantization......Page 595
Depth estimation......Page 596
Attractivity representation......Page 598
Gaze control......Page 600
Object recognition......Page 601
Two-dimensional object recognition......Page 602
Three-dimensional object recognition......Page 604
Displacement vector estimation......Page 608
Correspondence analysis......Page 609
Tracking process......Page 611
References......Page 613
Dynamic Vision for Perception and Control of Motion......Page 616
Introduction......Page 617
Active vision......Page 618
Dynamic vision......Page 619
Differential and multiple integral scales......Page 620
Road vehicles......Page 624
Sensory information used......Page 625
Vision sensors......Page 626
Dynamic perception with spatiotemporal models......Page 627
Basic assumptions underlying the four-dimensional approach......Page 629
Cybernetic structure of the four-dimensional approach......Page 631
Generic four-dimensional object classes......Page 633
Image feature extraction......Page 636
Feature aggregation......Page 648
State estimation......Page 649
Situation assessment......Page 651
Generation of behavioral capabilities......Page 652
Multiple loops in dynamic scene understanding......Page 653
Multiple-loop closure on different time scales......Page 654
Road vehicles......Page 656
Autonomous visual navigation of air vehicles......Page 661
Conclusions and outlook......Page 663
References......Page 664
Scientific Applications......Page 668
Introduction......Page 670
Depth-from-focus based measurements......Page 671
Measurement principle......Page 673
Description of the sensor......Page 674
Data processing......Page 677
Image processing and depth-from-focus......Page 678
Depth-from-focus......Page 680
Calibration......Page 682
Calculation of particle concentration......Page 683
Results and discussion......Page 685
In situ microscopy......Page 686
The in situ microscope......Page 687
Depth-from-focus algorithm......Page 688
References......Page 692
Introduction......Page 694
Historical review......Page 696
Experimental setup......Page 697
Optical wave follower......Page 700
Combined fluorescein/oxygen quenching experiments......Page 701
Images and image processing......Page 702
Results......Page 705
References......Page 707
Particle-Tracking Velocimetry......Page 710
Introduction......Page 711
Setup for particle-tracking velocimetry......Page 712
Setup for stereo particle-tracking velocimetry......Page 713
Applications for stereo particle-tracking velocimetry......Page 714
Choice of seeding particles and scattering angle......Page 716
Image processing for particle-tracking velocimetry......Page 718
Segmentation: region-growing......Page 719
Segmentation: model-based......Page 720
Image-sequence analysis......Page 723
Removing bad vectors from the flow field......Page 726
Accuracy of particle-tracking velocimetry......Page 727
Hybrid particle-imaging/particle-tracking velocimetry......Page 730
Stereo particle-tracking velocimetry......Page 731
Geometric camera calibration......Page 732
The camera model......Page 733
Virtual camera......Page 734
Stereoscopic correspondence......Page 735
The stereo correlation algorithm......Page 736
Results......Page 739
Conclusions......Page 741
References......Page 742
Analyzing Particle Movements at Soil Interfaces......Page 746
Experimental setup......Page 747
The pressure tank......Page 748
Endoscopes......Page 749
Image analysis......Page 750
Motion detection......Page 751
Motion compression......Page 752
Optical flow estimation......Page 753
Mixing parameters......Page 755
Estimation of water flow......Page 759
Movements due to pressure changes......Page 760
Observed and predicted motion......Page 761
Mixing......Page 762
Flow in gravel......Page 763
Conclusions and future activities......Page 764
References......Page 765
Introduction......Page 766
Previous investigations......Page 767
Mechanical setup......Page 769
Illumination......Page 770
Image and data acquisition......Page 771
Calibration......Page 772
Motion estimation......Page 774
Interpolation of sparse displacement vector fields......Page 776
Stability and validation......Page 777
Applications......Page 778
Turgor-induced variations of growth......Page 779
Outlook......Page 780
References......Page 781
Introduction......Page 784
The necessity of modeling fluorescence images......Page 785
The complex structure of muscle cells......Page 786
Ca$^{2+}$-regulation of muscular contraction......Page 787
Ca$^{2+}$-measurements......Page 788
Mathematical modeling......Page 789
Numerical model of the Ca$^{2+}$-turnover......Page 790
Simulated Ca$^{2+}$-transients......Page 793
Conclusions......Page 794
References......Page 796
Introduction......Page 798
The controlled flux technique......Page 799
Experimental setup......Page 803
Calibration......Page 804
Results and conclusions......Page 806
References......Page 807
Botanical background......Page 810
Transpiration measurements......Page 812
Theoretical background......Page 813
Leaf energy budget......Page 814
Calculation of transpiration rates......Page 817
Passive thermography......Page 819
Active thermography......Page 823
References......Page 827
Introduction......Page 830
The global ozone monitoring experiment (GOME)......Page 832
Earth coverage......Page 833
Differential optical absorption spectroscopy (DOAS)......Page 834
Nonlinear fitting algorithm......Page 837
B-spline interpolation......Page 838
Interpolation using a wind-vector field......Page 839
Normalized convolution......Page 842
Air mass factors......Page 843
Cloud detection......Page 844
Separation of troposphere from stratosphere......Page 847
Results......Page 849
References......Page 851
Introduction......Page 854
Problems of storm tracking......Page 855
Background: Zhang/Krezeski's algorithms......Page 856
Fuzzy point algebra......Page 859
Storm hypothesis......Page 860
Incremental relaxation-labeling algorithm......Page 861
Experimental results......Page 864
Conclusions......Page 865
References......Page 867
Introduction......Page 868
Image restoration......Page 870
Differential feature detection for segmentation......Page 872
Topology preserving three-dimensional thinning......Page 876
Graph construction and interpretation......Page 878
Results......Page 880
Discussion......Page 881
References......Page 883
The problem......Page 886
Principles of spectral precision distance microscopy......Page 892
Determination of the resolution equivalent in situ......Page 899
Conclusions......Page 900
References......Page 902
Introduction......Page 906
Overview of methodologies......Page 907
Morphological parameters of chromosome territories......Page 910
Topological parameters of subchromosomal targets......Page 912
Biological experiments and data acquisition......Page 917
Bias reduction in distance measurements......Page 918
Cluster analysis of early- and late-replicating foci......Page 920
Discussion and outlook......Page 921
References......Page 924
Index......Page 926