Image Analysis and Recognition: Third International Conference, ICIAR 2006, Póvoa de Varzim, Portugal, September 18-20, 2006, Proceedings, Part I (Lecture ... Vision, Pattern Recognition, and Graphics)

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000.pdf......Page 2
Unsupervised Learning and Self Organization......Page 27
Synaptic Self-amplification and Competition......Page 28
The Kohonen Self-Organizing Map (SOM)......Page 29
The Self-Organizing Tree Map (SOTM)......Page 30
Application of SOTM to Image Retrieval......Page 31
The Directed Self-Organizing Tree Map (DSOTM)......Page 32
Genetic Algorithm and Automatic CBIR System (GA-CBIR)......Page 33
Visualization......Page 34
Characterization and Analysis......Page 36
Local Variance Driven Self-Organization: Toward the Generation of Forests from Trees......Page 37
References......Page 39
015.pdf......Page 41
Introduction......Page 42
Foundations of DBCs......Page 43
Prototype Selection Methods for DBCs......Page 44
Dissimilarity Measures Used in DBCs......Page 45
State-of-the-Art Prototype Reduction Schemes......Page 46
Proposed Optimization of DBC's......Page 47
Experimental Results : Artificial/Real-Life Data Sets......Page 49
Conclusions......Page 53
Adaptivity with Spatial Structures......Page 55
Adaptivity with Intensity Values......Page 56
GANs Sets......Page 57
GAN Mean and Rank Filtering......Page 58
Adaptive Structuring Elements......Page 59
Image Restoration......Page 60
Image Enhancement......Page 61
Image Segmentation......Page 64
References......Page 65
Introduction......Page 67
Motivation......Page 68
Method......Page 69
Results......Page 72
Limitations......Page 77
Introduction......Page 79
Bilateral Filtering and Its Origin......Page 81
Digital Bilateral Total Variation......Page 82
Nonlinear Filtering-Based Image Magnification......Page 83
Experiment Results and Performance Analysis......Page 84
References......Page 87
Introduction......Page 89
Conventional Spatial Domain POCS......Page 90
LPF in POCS......Page 91
DCT Domain Separable Symmetric Linear Filtering......Page 92
Proposed DCT Domain POCS......Page 94
Simulation Results......Page 95
Complexity Comparison......Page 96
Conclusion......Page 98
Introduction......Page 100
Rank-Ordered Differences Statistic for Color Images......Page 101
Experimental Results......Page 102
Conclusions......Page 106
Introduction......Page 108
Basic Procedures and Equations in MRI......Page 109
Super-Resolution MRI in the Spatial Domain......Page 111
Super-Resolution MRI in the Frequency Domain......Page 112
Further Analysis of Case II: Phase Ramping Before Filtering/Sampling......Page 114
Discussion and Future Work......Page 117
Introduction......Page 120
Low Rank Approximation......Page 121
Blur Estimation Using Low Rank Approximation of Cepstrum......Page 122
Gaussian Blur......Page 123
Motion Blur......Page 124
Results......Page 125
Conclusion......Page 127
Introduction......Page 130
Repetitive Structures......Page 131
Proposed Reconstruction Scheme......Page 132
Matching and Registration of Repetitive Structures......Page 133
Data Fusion......Page 134
Denoising and Deblurring......Page 135
Experiments and Results......Page 136
Conclusion......Page 140
Introduction......Page 142
Variational and PDE Formulations of Denoising......Page 143
Review of Finite Element Methods......Page 144
Discontinuous Finite Element Methods......Page 145
Experiments and Discussions......Page 147
Conclusion and Future Work......Page 149
Introduction......Page 152
Well-Balanced Flow Equation (WBFE)......Page 153
Overview of the Proposed Method......Page 154
Interpolation and Restriction Filters......Page 155
Results......Page 156
Conclusions and Future Work......Page 162
Introduction......Page 164
Bilateral Filtering......Page 165
Fuzzy Metric Approach......Page 166
Fuzzy Bilateral Filtering......Page 167
Experimental Results......Page 168
Conclusions......Page 170
Introduction......Page 172
Proposed MPEG Processing......Page 173
Mallat Wavelet......Page 174
Edge Map Detection......Page 175
Inter-block Filtering......Page 176
Intra-block Filtering......Page 177
Experimental Results......Page 178
References......Page 181
Introduction......Page 182
Order Statistic Filters on Tensor Data......Page 183
Algorithmic Considerations......Page 184
Experimental Results......Page 186
References......Page 188
Color Mathematical Morphology......Page 189
The New Color Space for Processing:$L1-Norme$......Page 190
Vector Connected Filters......Page 191
Application: Gaussian Noise Removal......Page 192
References......Page 197
Introduction......Page 199
Shape-Based Segmentation with Level-Sets......Page 200
Space of Shapes......Page 201
Shape-Based Approach to Region-Based Segmentation......Page 202
Numerical Algorithm......Page 204
Shape Prior Involving Objects of the Same Type: ``Swedish Couple''......Page 205
Shape Priors Involving Objects of Different Types: ``Yellow'' and ``Orange''......Page 206
Conclusion......Page 208
Introduction......Page 210
Weighted Kappa Coefficient......Page 212
Geometric Approach......Page 216
Experimental Results and Conclusion......Page 217
Discussion......Page 220
Introduction......Page 222
Velocity Tuning......Page 223
Coherent Motion Based on “Velocity Channels”......Page 224
Application to an Overtaking Warning System......Page 227
Conclusions......Page 229
References......Page 230
Introduction......Page 232
The Proposed Algorithm Design......Page 233
Algorithm Evaluations......Page 237
References:......Page 239
Introduction......Page 241
Semi-automatic Method......Page 242
Using AdaBoost Learning to the Segmentation Problem......Page 243
Iterative Segmentation by Graph-Cuts......Page 246
Experiments......Page 250
References......Page 251
Introduction......Page 252
Proposed New Snake Energy Function......Page 253
Optimizing the Number of Snake Points......Page 256
Experimental Results......Page 257
References......Page 260
Introduction......Page 262
Efficient Graph Based Segmentation......Page 264
Feature Point Matching......Page 265
Region Grouping with Feature Points......Page 266
Grid-Based Space-Time Merging......Page 268
Subgraph Matching......Page 269
Experimental Results......Page 271
Conclusion......Page 272
Introduction......Page 274
Previous Work......Page 276
Local Consistency Error......Page 277
Distance Distribution Signatures......Page 278
Precision-Recall Measures......Page 279
Earth Mover's Distance......Page 280
New Discrepancy Measure......Page 281
Analysis on Evaluation Methods......Page 282
Conclusion......Page 284
Introduction......Page 286
Formalization and Examples......Page 287
Estimation of the Computing Power of the Weighted Primitive......Page 289
Parametric Architecture......Page 290
The HT Calculation Under the Scope of the Recursive Operation......Page 291
The Fourier Transform Calculation Under the Scope of the Recursive Operation......Page 293
References......Page 296
Introduction......Page 298
Topological Active Nets......Page 299
Mutation Operator......Page 301
Additional Considerations......Page 303
Results......Page 304
Conclusions......Page 307
Introduction......Page 309
Segmentation of Microscopy Images of Palynofacies......Page 310
Comparison of Sets of Centres Based on Distances......Page 312
Comparison of Sets of Centres Based on Segmentation Heuristics......Page 313
A Single-Object Illustration......Page 315
A Multiple-Object Illustration......Page 316
Results on Microfossil Images......Page 317
Conclusions......Page 319
Introduction......Page 321
Facial Shape Constraints......Page 323
Shape Alignment......Page 324
Active Shape Model Based Tracking......Page 325
Skin Color Segmentation Under Time-Varying Illumination......Page 327
Experiments......Page 329
Conclusion......Page 331
Introduction......Page 333
Proposed Filtering Design......Page 334
Experimental Results......Page 337
Conclusion......Page 342
Introduction......Page 344
Principle of 2D Maximally Decimated Filter Banks......Page 345
1D LP Near-PR Filter Banks with Partial Cosine Modulation......Page 347
2D LP Near-PR Filter Banks with Partial Cosine Modulation......Page 349
Design Example......Page 351
References......Page 353
Introduction......Page 355
2D Hermite Functions......Page 356
Fast Hermite Projection Method......Page 357
Image Database Retrieval......Page 360
Foveation......Page 361
References......Page 363
Introduction......Page 365
Bayesian Image Analysis......Page 366
Posterior Sampling......Page 367
Problem Formulation......Page 368
Gibbs Sampling Subject to Constraint......Page 369
Results and Evaluation......Page 371
Conclusion......Page 376
Introduction......Page 377
Image Reconstruction and Denoising......Page 378
Image Reconstruction from Sparse Data......Page 379
Experimental Results......Page 382
Conclusions......Page 383
Introduction......Page 385
Proposed Detection and Removal of Corrupted Pixels......Page 386
Experimental Study and Performance Comparison......Page 388
Conclusions......Page 392
Introduction......Page 396
Proposed Framework......Page 398
Spatial Training......Page 399
Experimental Results......Page 402
Discussion......Page 405
Introduction......Page 407
Binary and Colour Morphology......Page 408
Pixel Replication or Nearest Neighbour Interpolation......Page 409
Corner Detection......Page 410
Corner Validation......Page 411
Magnification by an Integer Factor n > 2......Page 413
Experimental Results......Page 414
Conclusion......Page 418
Introduction......Page 420
Kernel Histogram and Kernel Bandwidth......Page 421
Method Proposed......Page 422
Detection of Corner Correspondences......Page 423
Object Centroid Registration by Backward Tracking......Page 424
Bandwidth Selection by Robust Regression......Page 425
Experimental Results......Page 426
Conclusion......Page 428
Reference......Page 429
Introduction......Page 430
Related Work......Page 431
Technical Challenges and Analysis......Page 432
Kirsch Compass Kernels......Page 433
Laplacian......Page 434
Shortage on Existing Edge Detectors for Night Vision Analysis......Page 435
Proposed Method......Page 436
References......Page 438
Introduction......Page 440
Related Work......Page 441
Proposed Approach......Page 442
Segment’s Detection......Page 443
Segment’s Information Storing......Page 444
Detecting Segment’s Regularity......Page 445
Video Text Localization......Page 446
Experimental Result......Page 449
References......Page 450
Introduction......Page 452
Singular Value Decomposition (SVD)......Page 453
Singular Value as Image Signature......Page 454
Construction of the Proposed Signature and Distance Measure......Page 456
Matching Algorithm......Page 457
Comparisons with Ordinal Measure for Artificial Images......Page 458
Matching Results for Real Video Sequence......Page 459
References......Page 461
Formulation Problem and Notation......Page 462
Motivation......Page 464
The Proposed Method......Page 466
Results......Page 468
Conclusions......Page 471
Introduction......Page 472
Block-Based Fractal Image Coding......Page 474
Evolution in the Presence of Diffusion......Page 477
Evolution for a Convex Combination of Contraction Mappings......Page 479
Concluding Remarks......Page 481
Introduction......Page 484
Macroblock and Sub-macroblock Modes......Page 485
Macroblock Mode Selection in the Reference Software......Page 486
Complexity Reduction of Inter8×8......Page 487
Complexity Reduction of Intra4×4......Page 489
Simulation Results......Page 490
References......Page 493
Introduction......Page 494
Previous VSS Schemes......Page 495
Basic Concept......Page 496
The Proposed VSS Scheme......Page 498
The Contrast......Page 501
Experimental Results for the Black/White Printed Text Image......Page 502
Wide Applicability of the Proposed VSS Scheme......Page 503
Conclusion......Page 504
References......Page 505
Introduction......Page 506
Wavelet Packet Decomposition......Page 507
Cost Functions......Page 508
Variance Based Basis Selection Criterion......Page 509
L1-Norm Based Basis Selection Criterion......Page 512
Experimental Results......Page 514
References......Page 517
Introduction......Page 519
Fractal Image Encoding......Page 520
Set Theoretic Image Coding and Restoration......Page 522
Self-similarity Constraints Using Collage Distances......Page 523
Discrete Pointwise Collage Constraints and Associated Projections......Page 525
Restoration of an Image with an Incomplete Fractal Code......Page 527
Fractal Image Denoising......Page 529
Conclusions......Page 530
Introduction......Page 533
H.263 Annex I Intraframe Coding Method......Page 534
Proposed DCT Coefficient Prediction Method......Page 537
Simulation Results......Page 539
References......Page 541
Introduction......Page 542
Detection of Corner Outlier Artifacts......Page 543
Simulation Results......Page 546
References......Page 550
Introduction......Page 552
Rate Control Algorithm of MPEG-2 TM5......Page 553
Generation of Test Pattern in High Quality......Page 554
Proposed Rate Control Algorithm for Static Images......Page 555
Target Bit Allocation by GOP Unit......Page 556
Update of Target Bits by GOP Unit......Page 557
Experiments and Results......Page 558
References......Page 560
Introduction......Page 561
Problem Formulation......Page 562
LJPEG2000 Algorithm......Page 564
Simulation Results......Page 566
Conclusion......Page 572
Introduction......Page 573
Overview of Video Encryption Algorithms......Page 575
Our Model......Page 577
Security Analysis of Our Model......Page 580
Performance Analysis and Experimental Results......Page 582
Conclusions and Future Research......Page 583
Introduction......Page 585
Factorization of the Transform Kernel Matrix......Page 586
Simplified S-Transform for the Low Pass Block......Page 589
Summary of the Proposed Scheme......Page 591
Experimental Results......Page 592
Our Proposed Method vs. Pixel Domain Method......Page 593
Complexity Comparison......Page 594
References......Page 595
Introduction......Page 597
Conventional Fast Full Search Algorithms......Page 598
Proposed Algorithm......Page 599
Experimental Results......Page 601
Conclusions......Page 604
References......Page 605
Introduction......Page 606
Data Embedding and Extraction......Page 607
Experimental Results and Analysis......Page 608
References......Page 612
Introduction......Page 613
Watermark Embedding......Page 615
Quantization Function......Page 616
PSNR Estimation......Page 618
Results......Page 621
References......Page 624
Introduction......Page 626
Component Description......Page 627
Invariance......Page 628
Shape and Volume Identification......Page 629
Rotation and Reflection......Page 630
Neighborhood Operators......Page 631
Neighborhood Coding......Page 632
Coding According Neighborhood......Page 633
Code Reduction......Page 634
Experiment and Results......Page 635
Summary and Conclusions......Page 636
Introduction......Page 638
Detection of Prominent Regions......Page 639
Structure and Features of Regions......Page 641
Regions Versus Adjectives......Page 642
Region Clusters Versus Region Names......Page 643
Region and Image Retrieval......Page 646
Retrieval Results and Discussion......Page 647
Conclusions......Page 648
References......Page 649
Introduction......Page 650
Comparison of Sets of Shape Contexts by Means of Bipartite Matching......Page 651
Dynamic Time Warping......Page 652
Comparison of Canonical Sequences of Points by Means of DTW: The WARP System......Page 653
Cyclic Dynamic Time Warping......Page 655
The Proposed Method: CDTW of Shape Contexts Cyclic Sequences......Page 656
SQUID Database......Page 658
MPEG7 Database......Page 659
Discussion......Page 660
Introduction......Page 662
Image Attractors for the Active Net......Page 664
Topological Analysis......Page 666
Fitting Process of an Active Net......Page 668
Similarity Model for Topological Active Nets......Page 669
Experimental Results......Page 671
Conclusions......Page 672
Introduction......Page 674
Background......Page 675
Sketch-Based Image Query......Page 676
Feature Point Identification......Page 678
Mapping Unknown Fin Outline to Reference Fin Outline......Page 680
Selection of Corresponding Points for Error Calculation......Page 682
Testing and Results......Page 684
Introduction......Page 687
Relief......Page 688
The Method......Page 689
Experiments......Page 690
Toy Data......Page 691
Image and Shape Retrieval......Page 693
Conclusion and Future Work......Page 698
Introduction......Page 699
Block Edge Pattern Extraction in DCT Domain......Page 701
Construction of Indexing Keys......Page 703
Experimental Results and Analysis......Page 706
References......Page 709
Introduction......Page 711
Related Research Work......Page 712
The Proposed Model......Page 713
Estimation of Model's Parameters......Page 715
Recommendation......Page 716
The Data Set......Page 717
Evaluating the Rating's Prediction Accuracy......Page 718
Evaluating the Recommendation Algorithm......Page 719
Conclusion......Page 720
Introduction......Page 723
Information Retrieval in $IntelliSearch$......Page 725
Semantic Similarity Retrieval Model (SSRM) [16]......Page 726
PicASHOW [15]......Page 727
Weighted PicASHOW (WPicASHOW) [11]......Page 729
Weighted HITS [14]......Page 730
$IntelliSearch$ Architecture......Page 731
Conclusions......Page 732
Introduction......Page 735
Visual Rhythm and Pixel Sampling......Page 736
Characteristics of VRS for Automatic Shot-Change Detection......Page 737
Wipe Detection Algorithm......Page 740
Scene-Cut Detection Algorithm......Page 741
Experimental Results......Page 742
Conclusion......Page 745
References......Page 746
Motivation and State of the Art......Page 747
Paper Organization......Page 748
Global Motion Parameterization......Page 749
Feature-Based, Featureless, or Both ?!......Page 750
Summary of the Algorithm......Page 751
Chess Table Images......Page 752
Beach Towel Video Sequence......Page 753
Carpet Video Sequence......Page 754
Conclusion......Page 755
Introduction......Page 757
Detecting Moving Boundaries......Page 760
Intrinsic Curves......Page 761
Detecting Motion......Page 762
Mixture of Gaussians......Page 763
Estimating the Background Model......Page 764
Experimental Results......Page 765
Conclusion......Page 766
Introduction......Page 769
Definitions and Notations......Page 770
Theory......Page 772
Recommendations......Page 774
Conclusion and Summary......Page 776
Definitions and Notations......Page 778
Theory......Page 779
Introduction......Page 781
The Proposed Model......Page 782
2D Motion Estimation......Page 784
The Algorithm......Page 786
Experimental Results......Page 787
Conclusions......Page 790
Spatio-temporal Filtering......Page 791
Introduction......Page 793
Singular Points......Page 795
Flow......Page 796
Error Measure for Anchor Point Localisation and Tracking......Page 797
Stationary Reconstruction......Page 798
Proposed Vector Field Retrieval Method......Page 799
Evaluation......Page 800
Quantitative Evaluation......Page 801
Summary and Recommendations......Page 803
Recommendations......Page 804
Introduction......Page 806
Two-Dimensional Formulation......Page 807
Finite Element Bilinear Method (FETBI)......Page 808
Presmoothing Images......Page 809
Smoothing Implementation......Page 810
Experimental Results......Page 811
Conclusion......Page 816
Introduction......Page 818
Motion Estimation Methods......Page 819
Comparison of Pixel-Based and Window-Based Optical Flow Methods......Page 820
Anisotropic Diffusion Method......Page 821
Frame Interpolation......Page 822
Simulation Results and Discussions......Page 824
Conclusions......Page 828
References......Page 829
Introduction......Page 830
Sub-matrices Processing......Page 832
Synthetic Object......Page 834
Real Object......Page 837
Conclusions and Future Work......Page 840
Introduction......Page 842
Paper Organization......Page 843
Maximum Likelihood Inference......Page 844
Maximum Likelihood Estimation: Greedy Algorithm......Page 845
Initialization: Motion Detection......Page 846
Synthetic Sequence 1......Page 847
Experiments......Page 848
Conclusion......Page 851
Introduction......Page 853
3D Tracking with Stereo Vision System......Page 854
The Smallest Singular Value......Page 855
Condition Number......Page 856
3D Tracking Error Modeling......Page 857
Influence of System Configuration on Tracking Errors......Page 858
Combined Constraints for Image Feature Location......Page 859
Tracking Implementation......Page 860
Nonsingular Constraints for Tracking......Page 861
Optimal Targeting Area......Page 862
References......Page 863
Introduction......Page 865
Gabor Filter-Based Feature Extraction......Page 866
Matching......Page 867
Tracking......Page 869
Dancer Matching and Tracking......Page 871
Stereo (3D) Image Generation......Page 873
References......Page 874
Introduction......Page 876
Image Jacobian......Page 877
Estimation of the Jacobian......Page 878
Reliability Estimation......Page 880
Adding the Epipolar Constraint......Page 881
Glossary of Tested Algorithms......Page 882
Experimental Setup......Page 883
Evaluation Indices......Page 884
Results......Page 885
References......Page 886
Introduction......Page 888
The Basic Trust Region Algorithm......Page 889
QP_TR an Improved Trust Region Method......Page 890
Target Model......Page 891
Scale-Space Blob......Page 893
Examples......Page 895
Reference......Page 898
Introduction......Page 900
Related Work......Page 901
Problem Formulation......Page 902
Inference Algorithm......Page 904
Potential Functions......Page 905
Nonparametric Implementation......Page 906
Preprocess......Page 907
Posture Estimation Under Partial Self-occlusion......Page 908
Articulated Human Tracking......Page 909
Conclusion......Page 910
Introduction......Page 912
Preliminaries......Page 913
Locally Linear Embedding for Shape Analysis......Page 914
Curve Evolution......Page 915
The State Space Model......Page 916
Time Update......Page 917
Measurement Update......Page 918
Setting the Importance Weights......Page 919
Shark Sequence......Page 920
Soccer Sequence......Page 921
Conclusions and Limitations......Page 922
Introduction......Page 924
The Segmentation Process......Page 925
Tracking of Moving Objects......Page 927
Track Setup......Page 928
Track Update......Page 929
Constructing the N-Ary Tree Classifier......Page 930
Clustering the Data......Page 932
Experimental Results......Page 933
Conclusions......Page 934
References......Page 935
Introduction......Page 936
Previous Work......Page 937
From Features to Objects......Page 939
Occlusion Handling......Page 940
Second Order Statistics......Page 941
Probability Estimation......Page 942
Experimental Results......Page 943
Conclusion and Future Work......Page 946
Introduction......Page 948
Particle Filtering......Page 949
Face Detection Using Haar-Like Features......Page 951
"CamShift" Algorithm......Page 952
Dynamic Model......Page 953
Observation Likelihood......Page 954
Experimental Results......Page 956
Conclusions and Future Work......Page 957
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