Advances in Intelligent Information Processing: Tools and Applications (Statistical Science and Interdisciplinary Research, Vol. 2)

Contents......Page 13
Foreword......Page 7
Preface......Page 9
1.1 Introduction......Page 20
1.2 Need for Modelling Class Distributions Non-parametrically......Page 22
1.3 NP-windows Method for Non-parametric Estimation of PDFs......Page 23
1.4 NPMM-ICLS Framework......Page 26
1.5 Level Sets Method......Page 28
1.6 NPMM-ICLS Level Sets Method......Page 29
1.7 Results and Discussion......Page 30
1.8 Conclusions......Page 34
Bibliography......Page 35
2.1 Introduction......Page 38
2.2.1 Level set model of curve evolution......Page 40
2.2.2 Reaction-diffusion model......Page 41
2.2.3 Shape optimization......Page 43
2.3.1 Reaction-diffusion influenced curve evolution......Page 45
2.3.2 Shape optimization based curve evolution......Page 47
2.4 Results......Page 49
2.4.1 Pattern disocclusion......Page 52
2.5 Conclusions......Page 53
Bibliography......Page 55
3.1 Active Contours: Introduction......Page 56
3.2 Curve Evolution......Page 58
3.3 Difficulties with Parametric Curves......Page 59
3.5 Proposed Method......Page 61
3.5.1 Comparison with other works......Page 63
3.5.2 Choice of the ideal constant K......Page 64
3.5.3 Proof of conditional boundedness......Page 65
3.6 Applications in Image Segmentation and Tracking......Page 67
3.8 Results......Page 68
3.9 Conclusions and FutureWork......Page 71
Bibliography......Page 72
4. Information Theoretic Approaches for Next Best View Planning in Active Computer Vision C. Derichs, B. Deutsch, S. Wenhardt, H. Niemann and J. Denzler......Page 74
4.1 Introduction......Page 75
4.2 Information Theoretical Approaches for Next Best View Planning......Page 76
4.2.1 General state modeling and estimation......Page 77
4.2.2 Optimality criteria for active view planning......Page 78
4.3.1.1 State representation and information fusion......Page 81
4.3.1.2 Optimal action selection......Page 82
4.3.2.1 State and observation representation......Page 85
4.3.2.2 Optimal action selection......Page 87
4.3.2.3 Visibility......Page 88
4.3.3 Active object reconstruction......Page 90
4.3.3.2 Optimal action selection......Page 91
4.4.1 Evaluation for active object recognition......Page 93
4.4.2 Evaluation for active object tracking......Page 94
4.4.3 Evaluation for active object reconstruction......Page 97
4.4.3.1 Reconstructing a calibration pattern......Page 98
4.4.3.2 Reconstructing a mouse pad......Page 99
Bibliography......Page 100
5.1 Introduction......Page 104
5.2 Linear Combination of Views......Page 106
5.2.1 Image synthesis......Page 107
5.3 The Recognition System......Page 108
5.3.1 Template matching......Page 109
5.3.2 Optimisation......Page 110
5.4 Experimental Results......Page 113
5.4.1 Experiments on the CMU PIE database......Page 114
5.4.2 Experiments on the COIL-20 database......Page 118
Bibliography......Page 122
6.1 Introduction......Page 126
6.2.1 Video data clustering......Page 128
6.2.2 The perceptual grouping model......Page 129
6.3 Object Model as a Pictorial Structure......Page 132
6.3.2 Formulation of appearance parameters for object parts......Page 133
6.4.1 Formulation of object model evidence......Page 136
6.4.2 The grouping algorithm......Page 137
6.5 Results......Page 139
Bibliography......Page 142
7. Image Representations Based on Discriminant Non-negative Matrix Factorization I. Buciu and I. Pitas......Page 144
7.1 Introduction......Page 145
7.2 Bregman Distance, Kullback-Leibler Divergence and Non-negative Matrix Factorization......Page 147
7.3 Local Non-negative Matrix Factorization......Page 149
7.4 Discriminant Non-negative Matrix Factorization......Page 150
7.5.1 Data description......Page 152
7.5.3 Feature extraction and image representation......Page 153
7.6 Performance Evaluation and Discussions......Page 158
7.7 Conclusion......Page 162
Bibliography......Page 164
8.1 Introduction......Page 168
8.2 Related Work......Page 171
8.3 System Overview......Page 172
8.3.1 CFMT descriptor for images......Page 174
8.3.2 CFMT extraction for arbitrarily shaped regions......Page 175
8.4 Experimental Results......Page 176
8.4.2 Results on web image database......Page 177
8.4.3 Time performance......Page 179
8.4.4 Results on MM270K image database......Page 180
Bibliography......Page 184
9. Unsupervised Change Detection Techniques Based on Self-Organizing Feature Map Neural Network S. Patra, S. Ghosh and A. Ghosh......Page 186
9.1 Introduction......Page 187
9.2 Kohonen’s Model of Self-Organizing Feature Map......Page 189
9.3 Proposed Change Detection Techniques......Page 190
9.3.1 Change detection based on 1D-SOFM......Page 191
9.3.2 Change detection based on 2D-SOFM......Page 192
9.3.2.1 Learning the weights......Page 193
9.3.2.2 Proposed threshold selection techniques......Page 194
9.4.1 Data set related to Mexico area......Page 198
9.4.2 Data set related to Sardinia Island, Italy......Page 199
9.5.1 Description of experiments......Page 200
9.5.2 Results on Mexico data......Page 202
9.5.3 Results on Sardinia Island data......Page 204
9.6 Discussion and Conclusion......Page 206
Bibliography......Page 208
10.1 Introduction and Overview of Video Coding Standards......Page 210
10.2 Distributed Video Coding......Page 212
10.2.1 Wyner-Ziv video coding......Page 214
10.2.2 Rate distortion analysis......Page 215
10.2.3 Backward channel aware Wyner-Ziv video coding......Page 216
10.3 Texture-Based Video Coding......Page 218
10.3.1 Spatial texture models......Page 220
10.3.2 Temporal qnalysis......Page 221
10.3.3 A new perspective on texture-based video coding......Page 222
10.4 Scalable Video Coding......Page 223
10.4.2 Spatial scalability......Page 224
10.5 Multi-View Coding......Page 225
Bibliography......Page 227
11. Hardware Architecture for Ridge Extraction in Fingerprints A. Bishnu, P. Bhowmick, J. Dey, B. B. Bhattacharya, M. K. Kundu, C. A. Murthy, and T. Acharya......Page 232
11.1 Introduction......Page 233
11.2.1 Theme......Page 236
11.2.2 Combinatorial possibilities......Page 237
11.2.3 Implementation details......Page 239
11.2.4.1 Preliminary classification......Page 241
11.2.4.2 Final classification......Page 243
11.2.6 Algorithm......Page 244
11.3.1 Evaluation criteria for ridge/valley finding algorithms......Page 246
11.3.2 Results......Page 247
11.4.2 Hardware architecture......Page 252
11.4.4 Circuit cost......Page 258
Bibliography......Page 259
12.1 Introduction......Page 262
12.2.1 Bio-basis function......Page 265
12.2.2 Rough sets......Page 267
12.3 Rough-Fuzzy C-Medoids Algorithm......Page 268
12.3.2 Fuzzy C-medoids......Page 269
12.3.3 Rough C-medoids......Page 270
12.3.4 Rough-fuzzy C-medoids......Page 272
12.3.5 Selection of initial bio-basis......Page 274
12.4 Quantitative Measure......Page 275
12.4.1 Using homology alignment score......Page 276
12.4.2 Using mutual information......Page 277
12.5.1.1 Five whole HIV protein sequences......Page 278
12.5.1.2 Cai-Chou HIV data set......Page 279
12.5.2 Example......Page 280
12.5.3.1 Optimum value of parameter  3......Page 281
12.5.3.2 Random versus DOR based initialization......Page 282
12.5.3.3 Optimum values of parameters  m, w, and......Page 283
12.5.3.4 Comparative performance of different algorithms......Page 287
12.6 Conclusion......Page 292
13.1 Introduction......Page 296
13.2.1 Performance index......Page 299
13.2.2 Optimization of the level of knowledge reuse......Page 300
13.2.3 Refinement of knowledge reuse......Page 302
13.3 Fuzzy Rule-based Models and Their Experience-consistent Design......Page 303
13.4 The Alignment of Information Granules......Page 307
13.5 Granular Characterization of Experience-consistent Rule-based Models......Page 308
Bibliography......Page 311
Index......Page 314