Computer vision in control systems-6

Preface......Page 6
Contents......Page 7
About the Editors......Page 11
 1.1 Introduction......Page 13
 1.2 Chapters in the Book......Page 14
 References......Page 17
 2.1 Introduction......Page 19
 2.2 Shift Algorithm Based on Discrete Chebyshev Transformation......Page 20
 2.3 Position Estimation in Noisy Images......Page 23
 2.4 Analyze of Autocorrelation Function......Page 25
 2.5 Shift’s Estimation by Using Discriminator......Page 26
  2.5.1 Discriminator Structure......Page 27
  2.5.2 Distribution Law of Estimation......Page 30
  2.5.3 Robust Estimate of Signal Parameter......Page 32
 2.6 Conclusions......Page 34
 References......Page 35
 3.1 Introduction......Page 36
 3.2 The Model of One-Dimensional Energy-Phase Spectrum......Page 37
 3.3 The Model of Two-Dimensional Phase-Energy Spectrum......Page 42
 3.4 Conclusions......Page 46
 References......Page 47
 4.1 Introduction......Page 49
 4.2 The Primitive Detectors Field......Page 50
 4.3 Drift of the Detectors Field......Page 52
 4.4 Two-Dimensional Discrete Filtering of Detectors Fields for Output Signals......Page 53
 4.5 Experimental Studies......Page 57
 4.6 Using Detectors Field Filtering in Images Affected by Motion Blur......Page 58
 4.7 Conclusions......Page 61
 References......Page 62
 5.1 Introduction......Page 63
 5.2 Target Motion Models......Page 64
 5.3 Trajectory Filtration Algorithms......Page 66
 5.4 Body-Fixed Frame......Page 67
 5.5 Comparative Analysis of Filtration Efficiency......Page 69
 5.6 Conclusions......Page 71
 References......Page 72
 6.1 Introduction......Page 73
 6.2 Related Work......Page 74
 6.3 Watermarking Model of Videos in Uncompressed Domain......Page 75
 6.4 Watermarking Models of Videos in Compressed Domain......Page 77
  6.4.1 Watermarking Schemes for Compressed Video Sequences......Page 78
  6.4.2 Watermarking Models for Three Strategies......Page 80
 6.5 Basic Requirements for Watermarking Schemes......Page 81
 References......Page 85
 7.1 Introduction......Page 87
 7.2 Related Work......Page 88
 7.3 Camera Traps Data......Page 89
 7.4 Proposed Software System......Page 91
  7.4.1 Module of Data Management......Page 92
  7.4.2 Module of Preliminary Analysis......Page 93
  7.4.3 Module of Image Enhancement......Page 95
  7.4.4 Module of Animal Detection......Page 96
  7.4.5 Module of CNN Control......Page 97
  7.4.6 Module of Semantic Description......Page 98
 7.5 Conclusions......Page 100
 References......Page 101
 8.1 Introduction......Page 103
 8.2 Related Work......Page 104
  8.3.1 The Idea of a Two-Stage Approach......Page 106
  8.3.3 Binary Classification Based on Global Features......Page 108
  8.3.4 Segmentation Based on CNN......Page 110
 8.4 Experimental Studies......Page 111
 8.5 Conclusions......Page 114
 References......Page 115
9 Algorithms for Markers Detection on Facies Images of Human Biological Fluids in Medical Diagnostics......Page 117
 9.1 Introduction......Page 118
 9.2 The Examples of Images of Biological Liquids Facies......Page 119
 9.3 The Image Preprocessing......Page 120
 9.4 Algorithms for Markers Detection and Recognition......Page 127
 9.5 Statistical Tests of Algorithms......Page 133
 References......Page 134
10 An Investigation of Research Activities in Intelligent Data Processing Using Data Envelopment Analysis......Page 136
 10.1 Introduction......Page 137
 10.2 The Foresight of Impending Smart Infrastructure from the Position of Pervasive Informatics......Page 138
 10.3 Data Envelopment Analysis Background......Page 140
 10.4 System Integration of Research Activities in Geosocial Networking Using Data Envelopment Analysis......Page 143
 10.5 Conclusions......Page 145
 References......Page 146
11 Hybrid Optimization Modeling Framework for Research Activities in Intelligent Data Processing......Page 149
 11.1 Introduction......Page 150
 11.2 Intelligent Data Processing and Object-Based Image Analysis......Page 151
  11.3.1 Functionality of Hybrid Optimization Modeling Framework......Page 154
  11.3.2 Experimental Studies......Page 156
 11.4 Conclusions......Page 157
 References......Page 158
12 Non-local Means Denoising Algorithm Based on Local Binary Patterns......Page 161
 12.1 Introduction......Page 162
 12.2 Related Work......Page 163
 12.3 Description of Non-local Means Algorithm......Page 164
 12.4 Modified Non-local Means Algorithm......Page 166
 12.5 Non-local Means Based on Local Binary Patterns......Page 168
 12.6 Experimental Studies......Page 169
 12.7 Conclusions......Page 170
 References......Page 171
 13.1 Introduction......Page 173
 13.2 Robot Construction......Page 174
  13.3.1 Data Acquisition and Synchronization......Page 175
  13.3.2 Determining the Location of the Mobile Platform......Page 176
  13.3.3 Construction of Three-Dimensional Map......Page 178
 13.4 Results......Page 180
 13.5 Conclusions......Page 181
 References......Page 183