Brain-Inspired Intelligence and Visual Perception

1 Introduction of Brain Cognition /1 1.1Background/1 1.2TheoryandMechanisms /2 1.2.1 Brain Mechanisms to Determine AttentionValue of Information in the Video / 3 1.2.2 Swarm Intelligence to Implement the Above Biological Mechanisms/4 1.2.3 Models Framework for Social Computing in Object Detection /5 1.2.4 Swarm Optimization and Classification of the Target Impulse Responses /5 1.2.5 Performance of Integration Models on a Series of Challenging Real Data / 6 1.3FromDetectiontoTracking/ 7 1.3.1 Brain Mechanisms for Select Important Objects to Track/8 1.3.2 Mechanisms for Motion Tracking by Brain-Inspired Robots /9 1.3.3 Sketch of Algorithms to Implement Biological Mechanisms in the Model /10 1.3.4 Model Framework of the Brain-Inspired Compressive Tracking and Future Applications /11 1.4Objectivesand Contributions / 12 1.5 Outline of the Book /13 1.6 References / 15 2 The Vision–Brain Hypothesis/17 2.1 Background / 17 2.2 Attention Mechanisms/19 2.2.1 Attention Mechanisms in Manned Driving /19 2.2.2 Attention Mechanisms in Unmanned Driving / 20 2.2.3 Implications to the Accuracy of Cognition /21 2.2.4 Implications to the Speed of Response/21 2.2.5 Future Treatment of Regulated Attention /22 2.3 Locally Compressive Cognition/ 23 2.3.1 Construction of a Compressive Attention /24 2.3.2 Locating Centroid of a Region of Interest /25 2.3.3 Parameters and Classifiers of the Cognitive System/25 2.3.4 Treating Noise Data in the Cognition Process/26 2.4 An Example of the Vision–Brain / 27 2.4.1 Illustration of the Cognitive System /29 2.4.2 Definition of a Vision–Brain / 31 2.4.3 Implementation of the Vision–Brain/32 References/ 34 3 Pheromone Accumulation and Iteration / 41 3.1 Background /41 3.2 Improving the Classical Ant Colony Optimization / 43 3.2.1 Model of Ants’ Moving Environment /44 3.2.2 Ant Colony System: A Classical Model/44 3.2.3 The Pheromone Modification Strategy /46 3.2.4 Adaptive Adjustment of Involved Sub-paths /47 3.3 Experiment Tests of the SPB-ACO / 48 3.3.1 Test of SPB Rule / 48 3.3.2 Test of Comparing the SPB-ACO with ACS / 51 3.4 ACO Algorithm with Pheromone Marks/52 3.4.1 The Discussed Background Problem/52 3.4.2 The Basic Model of PM-ACO /53 3.4.3 The Improvement of PM-ACO/54 3.5 Two Coefficients of Ant Colony’s Evolutionary Phases /55 3.5.1 Colony Diversity Coefficient/ 55 3.5.2 Elitist Individual Persistence Coefficient /56 3.6 Experimental Tests of PM-ACO /56 3.6.1 Tests in Problems Which Have Different Nodes / 57 3.6.2 Relationship Between CDC and EIPC /57 3.6.3 Tests About the Best-Ranked Nodes/58 3.7 Further Applications of the Vision–Brain Hypothesis / 59 3.7.1 Scene Understanding and Partition/59 3.7.2 Efficiency of the Vision–Brain in Face Recognition /63 References / 67 4 Neural Cognitive Computing Mechanisms / 69 4.1 Background /69 4.2 The Full State Constrained Wheeled Mobile Robotic System / 71 4.2.1 System Description/ 71 4.2.2 Useful Technical Lemmas and Assumptions/ 72 4.2.3 NN Approximation /73 4.3 The Controller Design and Theoretical Analyses / 74 4.3.1 Controller Design / 74 4.3.2 Theoretic Analyses of the System Stability /78 4.4 Validation of the Nonlinear WMR System / 81 4.4.1 Modeling Description of the Nonlinear WMR System/81 4.4.2 Evaluating Performance of the Nonlinear WMR System /81 4.5 System Improvement by Reinforced Learning/85 4.5.1 Scheme to Enhance the Wheeled Mobile Robotic System /85 4.5.2 Strategic Utility Function and Critic NN Design /89 4.6 Stability Analysis of the Enhanced WMR System/91 4.6.1 Action NN Design Under the Adaptive Law/ 91 4.6.2 Boundedness Approach and the Tracking Errors Convergence/92 4.6.3 Simulation and Discussion of the WMR System/ 96 References / 99 5 Integration and Scheduling of Core Modules/105 5.1 Background / 105 5.2 Theoretical Analyses /106 5.2.1 Preliminary Formulation/ 106 5.2.2 Three-Layer Architecture /109 5.3 Simulation and Discussion/114 5.3.1 Brain-Inspired Cognition /114 5.3.2 Integrated Intelligence/ 119 5.3.3 Geospatial Visualization / 126 5.4 The Future Research Priorities / 131 5.4.1 Wheel–Terrain Interaction Mechanics of Rovers/131 5.4.2 The Future Research Priorities /135 References / 136 6 Brain-Inspired Perception, Motion and Control/143 6.1 Background / 143 6.2 Formulation of the Perceptive Information / 145 6.2.1 Visual Signals in Cortical Information Processing Pathways /145 6.2.2 Formulation of Cognition in the Vision–Brain/146 6.3 A Conceptual Model to Evaluate Cognition Efficiency /147 6.3.1 Computation of Attention Value and Warning Levels/ 147 6.3.2 Detailed Analysis on the Time Sequence Complexity / 151 6.4 From Perception to Cognition and Decision / 155 6.4.1 Brain-Inspired Motion and Control of Robotic Systems /155 6.4.2 Layer Fusion of Sensors, Feature and Knowledge / 155 6.5 The Major Principles to Implement a Real Brain Cognition/158 6.5.1 Intelligence Extremes of the Robotic Vision–Brain /158 6.5.2 Necessity to Set an up Limit for the Robotic Intelligence / 159 References / 161 Index /165