类脑智能与视觉感知 王文峰 等 著 专业科技 文轩网

1 Introduction of Brain Cognition
1．1 Background
1．2 Theory and Mechanisms
1．2．1 Brain Mechanisms to Determine Attention Value of Information in the Video
1．2．2 Swarm Intelligence to Implement the Above Biological Mechanisms
1．2．3 Models Framework for Social Computing in Object Detection
1．2．4 Swarm Optimization and Classification of the Target Impulse Responses
1．2．5 Performance of Integration Models on a Series of Challenging Real Data
1．3 From Detection to Tracking
1．3．1 Brain Mechanisms for Select Important Objects to Track
1．3．2 Mechanisms for Motion Tracking by Brain-Inspired Robots
1．3．3 Sketch of Algorithms to Implement Biological Mechanisms in the Model
1．3．4 Model Framework of the Brain-Inspired Compressive Tracking and Future Applications
1．4 Objectives and Contributions
1．5 Outline of the Book
References
2 The Vision-Brain Hypothesis
2．1 Background
2．2 Attention Mechanisms
2．2．1 Attention Mechanisms in Manned Driving
2．2．2 Attention Mechanisms in Unmanned Driving
2．2．3 Implications to the Accuracy of Cognition
2．2．4 Implications to the Speed of Response
2．2．5 Future Treatment of Regulated Attention
2．3 Locally Compressive Cognition
2．3．1 Construction of a Compressive Attention
2．3．2 Locating Centroid of a Region of Interest
2．3．3 Parameters and Classifiers of the Cognitive System
2．3．4 Treating Noise Data in the Cognition Process
2．4 An Example of the Vision-Brain
2．4．1 Illustration of the Cognitive System
2．4．2 Definition of a Vision-Brain
2．4．3 Implementation of the Vision-Brain
References
3 Pheromone Accumulation and Iteration
3．1 Background
3．2 Improving the Classical Ant Colony Optimization
3．2．1 Model of Ants' Moving Environment
3．2．2 Ant Colony System： A Classical Model
3．2．3 The Pheromone Modification Strategy
3．2．4 Adaptive Adjustment of Involved Sub-paths
3．3 Experiment Tests of the SPB-ACO
3．3．1 Test of SPB Rule
3．3．2 Test of Comparing the SPB-ACO with ACS
3．4 ACO Algorithm with Pheromone Marks
3．4．1 The Discussed Background Problem
3．4．2 The Basic Model of PM-ACO
3．4．3 The Improvement of PM-ACO
3．5 Two Coefficients of Ant Colony's Evolutionary Phases
3．5．1 Colony Diversity Coefficient
3．5．2 Elitist Individual Persistence Coefficient
3．6 Experimental Tests of PM-ACO
3．6．1 Tests in Problems Which Have Different Nodes
3．6．2 Relationship Between CDC and EIPC
3．6．3 Tests About the Best-Ranked Nodes
3．7 Further Applications of the Vision-Brain Hypothesis
3．7．1 Scene Understanding and Partition
3．7．2 Efficiency of the Vision-Brain in Face Recognition
References
4 Neural Cognitive Computing Mechanisms
4．1 Background
4．2 The Full State Constrained Wheeled Mobile Robotic System
4．2．1 System Description
4．2．2 Useful Technical Lemmas and Assumptions
4．2．3 NN Approximation
4．3 The Controller Design and Theoretical Analyses
4．3．1 Controller Design
4．3．2 Theoretic Analyses of the System Stability
4．4 Validation of the Nonlinear WMR System
4．4．1 Modeling Description of the Nonlinear WMR System
4．4．2 Evaluating Performance of the Nonlinear WMR System
4．5 System Improvement by Reinforced Learning
4．5．1 Scheme to Enhance the Wheeled Mobile Robotic System
4．5．2 Strategic Utility Function and Critic NN Design
4．6 Stability Analysis of the Enhanced WMR System
4．6．1 Action NN Design Under the Adaptive Law
4．6．2 Boundedness Approach and the Tracking Errors Convergence
4．6．3 Simulation and Discussion of the WMR System
References
5 Integration and Scheduling of Core Modules
5．1 Background
5．2 Theoretical Analyses
5．2．1 Preliminary Formulation
5．2．2 Three-Layer Architecture
5．3 Simulation and Discussion
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