类脑智能与视觉感知(英文版)(精)
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作者王文峰
出版社华中科技大学
ISBN9787568050791
出版时间2019-03
装帧其他
开本其他
定价96元
货号30606851
上书时间2024-06-07
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作者简介
王文峰，中国科学院西部之光学者，中国自动化学会认知计算与系统专委会和中国人工智能学会认知系统与信息处理专委会委员，CSIP2016和ICISCE2017委员，SCI期刊PJOEs审稿专家，国家自然科学基金项目评审专家，CV-MATH发起人。
目录
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
5
内容摘要
本书主要介绍关于大脑启发的智力和视觉感知（BIVP）研究的最新成果。著者们收集BIVP的新的研究假设，包括介绍脑科学和视觉脑假说。此外，本文还介绍了BIVP的理论和算法，包括信息素积累与迭代、神经认知计算机制、核心模块的集成与调度、大脑感知、运动与控制。
这本书不仅包含基本概念、理论和规律，以前分散在许多有影响的期刊和会议上，而且包含作者的项目中的一系列最新研究进展，包括中国科学院的“西部之光”计划（XBBS-2014-16），国家自然科学院中国科学基金会（41571299）、中国“千人计划”（Y47161）、国家基础研究计划（2013CB035502）和国家高技术研究发展计划（2013AA122302）。
本书即可以供大学研究人员、研发工程师、本科生和研究生学习以及对机器人、脑认知或计算机视觉感兴趣的读者参考。
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