Brain-inspired Intelligenceand Visual Perception：The Brain and Machine Eyes（类脑智能与视觉感知）

In the 19th century, Spanish anatomists founded the theory of neurons. With the development of brain science, the biological characteristics of neurons and related electrical properties have been discovered. The advent of mathematical methods to simulate the actual human neural network in 1943 can be recognized as one of the notable landmarks. 63 years since then, deep neural n...

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In the 19th century, Spanish anatomists founded the theory of neurons. With the development of brain science, the biological characteristics of neurons and related electrical properties have been discovered. The advent of mathematical methods to simulate the actual human neural network in 1943 can be recognized as one of the notable landmarks. 63 years since then, deep neural networks were proposed and developed to simulate the structure of the human cerebral cortex. The emergence of deep learning has a great influence on the traditional artificial intelligence and enhanced the importance of brain-inspired intelligence in the whole field of artificial intelligence. This is a great dream into reality!

Wenfeng Wang is currently the leader of a CAS “Light of West China” Program (XBBS-2014-16) and has been invited as the director of the Institute of Artificial Intelligence, the College of Brain-inspired Intelligence, Chinese Academy of Sciences (to be set up in Nov. 2017). He also serves as a Distinguished Professor and the academic director of the R&D and Promotion center of a...

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Wenfeng Wang is currently the leader of a CAS “Light of West China” Program (XBBS-2014-16) and has been invited as the director of the Institute of Artificial Intelligence, the College of Brain-inspired Intelligence, Chinese Academy of Sciences (to be set up in Nov. 2017). He also serves as a Distinguished Professor and the academic director of the R&D and Promotion center of artificial intelligence in the Robot Group of Harbin Institute of Technology, Hefei, China. His major research interests include functional analysis and intelligent algorithms with applications to video surveillance, ecologic modelling, geographic data mining and etc. He is the editor in chief of the book COMPUTER VISION AND MACHINE COGNITION (in Chinese), which has been published by Beihang University in China. Wenfeng Wang is enthusiastic in academic communications in any way and he served as PC members and Session chairs of a series of international conferences associated with the brain-inspired intelligence and visual cognition, including the 2017 IEEE International Conference on Advanced Robotics and Mechatronics, the 2017 International Conference on Information Science, Control Engineering and the 3rd International Conference on Cognitive Systems and Information Processing and etc. Xiangyang Deng is currently a full assistant professor with the Institute of Information Fusion, Naval Aeronautical University, Yantai, China. His current research interests include video big data, deep learning and computational intelligence. Xiangyang Deng has rich experience in R & D management. He won 3 First Class Prizes and 2 Third Class Prizes of Military Scientific and Technological Progress Award. He published 9 papers about the topics in the past 3 years while 5 of them were indexed by SCI, EI. He contributed to a monograph SWARM INTELLIGENCE AND APPLICATIONS (in Chinese), which was published by National Defense Industry Press. He has 2 patents and obtained 3 items of software copyright. Liang Ding is currently a full Professor with the State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China. His current research interests include intelligent control and robotics, including planetary rovers and legged robots. Dr. Ding was a recipient of the 2017 ISTVS Söhne-Hata-Jurecka Award, the 2011 National Award for Technological Invention of China and the 2009/2013/2015 Award for Technological Invention of Heilongjiang Province. He received the Hiwin Excellent Doctoral Dissertation Award, the Best Conference Paper Award of IEEE ARM, and the Best Paper in Information Award of the 2012 IEEE ICIA Conference. Liang Ding is an influential scientist in intelligent control of robots and has published more than 120 authored or co-authored papers in journals and conference proceedings. Limin Zhang is currently a Full Professor and Tutor for Doctor with the Institute of Information Fusion, Naval Aeronautical University, Yantai, Shangdong, China. He was a senior visiting scholar at university college london (UCL) Modern Space Analysis and Research Center (CASA) from 2006 to 2007. His current research interests include signal processing, Complex system simulation and computational intelligence. More than 180 papers are published and 80 papers are indexed by SCI, EI. 2 monographs are published and 20 patents are applied and 6 were authorized. Limin Zhang has won two Second Class Prizes of National Scientific and Technological Progress Award and five First Class Prizes of Military Scientific and Technological Progress Award. He has been selected as outstanding scientists in national science and technology and millions of talents in engineering research field and he is enjoying special allowance from the State Council.

1 Introduction of Brain Cognition /1
1.1Background/1
1.2TheoryandMechanisms /2
1.2.1 Brain Mechanisms to DetermineAttentionValue of Information in the Video / 3
1.2.2 Swarm Intelligence to Implement theAbove Biological
Mechanisms/4
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1 Introduction of Brain Cognition /1
1.1Background/1
1.2TheoryandMechanisms /2
1.2.1 Brain Mechanisms to DetermineAttentionValue of Information in the Video / 3
1.2.2 Swarm Intelligence to Implement theAbove Biological
Mechanisms/4
1.2.3 Models Framework for Social Computingin Object
Detection /5
1.2.4 Swarm Optimization and Classificationof the Target
Impulse Responses /5
1.2.5 Performance of Integration Models ona Series of Challenging Real Data / 6
1.3FromDetectiontoTracking/ 7
1.3.1 Brain Mechanisms for Select ImportantObjects to Track/8
1.3.2 Mechanisms for Motion Tracking byBrain-Inspired
Robots /9
1.3.3 Sketch of Algorithms to ImplementBiological
Mechanisms in the Model /10
1.3.4 Model Framework of the Brain-InspiredCompressive
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 MannedDriving /19
2.2.2 Attention Mechanisms in UnmannedDriving / 20
2.2.3 Implications to the Accuracy ofCognition /21
2.2.4 Implications to the Speed ofResponse/21
2.2.5 Future Treatment of RegulatedAttention /22
2.3 Locally Compressive Cognition/ 23
2.3.1 Construction of a CompressiveAttention /24
2.3.2 Locating Centroid of a Region ofInterest /25
2.3.3 Parameters and Classifiers of theCognitive System/25
2.3.4 Treating Noise Data in the CognitionProcess/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 ColonyOptimization / 43
3.2.1 Model of Ants’ Moving Environment /44
3.2.2 Ant Colony System: A ClassicalModel/44
3.2.3 The Pheromone Modification Strategy/46
3.2.4 Adaptive Adjustment of InvolvedSub-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 withACS / 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’sEvolutionary Phases /55
3.5.1 Colony Diversity Coefficient/ 55
3.5.2 Elitist Individual PersistenceCoefficient /56
3.6 Experimental Tests of PM-ACO /56
3.6.1 Tests in Problems Which HaveDifferent 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 theVision–Brain Hypothesis / 59
3.7.1 Scene Understanding and Partition/59
3.7.2 Efficiency of the Vision–Brain inFace Recognition /63
References / 67
4 Neural Cognitive Computing Mechanisms /69
4.1 Background /69
4.2 The Full State Constrained WheeledMobile Robotic System / 71
4.2.1 System Description/ 71
4.2.2 Useful Technical Lemmas andAssumptions/ 72
4.2.3 NN Approximation /73
4.3 The Controller Design and TheoreticalAnalyses / 74
4.3.1 Controller Design / 74
4.3.2 Theoretic Analyses of the SystemStability /78
4.4 Validation of the Nonlinear WMR System/ 81
4.4.1 Modeling Description of the NonlinearWMR System/81
4.4.2 Evaluating Performance of theNonlinear
WMR System /81
4.5 System Improvement by ReinforcedLearning/85
4.5.1 Scheme to Enhance the Wheeled MobileRobotic
System /85
4.5.2 Strategic Utility Function and CriticNN Design /89
4.6 Stability Analysis of the Enhanced WMRSystem/91
4.6.1 Action NN Design Under the AdaptiveLaw/ 91
4.6.2 Boundedness Approach and the TrackingErrors
Convergence/92
4.6.3 Simulation and Discussion of the WMRSystem/ 96
References / 99
5 Integration and Scheduling of CoreModules/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 Mechanicsof Rovers/131
5.4.2 The Future Research Priorities /135
References / 136
6 Brain-Inspired Perception, Motion andControl/143
6.1 Background / 143
6.2 Formulation of the PerceptiveInformation / 145
6.2.1 Visual Signals in CorticalInformation Processing
Pathways /145
6.2.2 Formulation of Cognition in theVision–Brain/146
6.3 A Conceptual Model to EvaluateCognition Efficiency /147
6.3.1 Computation of Attention Value andWarning Levels/ 147
6.3.2 Detailed Analysis on the TimeSequence Complexity / 151
6.4 From Perception to Cognition andDecision / 155
6.4.1 Brain-Inspired Motion and Control ofRobotic
Systems /155
6.4.2 Layer Fusion of Sensors, Feature andKnowledge / 155
6.5 The Major Principles to Implement aReal Brain Cognition/158
6.5.1 Intelligence Extremes of the RoboticVision–Brain /158
6.5.2 Necessity to Set an up Limit for theRobotic
Intelligence / 159
References / 161
Index /165
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