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E-grāmata: Brain-Inspired Intelligence and Visual Perception: The Brain and Machine Eyes

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This book presents the latest findings in the field of brain-inspired intelligence and visual perception (BIVP), and discusses novel research assumptions, including an introduction to brain science and the brain vision hypotheses. Moreover, it introduces readers to the theory and algorithms of BIVP – such as pheromone accumulation and iteration, neural cognitive computing mechanisms, the integration and scheduling of core modules, and brain-inspired perception, motion and control – in a step-by-step manner. Accordingly, it will appeal to university researchers, R&D engineers, undergraduate and graduate students; to anyone interested in robots, brain cognition or computer vision; and to all those wishing to learn about the core theory, principles, methods, algorithms, and applications of BIVP. 

1 Introduction of Brain Cognition 1(16)
1.1 Background
1(1)
1.2 Theory and Mechanisms
2(5)
1.2.1 Brain Mechanisms to Determine Attention Value of Information in the Video
3(1)
1.2.2 Swarm Intelligence to Implement the Above Biological Mechanisms
4(1)
1.2.3 Models Framework for Social Computing in Object Detection
5(1)
1.2.4 Swarm Optimization and Classification of the Target Impulse Responses
5(1)
1.2.5 Performance of Integration Models on a Series of Challenging Real Data
6(1)
1.3 From Detection to Tracking
7(5)
1.3.1 Brain Mechanisms for Select Important Objects to Track
8(1)
1.3.2 Mechanisms for Motion Tracking by Brain-Inspired Robots
9(1)
1.3.3 Sketch of Algorithms to Implement Biological Mechanisms in the Model
10(1)
1.3.4 Model Framework of the Brain-Inspired Compressive Tracking and Future Applications
11(1)
1.4 Objectives and Contributions
12(1)
1.5 Outline of the Book
13(2)
References
15(2)
2 The Vision-Brain Hypothesis 17(24)
2.1 Background
17(2)
2.2 Attention Mechanisms
19(4)
2.2.1 Attention Mechanisms in Manned Driving
19(1)
2.2.2 Attention Mechanisms in Unmanned Driving
20(1)
2.2.3 Implications to the Accuracy of Cognition
21(1)
2.2.4 Implications to the Speed of Response
21(1)
2.2.5 Future Treatment of Regulated Attention
22(1)
2.3 Locally Compressive Cognition
23(4)
2.3.1 Construction of a Compressive Attention
24(1)
2.3.2 Locating Centroid of a Region of Interest
25(1)
2.3.3 Parameters and Classifiers of the Cognitive System
25(1)
2.3.4 Treating Noise Data in the Cognition Process
26(1)
2.4 An Example of the Vision-Brain
27(7)
2.4.1 Illustration of the Cognitive System
29(2)
2.4.2 Definition of a Vision-Brain
31(1)
2.4.3 Implementation of the Vision-Brain
32(2)
References
34(7)
3 Pheromone Accumulation and Iteration 41(28)
3.1 Background
41(2)
3.2 Improving the Classical Ant Colony Optimization
43(5)
3.2.1 Model of Ants' Moving Environment
44(1)
3.2.2 Ant Colony System: A Classical Model
44(2)
3.2.3 The Pheromone Modification Strategy
46(1)
3.2.4 Adaptive Adjustment of Involved Sub-paths
47(1)
3.3 Experiment Tests of the SPB-ACO
48(4)
3.3.1 Test of SPB Rule
48(3)
3.3.2 Test of Comparing the SPB-ACO with ACS
51(1)
3.4 ACO Algorithm with Pheromone Marks
52(3)
3.4.1 The Discussed Background Problem
52(1)
3.4.2 The Basic Model of PM-ACO
53(1)
3.4.3 The Improvement of PM-ACO
54(1)
3.5 Two Coefficients of Ant Colony's Evolutionary Phases
55(1)
3.5.1 Colony Diversity Coefficient
55(1)
3.5.2 Elitist Individual Persistence Coefficient
56(1)
3.6 Experimental Tests of PM-ACO
56(3)
3.6.1 Tests in Problems Which Have Different Nodes
57(1)
3.6.2 Relationship Between CDC and EIPC
57(1)
3.6.3 Tests About the Best-Ranked Nodes
58(1)
3.7 Further Applications of the Vision-Brain Hypothesis
59(8)
3.7.1 Scene Understanding and Partition
59(4)
3.7.2 Efficiency of the Vision-Brain in Face Recognition
63(4)
References
67(2)
4 Neural Cognitive Computing Mechanisms 69(36)
4.1 Background
69(2)
4.2 The Full State Constrained Wheeled Mobile Robotic System
71(3)
4.2.1 System Description
71(1)
4.2.2 Useful Technical Lemmas and Assumptions
72(1)
4.2.3 NN Approximation
73(1)
4.3 The Controller Design and Theoretical Analyses
74(7)
4.3.1 Controller Design
74(4)
4.3.2 Theoretic Analyses of the System Stability
78(3)
4.4 Validation of the Nonlinear WMR System
81(4)
4.4.1 Modeling Description of the Nonlinear WMR System
81(1)
4.4.2 Evaluating Performance of the Nonlinear WMR System
81(4)
4.5 System Improvement by Reinforced Learning
85(6)
4.5.1 Scheme to Enhance the Wheeled Mobile Robotic System
85(4)
4.5.2 Strategic Utility Function and Critic NN Design
89(2)
4.6 Stability Analysis of the Enhanced WMR System
91(8)
4.6.1 Action NN Design Under the Adaptive Law
91(1)
4.6.2 Boundedness Approach and the Tracking Errors Convergence
92(4)
4.6.3 Simulation and Discussion of the WMR System
96(3)
References
99(6)
5 Integration and Scheduling of Core Modules 105(38)
5.1 Background
105(1)
5.2 Theoretical Analyses
106(8)
5.2.1 Preliminary Formulation
106(3)
5.2.2 Three-Layer Architecture
109(5)
5.3 Simulation and Discussion
114(17)
5.3.1 Brain-Inspired Cognition
114(5)
5.3.2 Integrated Intelligence
119(7)
5.3.3 Geospatial Visualization
126(5)
5.4 The Future Research Priorities
131(5)
5.4.1 Wheel-Terrain Interaction Mechanics of Rovers
131(4)
5.4.2 The Future Research Priorities
135(1)
References
136(7)
6 Brain-Inspired Perception, Motion and Control 143(22)
6.1 Background
143(2)
6.2 Formulation of the Perceptive Information
145(2)
6.2.1 Visual Signals in Cortical Information Processing Pathways
145(1)
6.2.2 Formulation of Cognition in the Vision-Brain
146(1)
6.3 A Conceptual Model to Evaluate Cognition Efficiency
147(8)
6.3.1 Computation of Attention Value and Warning Levels
147(4)
6.3.2 Detailed Analysis on the Time Sequence Complexity
151(4)
6.4 From Perception to Cognition and Decision
155(3)
6.4.1 Brain-Inspired Motion and Control of Robotic Systems
155(1)
6.4.2 Layer Fusion of Sensors, Feature and Knowledge
155(3)
6.5 The Major Principles to Implement a Real Brain Cognition
158(3)
6.5.1 Intelligence Extremes of the Robotic Vision-Brain
158(1)
6.5.2 Necessity to Set an up Limit for the Robotic Intelligence
159(2)
References
161(4)
Index 165
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 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.

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. 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.

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 received several awards and he 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.

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 has 2 patents and obtained 3 items of software copyright.

Dong Wang is currently a full professor and a supervisor of postgraduate at the Chinese academy of sciences, He also served as the director of IOT Production and Mobile Grain Networking Laboratory and the chairman of the International Alliance, His currentinterest focuses on brain-computer interaction and the classic brain intelligence research. Dong Wang published 20 papers, 45 patents and two books and was invited as visiting scholars and distinguished researchers by the Stanford University and Nanyang Technological University.
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