Intro
Preface
Organization
Contents - Part II
Contents - Part I
Brain Modeling for Surgical Training on the Basis of Unity 3D
1 Introduction
2 Tissue Deformation Modeling and Kinetic Equations
2.1 Biomechanical Properties of Brain Tissue
2.2 Mass-Spring Model
2.3 Physical Modeling of Brain Tissue
2.4 Kinetic Equations
3 Experimental Environment and Experimental Results
3.1 Soft and Hardware Environment
3.2 Experimental Platform
3.3 Experimental Simulation
4 Conclusion
References

Motion Saliency Detection Based on Drosophila Vision-Inspired Model
1 Introduction
2 Related Work
2.1 Drosophila Vision
2.2 Saliency Detection
2.3 Our Contributions
3 Methodology: Drosophila Vision-Inspired Model
3.1 Motion Pathway
3.2 Color Pathway
3.3 Central Brain
4 Experiment Results
4.1 Evaluation Metrics
4.2 Results on FBMS and DAVIS
4.3 Performance Discussion
5 Conclusions
References
Research on Matching Mechanism and Route Planning of Intercity Carpool
1 Overview
1.1 Introduction
1.2 Related Work
1.3 Study Content

1.4 Thesis Organization
2 Problem Definition and Model
3 Algorithm Design
4 Theorem Proof
5 Experiments
6 Conclusion
Annex 1 Source Code (code available for review only)
References
Image Undistortion and Stereo Rectification Based on Central Ray-Pixel Models
1 Introduction
2 Related Work
2.1 Corner Detection
2.2 Pinhole Models
2.3 Ray-Pixel Models
2.4 Image Undistortion and Stereo Rectification
3 Algorithms and Pipeline
3.1 Projection and Unprojection
3.2 Bundle Adjustment
3.3 Image Undistortion
3.4 Stereo Rectification

3.5 Calibration Pipeline
4 Evaluation
4.1 Image Undistortion Results
4.2 Stereo Rectification Results
4.3 Sector Grid Results
4.4 3D Reconstruction Results
5 Evaluation
References
GGM-Net: Gradient Constraint on Multi-category Brain MRI Segmentation
1 Introduction
2 Methods
2.1 Image Acquisition and Preprocessing
2.2 Gradient-Guided Multi-category Segmentation Network
2.3 Training
3 Experiment Result
3.1 Evaluation Metric
3.2 Analysis and Presentation of Results
4 Conclusion
References
Linear Split Attention for Pavement Crack Detection

1 Introduction
2 Related Work
2.1 Traditional Methods
2.2 Deep Learning Based Methods
3 Proposed Method
3.1 Model Architecture
3.2 Linear Split Attention Module
3.3 Multi-scale Feature Fusion Module
4 Experimental Result and Analysis
4.1 Experimental Setting
5 Dataset Introduction
6 Evaluation Metrics
7 Evaluation on Image Datasets
8 Ablation Studies
9 Conclusions
References
Information Acquisition and Feature Extraction of Motor Imagery EEG
1 Introduction
2 EEG Signals and Signals Acquisition
3 Preprocessing of MI-EEG Signals