Preface
List of Abbreviations
Chapter 1 Introduction
1.1 Background
1.2 Contribution of This Book
1.3 Organization of This Book
References
Chapter 2 A Survey of Image Retrieval Techniques for Tire Pattern Database
2.1 Introduction
2.2 Tire Pattern Database and Performance EvaluationMethods
2.2.1 Tire pattern image databases
2.2.2 Performance evaluation
2.3 Tire Pattern Retrieval
2.3.1 Tire tread pattern retrieval
2.3.2 Tire surface wear feature extraction
2.3.3 Video tire pattern retrieval
2.3.4 Tire indentation mark image retrieval
2.3.5 Summary
2.4 Discussion about Future Research Directions
2.4.1 Standard test dataset and performance evaluation
2.4.2 Matching between tire indentation mark and tire tread pattern
2.5 Conclusions
References
Chapter 3 A Modified Tamura Feature for Tire Pattern Image Description
3.1 Introduction of Tamura Feature
3.2 Modification of Tamura Texture Feature
3.2.1 Tamura texture feature
3.2.2 Modification
3.3 Experimental Results
3.4 Conclusions
References
Chapter 4 H-SIFT: SIFT from High-Frequency Information of Tire Pattern Images
4.1 Introduction of SIFT Feature
4.2 Review of SIFT Feature
4.2.1 Scale space and relevant concepts
4.2.2 The model of Gaussian pyramid and difference of Gaussian pyramid
4.2.3 The establishment of the key points
4.2.4 The key points matching
4.3 Description of the Proposed Method H-SIFT
4.4 Experimental Results
4.5 Conclusions
References
Chapter 5 Study on Rotation-Invariant Texture Feature Extraction for Tire Pattern Retrieval
5.1 Introduction
5.2 Radon-DTCWT Algorithm
5.2.1 Radon transform
5.2.2 Translation sensitivity of ridgelet transform
5.2.3 The new Radon-DTCWT algorithm
5.3 Curvelet Energy Distribution Algorithm
5.3.1 Curvelet transform of tire pattern image
5.3.2 Direction characteristics of tire pattern images
5.3.3 Implementation of curvelet energy distribution algorithm
5.4 Experiment Results
5.5 Conclusions
References
Chapter 6 HOG-TT: A Robust HOG-Based Texture Feature Extraction Method Making Use of Texture Tendency in Tread Pattern Images
6.1 Introduction
6.2 Description of HOG-TT
6.2.1 HOG descriptor
6.2.2 HOG-TT
6.3 Experimental Results
6.4 Conclusions
References
Chapter 7 FF-TL: An Effective Tread Pattern Image Classification Algorithm Based on Transfer Learning
7.1 Introduction
7.2 Related Work
7.2.1 Convolutional neural network
7.2.2 Transfer learning
7.3 Proposed Algorithm
7.3.1 Fine-tuning the network
7.3.2 Feature extraction, feature fusion and SVM classification
7.4 Experimental Results
7.4.1 Experimental dataset and performance evaluation parameter
7.4.2 Experimental results and analysis
7.5 Conclusions
References
Chapter 8 Summary and Future Work
8.1 Summary of the Book
8.2 Discussion of Future Work
8.3 Acknowledgment
Appendix 1: CIIP Tread Indentation Database
Appendix 2: CIIP Tread Pattern Database

在各种图像中，轮胎图案是犯罪现场调查中重要的图像数据类型。轮胎花纹图像检索（TPIR）是提供使用的重要手段，也是交通事故控制和犯罪案件解决的充分线索。
刘颖著的《轮胎花纹图像检索（英文版）》的目的是总结现有技术在TPIR。本书重点研究了纹理特征提取的关键技术。
本书内容包括胎面花纹识别的主要技术类别检索、胎面磨损特征提取、轮胎压痕标记检索和视频胎面花纹检索。