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研究生： 裴哈利
Heri - Prasetyo
論文名稱： In-depth Exploration on Image Features and Characteristics Modeling and Excavating for Various Applications
In-depth Exploration on Image Features and Characteristics Modeling and Excavating for Various Applications
指導教授： 郭景明
Jing-Ming Guo
口試委員: 王乃堅
Nai-Jian Wang
李宗南
Chung-Nan Lee
范國清
Kuo-Chin Fan
貝蘇章
Soo-Chang Pei
蔡文祥
Wen-Hsiang Tsai
鍾國亮
Kuo-Liang Chung
蘇順豐
Shun-Feng Su
學位類別： 博士
Doctor
系所名稱： 電資學院 - 電機工程系
Department of Electrical Engineering
論文出版年： 2015
畢業學年度： 103
語文別： 英文
論文頁數： 276
外文關鍵詞： halftoning-based BTC, vehicle verification
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    • This dissertation presents some techniques on image feature excavating and image characteristic modeling for several applications. The image feature can be simply constructed and generated by considering the underlying statistical property of an image as well as image content and characteristic. Based on this premise, four various research topics are included: 1) Singular Value Decomposition (SVD)-based image watermarking, 2) statistical-based vehicle verification, 3) image retrieval using halftoning-based Block Truncation Coding (BTC), and 4) halftoning-based BTC image restoration. Among these, the vehicle verification employs the statistical modeling of an image to generate an image feature descriptor, and the other methods extract the image feature descriptor by considering the image content and characteristics.
    Some security attacks and ambiguity issues on the SVD-based image watermarking are explored and discussed in this dissertation. These attacks are delivered with the demonstration on the former published SVD-based image watermarking scheme, and the solutions are provided in this dissertation. Some methods with the statistical modeling on feature descriptor generation are also presented in this dissertation for the vehicle verification task. An effective approach is presented in this dissertation to generate the image descriptor from the halftoning-based BTC compressed data stream for image retrieval and classification. An additional approach on image restoration of halftoning-based BTC is also presented in this dissertation. The performance of the proposed feature descriptors are extensively investigated and tested for their suitability on the image watermarking, vehicle verification, image retrieval, and image restoration applications. As documented in the experimental results. The proposed methods can be effectively applied to these domains, and thus they can be very competitive candidates to the practical usages.

    Acknowledgements iii Abstract iv Contents v List of Figures ix List of Tables xiii Chapter 1. Introduction 1 1.1. Motivation 1 1.2. Contribution 5 1.3. Organization 12 Chapter 2. Image Characteristic Excavating on Watermarking Application 14 2.1. Prior Works on SVD-based Image Watermarking 17 2.2. Singular Value Decomposition 20 2.2.1. Singular Value Decomposition 20 2.2.2. Shuffled Singular Value Decomposition 22 2.2.3. Generalized Singular Value Decomposition 24 2.2.4. Principal Component 25 2.3. Inserting Whole Image into Host Image 27 2.3.1. SVD-Based Image Watermarking on Redundant Discrete Wavelet Transform 28 2.3.2. Watermark Embedding on RDWT-SVD Image Watermarking 29 2.3.3. Watermark Extraction on RDWT-SVD Image Watermarking 32 2.3.4. Security Attack I on RDWT-SVD Image Watermarking 38 2.3.5. Security Attack II on RDWT-SVD Image Watermarking 42 2.3.6. Security Attack III on RDWT-SVD Image Watermarking 47 2.3.7. Analysis of RDWT-SVD Image Watermarking 53 2.3.8. Secure RDWT-SVD Image Watermarking 56 2.3.9. Watermark Embedding on S-RDWT-SVD Image Watermarking 56 2.3.10. Watermark Extraction on S-RDWT-SVD Image Watermarking 59 2.3.11. Performance Evaluation of S-RDWT-SVD Image Watermarking 61 2.3.12. Security Attacks on S-RDWT-SVD Image Watermarking 64 2.4. Inserting Singular Value Matrix of Watermark into Host Image 69 2.4.1. SVD-Based Image Watermarking on Discrete Wavelet Transform 70 2.4.2. Security Attacks on DWT-SVD Image Watermarking 74 2.4.3. Security Analysis of SVD-DWT Image Watermarking 76 2.4.4. False-Positive-Free SVD Image Watermarking 79 2.4.5. Watermark Embedding on FPF-SVD Image Watermarking 80 2.4.6. Watermark Extraction on FPF-SVD Image Watermarking 83 2.4.7. Performance Evaluation of FPF-SVD Image Watermarking 85 2.4.8. Security Attacks on FPF-SVD Image Watermarking 91 2.5. GSVD-Based Image Watermarking 93 2.5.1. GSVD-Based Image Watermarking 93 2.5.2. Security Attacks on GSVD Image Watermarking 96 2.5.3. False-Positive-Free GSVD Image Watermarking 99 2.5.4. Watermark Embedding of FPF-GSVD Image Watermarking 99 2.5.5. Watermark Extraction of FPF-GSVD Image Watermarking 103 2.5.6. Performance Evaluation of FPF-GSVD Image Watermarking 105 2.5.7. Security Attack on FPF-GSVD Image Watermarking 109 2.6. Preliminary Summaries 111 Chapter 3. Image Feature Modeling on Vehicle Verification Application 118 3.1. Introduction and Prior Works 121 3.2. Gabor Filter and Curvelet Transform 123 3.3. Maximum Likelihood Estimation 127 3.3.1. Under Gamma Distribution Assumption 128 3.3.2. Under Gaussian Distribution Assumption 130 3.3.3. Under Laplace Distribution Assumption 132 3.3.4. Under Generalized Gaussian Distribution Assumption 133 3.3.5. Statistical Goodness-of-Fit 135 3.4. Vehicle Verification 138 3.4.1. Gamma Feature Descriptor 139 3.4.2. Gaussian, Laplace, and Generalized Gaussian Feature Descriptor 140 3.5. Experimental Result 142 3.5.1. Effectiveness of Gamma Feature Descriptor 144 3.5.2. Effectiveness of Gaussian Feature Descriptor 144 3.5.3. Effectiveness of Laplace Feature Descriptor 145 3.5.4. Effectiveness of Generalized Gaussian Distribution Feature Descriptor 146 3.5.5. Performance Comparison 147 3.6. Preliminary Summary 150 Chapter 4. Image Characteristic Excavating on Retrieval Application 152 4.1. Introduction and Prior Works 155 4.2. Halftoning-Based Block Truncation Coding for Color Image 157 4.2.1. Bitmap Image Generation for ODBTC 157 4.2.2. Bitmap Image Generation for EDBTC 160 4.2.3. Bitmap Image Generation for DDBTC 161 4.2.4. Color Quantizer Determination 165 4.2.5. Halftoning-Based BTC Image Decoding 165 4.3. Image Feature from Color Quantizer 167 4.3.1. Color Co-occurrence 1 167 4.3.2. Color Co-occurrence 2 170 4.3.3. Color Histogram Feature 174 4.4. Image Feature from Bitmap Image 177 4.5. Image Retrieval Using Halftoning-Based BTC Feature 179 4.5.1. Similarity Distance Computation Using ODBTC Feature 1 180 4.5.2. Similarity Distance Computation Using ODBTC Feature 2 183 4.5.3. Similarity Distance Computation Using EDBTC Feature 184 4.5.4. Similarity Distance Computation Using DDBTC Feature 184 4.6. Performance Evaluation 185 4.7. Experimental Result Using ODBTC Feature 1 186 4.8. Experimental Result Using ODBTC Feature 2 195 4.9. Experimental Result Using EDBTC Feature 200 4.10. Experimental Result Using DDBTC Feature 205 4.11. Preliminary Summary 214 Chapter 5. Image Characteristic Excavating on Restoration Application 218 5.1. Introduction and Prior Works 220 5.1.1. Halftoning-Based Block Truncation Coding for Grayscale Image 223 5.1.2. Lowpass Filtering Image Restoration 226 5.1.3. Variance Classified Image Restoration 227 5.2. Halftoning-Based BTC Image Restoration 228 5.2.1. Vector Quantization-Based Image Restoration 229 5.2.2. Sparsity-Based Image Restoration 231 5.2.3. Adding Border Constraint on VQ-Based Image Restoration 233 5.2.4. Adding Border Constraint on Sparsity-Based Image Restoration 236 5.3. Experimental Result 240 5.4. Preliminary Summary 246 Chapter 6. Conclusions and Future Works 249 References 255

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