Visual Computation Methods-视觉计算方法_PDF下载[123MB-百度云]谢剑斌

本书特色

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　　In order to help our readers fully understand the history, ideas, theories, improvement, simulation and features of video parsing algorithm, this book gives a detailed introduction to more than 50 basic theories of operators, descriptors, filtration, transformation, methods and so on concerning video parsing algorithm, and expounds the means for improvement and experimental simulation of video parsing algorithm. It systematically summarizes both its strong points and shortcomings, and provides sets of source codes of experimental simulation and video image libraries. Please find www.kedachang.com for related materials.

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内容简介

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This book can be used astextbooks for junior andsenior undergraduates and graduates in disciplines like information，computer，automation，electronics and communications，be used as a reference book for research and development in fields likevideo analysis and intelligence identification

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CONTENTSPREFACEChapter 1 Video Image Enhancement 11.1 Recursive Median Filter 1References 21.2 Least Squares Filter 21.3 Homomorphic Filter 7References 101.4 Bilateral Filter 10References 141.5 Guided Filter 14References 161.6 Lateral Inhibition Network 16References 221.7 Mathematical Morphology 22References 30Chapter 2 Video Image Segmentation 312.1 Double-Peak Histogram 31References 342.2 Watershed 34References 362.3 Regional Split-and-Merge 37References 382.4 OTSU 38References 402.5 Maximum 2D Entropy 41References 472.6 2D Cross-Entropy 48References 55Chapter 3 Key Point Detection 563.1 Moravec 56References 583.2 Forstner 58References 603.3 Harris 60References 643.4 SUSAN 64References 693.5 CSS 69References 743.6 FAST 74References 773.7 DoG 77References 803.8 LoG 80References 83Chapter 4 Visual Feature Descriptors 844.1 Hu Moment 84References 864.2 Legendre Moments 874.3 Fourier Descriptors 89References 914.4 Haar 91References 964.5 HOG 96References 984.6 LBP 99References 1034.7 SIFT 103References 1124.8 SURF 112References 116Chapter 5 Transform and Dimension Reduction 1175.1 K-L Transform 117References 1195.2 DCT Transform 120References 1265.3 Gabor Transform 126References 1295.4 Wavelet Transform 129References 1355.5 Haar Transform 136References 1405.6 Hough Transform 140References 1465.7 LPT Transform 146References 1505.8 PCA 150References 1545.9 LDA 154References 157Chapter 6 Clustering and Classification 1586.1 Measure Methods of Similarity 1586.2 K-Means Clustering 162References 1656.3 Bayesian Methods 165References 1686.4 Adaptive Boosting 168References 1746.5 SVM 174References 181Chapter 7 Motion Detection and Target Tracking 1827.1 Background Subtraction 182References 1897.2 Temporal Difference 189References 1947.3 Optical Flow 194References 2027.4 Kalman Filtering 202References 2077.5 Mean Shift 207References 2117.6 CamShift Method 211References 213