contents1 multi-dimensional signals and systems 11.1 multi-dimensional signals 21.1.1 finite-extent signals and periodic signals 21.1.2 symmetric signals 51.1.3 special multi-dimensional signals 51.2 multi-dimensional transforms 81.2.1 fourier transform of continuous signals 81.2.2 fourier transform of discrete signals 121.2.3 discrete fourier transform (dft) 141.2.4 discrete cosine transform (dct) 181.3 multi-dimensional systems 201.3.1 impulse response and 2d convolution 201.3.2 frequency response 231.3.3 fir filters and symmetry 251.3.4 iir filters and partial difference equations 271.4 multi-dimensional sampling theory 301.4.1 sampling on a lattice 301.4.2 spectrum of signals sampled on a lattice 341.4.3 nyquist criterion for sampling on a lattice 361.4.4 reconstruction from samples on a lattice 411.5 sampling structure conversion 42references 47exercises 48problem set 1 48matlab exercises 502 digital images and video 532.1 human visual system and color 542.1.1 color vision and models 542.1.2 contrast sensitivity 572.1.3 spatio-temporal frequency response 592.1.4 stereo/depth perception 622.2 digital video 632.2.1 spatial resolution and frame rate 642.2.2 color， dynamic range， and bit-depth 652.2.3 color image processing 672.2.4 digital-video standards 702.3 3d video 752.3.1 3d-display technologies 752.3.2 stereoscopic video 792.3.3 multi-view video 792.4 digital-video applications 812.4.1 digital tv 812.4.2 digital cinema 852.4.3 video streaming over the internet 882.4.4 computer vision and scene/activity understanding 912.5 image and video quality 922.5.1 visual artifacts 922.5.2 subjective quality assessment 932.5.3 objective quality assessment 94references 96image filtering 1013.1 image smoothing 1023.1.1 linear shift-invariant low-pass filtering 1023.1.2 bi-lateral filtering 1053.2 image re-sampling and multi-resolution representations 1063.2.1 image decimation 1073.2.2 interpolation 1093.2.3 multi-resolution pyramid representations 1163.2.4 wavelet representations 1173.3 image-gradient estimation好看的欧美情色电影， edge and feature detection 1233.3.1 estimation of the image gradient 1243.3.2 estimation of the laplacian 1283.3.3 canny edge detection 1303.3.4 harris corner detection 1313.4 image enhancement 1333.4.1 pixel-based contrast enhancement 1333.4.2 spatial filtering for tone mapping and image sharpening 1383.5 image denoising 1433.5.1 image and noise models 1443.5.2 linear space-invariant filters in the dft domain 1463.5.3 local adaptive filtering 1493.5.4 nonlinear filtering: order-statistics好看的欧美情色电影， wavelet shrinkage， and bi-lateral filtering 1543.5.5 non-local filtering: nl-means and bm3d 1583.6 image restoration 1603.6.1 blur models 1613.6.2 restoration of images degraded by linear space-invariant blurs 1653.6.3 blind restoration – blur identification 1713.6.4 restoration of images degraded by space-varying blurs 1733.6.5 image in-painting 176references 177exercises 182problem set 3 182matlab exercises 185matlab resources 1894 motion estimation 1914.1 image formation 1924.1.1 camera models 1924.1.2 photometric effects of 3d motion 1974.2 motion models 1984.2.1 projected motion vs. apparent motion 1994.2.2 projected 3d rigid-motion models 2034.2.3 2d apparent-motion models 2064.3 2d apparent-motion estimation 2104.3.1 sparse correspondence， optical-flow estimation， and image-registration problems 2104.3.2 optical-flow equation and normal flow 2134.3.3 displaced-frame difference 2154.3.4 motion estimation is ill-posed: occlusion and aperture problems 2164.3.5 hierarchical motion estimation 2194.3.6 performance measures for motion estimation 2204.4 differential methods 2214.4.1 lukas–kanade method 2214.4.2 horn–schunk motion estimation 2264.5 matching methods 2294.5.1 basic block-matching 2304.5.2 variable-size block-matching 2344.5.3 hierarchical block-matching 2364.5.4 generalized block-matching – local deformable motion 2374.5.5 homography estimation from feature correspondences 2394.6 nonlinear optimization methods 2414.6.1 pel-recursive motion estimation 2414.6.2 bayesian motion estimation 2434.7 transform-domain methods 2454.7.1 phase-correlation method 2454.7.2 space-frequency spectral methods 2474.8 3d motion and structure estimation 2474.8.1 camera calibration 2484.8.2 affine reconstruction 2494.8.3 projective reconstruction 2514.8.4 euclidean reconstruction 2564.8.5 planar-parallax and relative affine structure reconstruction 2574.8.6 dense structure from stereo 259references 259exercises 264problem set 4 264matlab exercises 266matlab resources 2685 video segmentation and tracking 2695.1 image segmentation 2715.1.1 thresholding 2715.1.2 clustering 2735.1.3 bayesian methods 2775.1.4 graph-based methods 2815.1.5 active-contour models 2835.2 change detection 2855.2.1 shot-boundary detection 2855.2.2 background subtraction 2875.3 motion segmentation 2945.3.1 dominant-motion segmentation 2955.3.2 multiple-motion segmentation 2985.3.3 region-based motion segmentation: fusion of color and motion 3075.3.4 simultaneous motion estimation and segmentation 3095.4 motion tracking 3135.4.1 graph-based spatio-temporal segmentation and tracking 3155.4.2 kanade–lucas–tomasi tracking 3155.4.3 mean-shift tracking 3175.4.4 particle-filter tracking 3195.4.5 active-contour tracking 3215.4.6 2d-mesh tracking 3235.5 image and video matting 3245.6 performance evaluation 326references 327matlab exercises 334internet resources 3356 video filtering 3376.1 theory of spatio-temporal filtering 3386.1.1 frequency spectrum of video 3386.1.2 motion-adaptive filtering 3416.1.3 motion-compensated filtering 3416.2 video-format conversion 3456.2.1 down-conversion 3476.2.2 de-interlacing 3516.2.3 frame-rate conversion 3576.3 multi-frame noise filtering 3636.3.1 motion-adaptive noise filtering 3636.3.2 motion-compensated noise filtering 3656.4 multi-frame restoration 3706.4.1 multi-frame modeling 3716.4.2 multi-frame wiener restoration 3716.5 multi-frame super-resolution 3736.5.1 what is super-resolution? 3746.5.2 modeling low-resolution sampling 3776.5.3 super-resolution in the frequency domain 3826.5.4 multi-frame spatial-domain methods 385references 390exercises 395problem set 6 395matlab exercises 3967 image compression 3977.1 basics of image compression 3987.1.1 information theoretic concepts 3987.1.2 elements of image-compression systems 4017.1.3 quantization 4027.1.4 symbol coding 4057.1.5 huffman coding 4067.1.6 arithmetic coding 4107.2 discrete-cosine transform coding and jpeg 4137.2.1 discrete-cosine transform 4147.2.2 iso jpeg standard 4167.2.3 encoder control and compression artifacts 4237.3 wavelet-transform coding and jpeg 2000 4247.3.1 wavelet transform and choice of filters 4257.3.2 iso jpeg 2000 standard 429references 435exercises 437internet resources 4408 video compression 4418.1 video-compression approaches 4428.1.1 intra-frame compression， motion jpeg 2000， and digital cinema 4428.1.2 3d-transform coding 4438.1.3 motion-compensated transform coding 4468.2 early video-compression standards 4478.2.1 iso and itu standards 4478.2.2 mpeg-1 standard 4488.2.3 mpeg-2 standard 4568.3 mpeg-4 avc/itu-t h.264 standard 4638.3.1 input-video formats and data structure 4648.3.2 intra-prediction 4658.3.3 motion compensation 4668.3.4 transform 4688.3.5 other tools and improvements 4698.4 high-efficiency video-coding (hevc) standard 4718.4.1 video-input format and data structure 4718.4.2 coding-tree units 4728.4.3 tools for parallel encoding/decoding 4738.4.4 other tools and improvements 4758.5 scalable-video compression 4778.5.1 temporal scalability 4788.5.2 spatial scalability 4798.5.3 quality (snr) scalability 4808.5.4 hybrid scalability 4828.6 stereo and multi-view video compression 4828.6.1 frame-compatible stereo-video compression 4838.6.2 stereo and multi-view video-coding extensions of the h.264/avc standard 4848.6.3 multi-view video plus depth compression 487references 492exercises 494internet resources 495a ill-posed problems in image and video processing 497a.1 image representations 497a.1.1 deterministic framework – function/vector spaces 497a.1.2 bayesian framework – random fields 498a.2 overview of image models 498a.3 basics of sparse-image modeling 500a.4 well-posed formulations of ill-posed problems 501a.4.1 constrained-optimization problem 501a.4.2 bayesian-estimation problem 502references 502b markov and gibbs random fields 503b.1 equivalence of markov random fields and gibbs random fields 503b.1.1 markov random fields 504b.1.2 gibbs random fields 505b.1.3 equivalence of mrf and grf 506b.2 gibbs distribution as an a priori pdf model 507b.3 computation of local conditional probabilities from a gibbs distribution 508references 509c optimization methods 511c.1 gradient-based optimization 512c.1.1 steepest-descent method 512c.1.2 newton–raphson method 513c.2 simulated annealing 514c.2.1 metropolis algorithm 515c.2.2 gibbs sampler 516c.3 greedy methods 517c.3.1 iterated conditional modes 517c.3.2 mean-field annealing 518c.3.3 highest confidence first 518references 519d model fitting 521d.1 least-squares fitting 521d.2 least-squares solution of homogeneous linear equations 522d.2.1 alternate derivation 523d.3 total least-squares fitting 524d.4 random-sample consensus (ransac) 526references 526glossary527目　　录第1章　多维信号与系统 11.1　多维信号 21.1.1　有限域信号和周期信号 21.1.2　对称信号 51.1.3　终点的多维信号 51.2　多维变换 81.2.1　相连信号的傅里叶变换 81.2.2　突破信号的傅里叶变换 121.2.3　突破傅里叶变换（dft） 141.2.4　突破余弦变换（dct） 181.3　多维系统 201.3.1　脉冲反映和2d卷积 201.3.2　频率反映 231.3.3　fir滤波器及对称性 251.3.4　iir滤波器及偏微分方程 271.4　多维采样表面 301.4.1　格上采样 301.4.2　格上采样信号的谱 341.4.3　格上采样中的奈奎斯特许则 361.4.4　格上采样信号重建 411.5　采样形态诊疗 42参考文件 47习题 48问题集1 48matlab习题 50第2章　数字图像和视频 532.1　东谈主类视觉系统和色调 542.1.1　色觉及彩色模子 542.1.2　对比敏锐度 572.1.3　时空频率反映 592.1.4　立体/深度感知 622.2　数字视频 632.2.1　空间分离率和帧速 642.2.2　颜料、动态领域和位深 652.2.3　彩色图像处置 672.2.4　数字视频圭臬 702.3　3d视频 752.3.1　3d融会技巧 752.3.2　立体视频 792.3.3　多视角视频 792.4　数字视频哄骗 812.4.1　数字电视 812.4.2　数字影院 852.4.3　互联网中的视频流 882.4.4　规划机视觉和场景/举止融会 912.5　图像和视频的质料 922.5.1　视觉着力挫伤 922.5.2　主不雅质料评估 932.5.3　客不雅质料评估 94参考文件 96第3章　图像滤波 1013.1　图像平滑 1023.1.1　线性移不变低通滤波 1023.1.2　双边滤波 1053.2　图像重采样和多分离率默示 1063.2.1　图像抽取 1073.2.2　图像内插 1093.2.3　多分离率金字塔默示 1163.2.4　小波默示 1173.3　图像梯度预见、旯旮和特征检测 1233.3.1　图像梯度预见 1243.3.2　拉普拉斯预见 1283.3.3　canny旯旮检测 1303.3.4　harris角检测 1313.4　图像增强 1333.4.1　基于像素的对比度增强 1333.4.2　用于色调映射和图像锐化的空间滤波 1383.5　图像去噪 1433.5.1　图像和噪声模子 1443.5.2　dft域的线性空间不变滤波器 1463.5.3　局部自符合滤波 1493.5.4　非线性滤波：排序统计、小波松开和双边滤波 1543.5.5　非局部滤波：nl-means和bm3d 1583.6　图像收复 1603.6.1　恶浊模子 1613.6.2　线性空间不变恶浊图像的收复 1653.6.3　盲收复—恶浊识别 1713.6.4　空间变化恶浊图像的收复 1733.6.5　图像缔造 176参考文件 187习题 182问题集3 182matlab习题 185matlab资源 189第4章　畅通预见 1914.1　图像的酿成 1914.1.1　相机模子 1924.1.2　3d畅通的光学着力 1974.2　畅通模子 1984.2.1　投射畅通与表不雅畅通 1994.2.2　3d刚体畅通投射模子 2034.2.3　2d表不雅畅通模子 2064.3　2d表不雅畅通预见 2104.3.1　寥落对应性、光流预见和图像配准问题 2104.3.2　光流方程和法向流 2134.3.3　帧间差 2154.3.4　畅通预见的病态性：装扮与孔洞问题 2164.3.5　分层畅通预见 2194.3.6　畅通预见的性能权衡 2204.4　差分法 2214.4.1　lukas-kanade法 2214.4.2　horn-schunk畅通预见 2264.5　匹配法 2294.5.1　基本的块匹配 2304.5.2　变尺寸块匹配 2344.5.3　分层块匹配 2364.5.4　推广的块匹配—局部变形畅通 2374.5.5　特征对应的单应性预见 2394.6　非线性优化法 2414.6.1　像素递归畅通预见 2414.6.2　贝叶斯畅通预见 2434.7　变换域门径 2454.7.1　相位相关法 2454.7.2　空间-频率谱法 2474.8　3d畅通预见和结构预见 2474.8.1　相机标定 2484.8.2　仿射重建 2494.8.3　投影重建 2514.8.4　欧氏重建 2564.8.5　平面视差和相关仿射结构重建 2574.8.6　立体中的详细结构 259参考文件 259习题 264问题集4 264matlab习题 266matlab资源 268第5章　视频分割与追踪 2695.1　图像分割 2715.1.1　阈值法 2715.1.2　聚类法 2735.1.3　贝叶斯法 2775.1.4　图形法 2815.1.5　主动概括模子 2835.2　变化检测 2855.2.1　镜头范畴检测 2855.2.2　布景差法 2875.3　畅通分割 2945.3.1　主要畅通分割 2955.3.2　复杂畅通分割 2985.3.3　基于区域的畅通分割：彩色与畅通的交融 3075.3.4　畅通预见与分割的同期扫尾 3095.4　畅通追踪 3135.4.1　基于图形的空-时辰割与追踪 3155.4.2　kanade-lucas-tomasi追踪 3155.4.3　mean-shift追踪 3175.4.4　粒子滤波追踪 3195.4.5　主动概括追踪 3215.4.6　2d-mesh追踪 3235.5　图像抠图和视频抠像 3245.6　性能评估 326参考文件 327matlab习题 334互联网资源 335第6章　视频滤波 3376.1　空–时滤波表面 3386.1.1　视频的频谱 3396.1.2　畅通自符合滤波 3416.1.3　畅通抵偿滤波 3416.2　视频形态诊疗 3456.2.1　降采样 3476.2.2　去隔行 3516.2.3　帧率诊疗 3576.3　多帧麇集噪声滤除 3636.3.1　畅通自符合噪声滤除 3636.3.2　畅通抵偿噪声滤除 3656.4　多帧麇集收复 3706.4.1　多帧麇集建模 3716.4.2　多帧麇集维纳收复 3716.5　多帧麇集超分离率重建 3736.5.1　什么是超分离率重建 3746.5.2　低分离率采样建模 3776.5.3　频域超分离率重建 3826.5.4　空域多帧法 385参考文件 390习题 395问题集6 395matlab习题 396第7章　图像压缩 3977.1　图像压缩的基础 3987.1.1　信息论成见 3987.1.2　图像压缩系统的构成 4017.1.3　量化 4027.1.4　秀美编码 4057.1.5　huffman编码 4067.1.6　算术编码 4107.2　突破余弦变换编码和jpeg 4137.2.1　突破余弦变换 4147.2.2　iso jpeg圭臬 4167.2.3　编码为止与压缩挫伤 4237.3　小波变换编码和jpeg 2000 4247.3.1　小波变换和滤波器选择 4257.3.2　iso jpeg 2000圭臬 429参考文件 435习题 437互联网资源 440第8章　视频压缩 4418.1　视频压缩门径 4428.1.1　帧内压缩、畅通jpeg 2000和数字影院 4428.1.2　3d变换编码 4438.1.3　畅通压缩变换编码 4468.2　早期的视频压缩圭臬 4478.2.1　iso和itu圭臬 4478.2.2　mpeg-1圭臬 4488.2.3　mpeg-2圭臬 4568.3　mpeg-4 avc/itu-t h.264圭臬 4638.3.1　视频输入形态和数据结构 4648.3.2　帧内预计 4658.3.3　畅通抵偿 4668.3.4　变换 4688.3.5　其他器用和蜕变 4698.4　高效视频编码（hevc）圭臬 4718.4.1　视频输入形态和数据结构 4718.4.2　编码树单位 4728.4.3　并行编码/解码器用 4738.4.4　其他器用与蜕变 4758.5　可伸缩视频压缩 4778.5.1　时期可伸缩性 4788.5.2　空间可伸缩性 4798.5.3　质料（snr）分级 4808.5.4　夹杂可伸缩 4828.6　立体视频和多视角视频压缩 4828.6.1　帧兼容的立体视频压缩 4838.6.2　h.264/avc圭臬中对于立体和多视角视频编码的推广 4848.6.3　多视角加深度信息的视频压缩 487参考文件 492习题 494互联网资源 495附录a　图像和视频处置中的病态问题 497a.1　图像默示 497a.1.1　细目性框架—函数/矢量空间 497a.1.2　贝叶斯框架—当场场 498a.2　图像模子概览 498a.3　图像寥落建模基础 500a.4　病态问题的适定公式 501a.4.1　条目优化问题 501a.4.2　贝叶斯预见问题 502参考文件 502附录b　markov和gibbs当场场 503b.1　markov当场场与gibbs当场场的等价性 503b.1.1　markov当场场 504b.1.2　gibbs当场场 505b.1.3　mrf和grf的等价性 506b.2　先验pdf模子的gibbs散播 507b.3　gibbs散播中局部条目概率的规划 508参考文件 509附录c　优化门径 511c.1　基于梯度的优化 512c.1.1　*速下落法 512c.1.2　newton-raphson法 513c.2　模拟退火法 514c.2.1　metropolis算法 515c.2.2　gibbs抽样 516c.3　揣度法 517c.3.1　条目递归法 517c.3.2　平均场退火法 518c.3.3　*高信任优先法 518参考文件 519附录d　模子拟合 521d.1　*小均方拟正当 522d.2　皆次线性方程组的ls解 522d.2.1　轮流推导法 523d.3　总体*小均方拟正当 524d.4　当场采样一致性（ransac） 526参考文件 526术语表 527 26uuu新地址网站 色戒完整未删版在线看