A. 基本訊號與系統 1. Discrete-time (DT) signal and system 2. LTI system 3. DT Fourier transform　 B. Z轉換 1. Z-transform 2. Region of convergence 3. Inverse Z-transform　 C. 取樣分析 1. Periodic sampling 2. Signal reconstruction 3. Discrete-time processing　 D. 多率系統 1. Changing the sampling rate 2. Multirate signal processing 3. Oversampling and noise shaping　 E. 頻域分析 1. Frequency responses of LTI systems 2. All-pass systems 3. Minimum-phase and linear-phase systems　 F. 數字系統架構 1. Structures for IIR and FIR systems 2. Lattice filter 3. Quantization effect 4. Round-off noise effect 　 G. 濾波器設計 1. IIR filter design 2. FIR filter design 3. Optimal filter design　 H. 離散傅立業轉換 1. Discrete Fourier series 2. Discrete Fourier transform (DFT) 3. Linear convolution using DFT　 4. The discrete cosine transform (DCT)　 I. 快速傅立業轉換 1. Decimation-in-time FFT 2. Decimation-in-freq. FFT 3. Convolution approach　 　

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Multiscale Transforms with Application to Image Processing 英文无水印原版pdf pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

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This exploratory paper quests for a stochastic and context sensitive grammar of images. The grammar should achieve the following four objectives and thus serves as a unified framework of representation, learning, and recognition for a large number of object categories. (i) The grammar represents both the hierarchical decompositions from scenes, to objects, parts, primitives and pixels by terminal and non-terminal nodes and the contexts for spatial and functional relations by horizontal links between the nodes. It formulates each object category as the set of all possible valid configurations produced by the grammar. (ii) The grammar is embodied in a simple And–Or graph representation where each Or-node points to alternative sub-configurations and an And-node is decomposed into a number of components. This representation supports recursive top-down/bottom-up procedures for image parsing under the Bayesian framework and make it convenient to scale up in complexity. Given an input image, the image parsing task constructs a most probable parse graph on-the-fly as the output interpretation and this parse graph is a subgraph of the And–Or graph after * Song-Chun Zhu is also affiliated with the Lotus Hill Research Institute, China. making choice on the Or-nodes. (iii) A probabilistic model is defined on this And–Or graph representation to account for the natural occurrence frequency of objects and parts as well as their relations. This model is learned from a relatively small training set per category and then sampled to synthesize a large number of configurations to cover novel object instances in the test set. This generalization capability is mostly missing in discriminative machine learning methods and can largely improve recognition performance in experiments. (iv) To fill the well-known semantic gap between symbols and raw signals, the grammar includes a series of visual dictionaries and organizes them through graph composition. At the bottom-level the dictionary is a set of image primitives each having a number of anchor points with open bonds to link with other primitives. These primitives can be combined to form larger and larger graph structures for parts and objects. The ambiguities in inferring local primitives shall be resolved through top-down computation using larger structures. Finally these primitives forms a primal sketch representation which will generate the input image with every pixels explained. The proposal grammar integrates three prominent representations in the literature: stochastic grammars for composition, Markov (or graphical) models for contexts, and sparse coding with primitives (wavelets). It also combines the structure-based and appearance based methods in the vision literature. Finally the paper presents three case studies to illustrate the proposed grammar.

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使用Python进行动手图像处理 这是Packt发布的“ 进行的代码存储库。 用于高级图像分析和有效解释图像数据的专家技术 这本书是关于什么的？ 图像处理在我们的日常生活中扮演着重要角色，它在社交媒体（面部检测），医学成像（X射线，CT扫描），对机器人技术和太空的安全性（指纹识别）等各种应用中发挥着重要作用。 本书将触及图像处理的核心，从概念到使用Python的代码。 本书涵盖以下激动人心的功能： 在Python中执行基本的数据预处理任务，例如图像去噪和空间滤波 在Python中实现快速傅立叶变换（FFT）和频域滤波器（例如Weiner） 进行形态图像处理并使用不同的算法对图像进行分割 学习从图像中提取特征并匹配图像的技术 编写Python代码以实现用于图像处理的有监督/无监督机器学习算法 使用深度学习模型进行图像分类，分割，对象检测，转移学习和神经样式转移 如果您觉得这本书适

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