深度学习在动态媒体中的应用与实践 pdf epub mobi txt 电子书下载 2026

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出版者:人民邮电出版社

作者:唐宏

出品人:

页数:120

译者:

出版时间:2018-5

价格:59.00元

装帧:平装

isbn号码:9787115480101

丛书系列:

图书标签:

深度学习
深度学习
动态媒体
计算机视觉
机器学习
图像处理
视频分析
人工智能
多媒体
算法
实践

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具体描述

本书是一本深度学习的基础入门读物，对深度学习的基本理论进行了介绍，主要以Ubuntu系统为例搭建了三大主流框架——Caffe、TensorFlow、Torch，然后分别在3个框架下，通过3个实战项目掌握了框架的使用方法，并详细描述了生产流程，最后讲述了通过集群部署深度学习的项目以及如何进行运营维护的注意事项。

好的，这是一份图书简介，主题为《深度学习在动态媒体中的应用与实践》的姊妹篇，聚焦于一个相关但不同的领域——《计算机视觉与模式识别：从理论基石到前沿算法》。 --- 图书简介：计算机视觉与模式识别：从理论基石到前沿算法作者：[请在此处填写作者姓名] 出版社：[请在此处填写出版社名称] ISBN：[请在此处填写ISBN号] 内容概要本书旨在为读者提供一个全面、深入且结构严谨的指南，探索计算机视觉（Computer Vision, CV）与模式识别（Pattern Recognition, PR）领域的核心理论、经典算法以及近年来由深度学习驱动的前沿进展。在信息爆炸的时代，机器“看懂”世界的能力已成为人工智能研究的基石。本书不仅关注那些在工业界和学术界产生深远影响的成熟技术，更着重剖析驱动这些技术背后的数学原理、统计学基础和计算模型，帮助读者建立起坚实的理论框架，并能独立应对复杂的视觉识别与理解挑战。全书内容划分为四个主要部分：基础理论、经典方法、深度学习时代的方法论，以及前沿与交叉领域。这种组织方式确保了即便是初次接触该领域的读者，也能通过扎实的数学基础逐步迈向尖端技术的研究与应用。第一部分：基础理论与数学基石本部分是全书的理论引擎，旨在为后续的算法学习奠定不可动摇的数学基础。我们详细阐述了处理图像与数据所需的关键数学工具，并引入了模式识别的统计学视角。第一章：数字图像处理基础本章从信号处理的角度切入，探讨数字图像的采样、量化过程及其对后续处理的影响。内容涵盖图像的表示形式（如像素矩阵、傅里叶变换域）、基本的空间域滤波技术（如卷积、高斯平滑、Sobel算子），以及图像增强（直方图均衡化）和复原（逆滤波、Wiener滤波）的数学模型。特别强调了图像的频域分析，这是理解高频与低频信息特征提取的关键。第二章：概率论与统计决策理论模式识别的核心在于从不确定性中做出最优决策。本章聚焦于贝叶斯决策理论，详细推导了最小错误率、最小风险准则。内容包括参数估计（最大似然估计MLE、最大后验估计MAP）、非参数方法（如K近邻法），以及判别分析的基础，为理解分类器的工作原理打下统计学基础。第三章：线性代数与特征提取特征是连接原始数据与识别模型的桥梁。本章深入探讨了降维技术，重点剖析了主成分分析（PCA）的数学推导及其在特征空间投影中的应用。此外，还引入了线性判别分析（LDA）在最大化类间散度、最小化类内散度的目标函数构建过程，并讨论了奇异值分解（SVD）在数据压缩和噪声抑制中的作用。第二部分：经典模式识别与视觉算法在深度学习浪潮兴起之前，诸多精巧的算法构成了计算机视觉的黄金时代。本部分系统回顾了这些经典方法，它们至今仍是许多嵌入式系统、低资源环境或特定任务的有效解决方案。第四章：传统特征描述子本章专注于手工设计特征（Handcrafted Features）的原理。详细介绍了边缘检测算法（如Canny）、角点检测（Harris角点）、纹理分析（灰度共生矩阵GLCM）的计算方法。随后，重点讲解了尺度不变特征变换（SIFT）和加速鲁棒特征（SURF）的构建流程，包括其尺度空间构建、梯度计算、描述符生成及其对仿射变换的鲁棒性来源。第五章：分类器的构建与优化本章深入研究了经典的分类器模型。支持向量机（SVM）的推导将围绕最大间隔超平面、核技巧（Kernel Trick）展开，并讨论了软间隔的优化问题。同时，系统讲解了决策树和随机森林的构建过程、熵和信息增益的概念，以及提升（Boosting）算法（如AdaBoost）如何迭代优化弱分类器。第六章：几何视觉与三维重建基础本部分从几何学的角度审视图像。内容包括相机模型（针孔模型、内参与外参标定）、立体视觉的基础——视差计算，以及单应性（Homography）的计算与应用。对基础的结构光原理和对极几何（Epipolar Geometry）的数学表达进行了详尽的阐述，为理解多视图几何奠定了基础。第三部分：深度学习时代的方法论随着大型数据集和高性能计算的普及，深度学习范式彻底革新了CV/PR领域。本部分将核心放在深度神经网络的设计、训练与优化上。第七章：卷积神经网络（CNN）的架构与原理本章是深度学习视觉应用的核心。从最基本的神经元模型和反向传播算法讲起，逐步过渡到卷积层的数学定义、池化操作的功能。随后，详细对比和分析了AlexNet、VGGNet、GoogLeNet/Inception、ResNet（残差连接的意义）等标志性网络的结构设计思想，及其如何解决深度网络训练中的梯度消失/爆炸问题。第八章：目标检测与定位技术目标检测是计算机视觉中应用最广泛的任务之一。本章系统地梳理了基于区域的（R-CNN系列，Fast/Faster R-CNN的演进）与单阶段（YOLO、SSD）检测器的核心思想。重点分析了Anchor Box机制、非极大值抑制（NMS）的算法流程，以及损失函数（如交并比IoU损失）的设计如何影响最终的定位精度。第九章：语义分割与实例分割本章聚焦于像素级的理解。语义分割部分，详细介绍了全卷积网络（FCN）的原理及其如何实现密集的预测。随后，深入探讨了U-Net（及其在医学图像中的应用）和DeepLab系列模型中空洞卷积（Atrous Convolution）和条件随机场（CRF）后处理的作用。实例分割部分则侧重于Mask R-CNN等方法中，如何通过并行分支实现实例级的掩膜预测。第四部分：前沿探索与跨领域应用本部分将视野拓展到当前研究的热点方向，展示了计算机视觉与模式识别技术如何与其他AI分支进行深度融合，解决更复杂的问题。第十章：生成模型与对抗学习本章探索了机器生成逼真图像的能力。详细阐述了生成对抗网络（GAN）的博弈论基础，包括判别器与生成器的训练机制。内容将覆盖DCGAN、WGAN（Wasserstein距离的引入）以及条件GAN（cGAN）在图像到图像翻译（如CycleGAN）中的应用，并探讨了其在数据增强和隐私保护方面的潜力。第十一章：度量学习与少样本学习在数据稀疏或需要高区分度识别的任务中，度量学习至关重要。本章聚焦于如何设计有效的损失函数来学习特征空间的距离关系，包括对比损失（Contrastive Loss）、三元组损失（Triplet Loss）及其在人脸验证和行人重识别中的应用。同时，介绍了元学习（Meta-Learning）的基本思想，如何实现“学会学习”的范式。第十二章：模型部署与效率优化理论模型到实际应用的落地需要考虑效率和资源消耗。本章探讨了模型量化（Quantization）、剪枝（Pruning）等模型压缩技术。此外，还介绍了知识蒸馏（Knowledge Distillation）用于加速模型推理，并简要讨论了针对特定硬件（如移动端GPU或FPGA）的推理框架优化策略。读者对象与学习价值本书面向具有一定高等数学和线性代数基础的计算机科学、电子工程、自动化或数据科学专业的本科高年级学生、研究生以及希望系统性更新知识结构的工程师和研究人员。通过本书的学习，读者将能够： 1. 掌握核心原理：深刻理解从傅里叶变换到贝叶斯决策，再到反向传播等基础算法的数学推导过程。 2. 辨识算法优势：准确评估经典算法与深度学习模型在特定场景下的适用性与局限性。 3. 构建前沿模型：掌握当前主流的CNN架构设计、目标检测与分割的实现细节。 4. 指导研究方向：为深入研究度量学习、生成对抗网络等前沿课题提供坚实的理论支撑。本书力求在理论的深度与实践的可操作性之间取得最佳平衡，是深入探索计算机视觉与模式识别领域的必备参考书。

作者简介

唐宏

中国电信股份有限公司广州研究院数据通信研究所所长、高级工程师，中国电子学会云计算专家委员会委员，中国电信股份有限公司科技委员会数据组副组长，中国SDN产业联盟需求场景与网络架构组组长。主要从事IP承载网、下一代互联网、网络新技术方面的研发与管理工作。

目录信息

第 1 章深度学习简介 ············································································ 1
1.1 深度学习的发展 ·······································································1
1.2 深度学习的应用及研究方向 ···················································3
1.3 深度学习工具介绍和对比 ·······················································4
1.3.1 Caffe·················································································4
1.3.2 TensorFlow ······································································5
1.3.3 Torch ················································································6
1.4 小结 ···························································································7
第 2 章深度学习基本理论 ····································································9
2.1 深度学习的基本概念 ·······························································9
2.2 深度学习的训练过程 ·····························································13
2.3 深度学习的常用模型和方法 ·················································14
2.4 小结 ·························································································20
第 3 章深度学习环境搭建 ································································· 23
3.1 Caffe 安装 ···············································································23
3.1.1 安装 Caffe 的相关依赖项·············································24
3.1.2 安装 NVIDIA 驱动 ·······················································24
3.1.3 安装 CUDA ···································································27
3.1.4 配置 cuDNN ··································································30
3.1.5 源代码编译安装 OpenCV ············································32
3.1.6 编译 Caffe，并配置 Python 接口 ································34
3.2 Caffe 框架下的 MNIST 数字识别问题···································41
3.3 TensorFlow 安装 ······································································42
3.3.1 基于 pip 安装·································································42
3.3.2 基于 Anaconda 安装 ······················································46
3.3.3 基于源代码安装····························································51
3.3.4 常见安装问题································································56
3.4 TensorFlow 框架下的 CIFAR 图像识别问题·························59
3.5 Torch 安装 ···············································································61
3.5.1 无 CUDA 的 Torch 7 安装 ·············································61
3.5.2 CUDA 的 Torch 7 安装 ··················································61
3.6 Torch 框架下 neural-style 图像合成问题······························62
3.7 小结 ·························································································74
第 4 章人脸识别 ················································································· 75
4.1 人脸识别概述 ·········································································75
4.2 人脸识别系统设计 ·································································76
4.2.1 需求分析········································································76
4.2.2 功能设计········································································77
4.2.3 模块设计········································································78
4.3 系统生产环境部署及验证 ·····················································81
4.3.1 抽帧环境部署································································81
4.3.2 抽帧功能验证································································82
4.3.3 OpenFace 环境部署·······················································82
4.3.4 OpenFace 环境验证·······················································84
4.4 批量生产 ·················································································90
4.5 小结 ·······················································································102
第 5 章车辆识别 ···············································································103
5.1 概述 ·······················································································103
5.2 系统设计 ···············································································104
5.2.1 需求分析······································································104
5.2.2 功能设计······································································104
5.2.3 模块设计······································································105
5.3 系统生产环境部署及验证 ···················································106
5.3.1 生产环境部署······························································106
5.3.2 项目部署······································································107
5.3.3 环境验证······································································108
5.4 批量生产 ···············································································109
5.5 小结 ·······················································································117
第 6 章不良视频识别 ······································································· 119
6.1 概述 ·······················································································119
6.2 不良图片模型简介 ·······························································120
6.3 系统设计 ···············································································122
6.4 系统部署及系统测试验证 ···················································123
6.5 批量生产 ···············································································125
6.5.1 批量节目元数据信息检索与筛选······························125
6.5.2 基于 FFmpeg 的 SDK 抽取视频 I 帧 ··························126
6.5.3 基于肤色比例检测的快速筛查··································128
6.5.4 基于 Caffe 框架的不良图片检测································128
6.6 小结 ·······················································································129
第 7 章集群部署与运营维护 ··························································· 131
7.1 认识 Docker···········································································131
7.2 基于 Docker 的 TensorFlow 实验环境··································134
7.3 运营维护 ···············································································137
7.4 小结 ·······················································································138
参考文献································································································139
· · · · · · (收起)

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这本新近出版的著作，聚焦于一个极为前沿且富有挑战性的交叉领域——深度学习在动态媒体处理中的创新应用与实际部署，无疑为我们揭示了未来交互式内容体验的巨大潜力。作者以其深厚的理论功底和丰富的工程实践经验为基础，系统地梳理了从基础卷积网络（CNNs）到复杂的循环神经网络（RNNs）乃至更为先进的Transformer架构，在处理视频序列、实时音频流和高维时间序列数据时的独特优势与局限。书中对特征提取与时序建模的探讨尤为精到，尤其是在处理非结构化、高带宽的动态数据流时，如何优化模型结构以平衡计算效率与识别精度，这对于希望将实验室成果转化为实际产品的工程师而言，提供了宝贵的路线图。此外，对于数据增强策略在动态场景下的特殊考量，例如如何有效模拟运动模糊、光照变化对序列帧的影响，以及如何构建大规模、多模态的动态数据集，都有着深入的剖析，这些细节往往是初学者容易忽略却至关重要的环节。整体来看，它不仅仅是理论的堆砌，更像是为有志于此领域的实践者准备的一份详尽的技术手册，旨在跨越理论与工业应用的鸿沟。

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对于那些已经具备一定机器学习基础，但希望将知识体系扩展到“时间维度”的开发者来说，这本书无疑是量身定做的进阶读物。它成功地架设了从静态图像处理到动态视频理解之间的桥梁。书中对时间建模的数学基础，如光学流估计的深度网络替代方案，以及如何利用RNN/LSTM处理不规则时间采样数据的策略，都有着深入浅出的讲解。让我印象深刻的是关于视频摘要（Video Summarization）这一主题的讨论，作者不仅涵盖了基于内容重要性的抽取式摘要，还涉及到了更具挑战性的叙事驱动型生成式摘要。这种对应用场景颗粒度的细分处理，使得读者能够针对特定目标选择最合适的深度学习范式。总而言之，本书的结构严谨，内容层层递进，是帮助专业人士实现技术栈升级的有力工具，它引导我们思考的不再是“网络能学什么”，而是“网络如何高效地‘观看’和‘理解’世界在时间中的变化”。

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初次翻阅这本书时，我最大的感受是其对“实践”二字的强调，这使得它区别于许多偏重数学推导的纯学术教材。作者似乎非常清楚当前行业中‘模型即服务’（MaaS）架构的复杂性，因此在后续章节中，对边缘计算设备上的模型压缩、量化技术以及模型部署流水线（MLOps for Media）的讨论显得尤为贴心。例如，书中详尽介绍了如何利用知识蒸馏（Knowledge Distillation）将大型时空网络压缩至能在移动端GPU上实时运行，同时保持关键识别指标的下降控制在可接受的范围内。这种对资源受限环境的关注，体现了作者对现实世界部署挑战的深刻理解。此外，对于实时互动性在动态媒体中的核心地位，书中深入探讨了低延迟推理框架的选择，包括对TensorRT、OpenVINO等硬件加速库的适配性分析，并辅以具体的代码片段和性能基准测试。这种从算法设计到硬件加速的“全栈式”讲解，极大地提升了本书的实用价值，使得读者在阅读过程中就能构建起完整的工程化思维框架。

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本书的视觉呈现和排版质量同样令人赞叹，这对于一本技术书籍来说至关重要，因为复杂的网络结构图和数据流向图的清晰度直接影响了学习效率。作者在解释模型架构时，经常使用精心设计的流程图来代替大段文字描述，例如，对3D卷积核的分解和时间维度上的扩展过程，通过图示可以一目了然。我发现作者在引用最新的顶级会议（如CVPR、ICCV、ECCV等）前沿工作方面做得非常出色，确保了内容的时效性。然而，真正让这本书脱颖而出的，是它对“数据驱动的系统鲁棒性”的强调。在动态媒体领域，数据的噪声和漂移是常态，书中关于领域适应性（Domain Adaptation）在视频跟踪和识别任务中的应用介绍，为我们应对真实世界复杂环境下的模型漂移问题提供了系统化的解决方案思路，而非仅仅提供一个孤立的算法，这体现了作者超越单一模型局限性的宏观视野。

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这本书的叙事节奏把握得非常到位，它没有陷入过分冗长且抽象的数学证明中，而是巧妙地将复杂的深度学习概念嵌入到具体的应用案例之中进行阐释。特别是关于行为识别与场景理解的章节，作者并没有仅仅停留在传统的分类任务上，而是引入了更具挑战性的多主体交互分析和意图预测模型。我尤其欣赏作者在介绍新颖模型结构时所采用的对比分析方法，例如，对比了纯粹基于卷积的时空块与引入自注意力机制的时空注意力模块在捕捉长距离依赖性方面的表现差异。这种比较论证方式，使得读者能够清晰地辨别不同技术选择背后的性能权衡。更值得称赞的是，本书似乎非常注重伦理与安全维度，在讨论生成对抗网络（GANs）或扩散模型在动态媒体内容生成时的潜力时，也同步提及了深度伪造（Deepfake）检测的对抗性防御技术，显示出作者对技术双刃剑效应的审慎态度，这在当前的技术热点中是极其必要的补充。

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