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Jingdong book

计算机视觉：算法与应用（套装共2册）

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Jingdong book

计算机视觉：算法与应用（套装共2册）

Name: 计算机视觉：算法与应用（套装共2册）
Brand: Jingdong book
SKU: 3101000251
Price: 38.15 USD
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《计算机视觉：算法与应用》主题和特色：
编排结构有利于活跃课堂气氛，适合面向项目的课程，针对各种特定课程提供了《计算机视觉：算法与应用》使用提示各章末尾的习题着重强调对算法的测试，重点包含大量针对小型期中课题的建议。
附录中提供额外的补充材料和更详细的数学知识介绍，包括线性代数、数值方法和贝时斯估计理论完整的参考文献和各章的补充阅读，全面覆盖各个子领域的新研究进展和成果。

Content Description

《计算机视觉：算法与应用》探索了用于分析和解释图像的各种常用技术，描述了具有一定挑战性的视觉应用方面的成功实例，兼顾专业的医学成像和图像编辑与拼接之类有趣的大众应用，以便学生能够将其应用于自己的照片和视频，从中获得成就感和乐趣。《计算机视觉：算法与应用》从科学的角度介绍基本的视觉问题，将成像过程的物理模型公式化，然后在此基础上生成对场景的逼真描述。作者还运用统计模型来分析和运用严格的工程方法来解决这些问题。
《计算机视觉：算法与应用》作为本科生和研究生“计算机视觉”课程的理想教材，适合计算机和电子工程专业学生使用，重点介绍现实中行之有效的基本技术，通过大量应用和练习来鼓励学生大胆创新。此外，本书的精心设计和编排，使其可以作为计算机视觉领域中一本独特的基础技术参考和新研究成果文献。

Author Description

Richard Szeliski博士，计算机视觉领域的大师级人物，Szeliski博士在计算机视觉研究方面有25年以上的丰富经验，先后任职于DEC和微软研究院1996年，他在微软研究院任职期间，提出一种基于运动的全景图像拼接模型，采用L-M算法，通过求图像间的几何变换关系来进行图像匹配，此方法是图像拼接领域的经典算法，Richard Szel iski也因此成为图像拼接领域的奠基人。

艾海舟博士，清华大学计算机系教授，IEEE高级会员和IEEE CS会员。先后于1985年、1988年和1991年获得清华大学工学学士、硕士和博士学位，博士论文荣获“1992年度清华大学优秀博士论文”奖1994-1996年，在比利时布鲁塞尔自由大学做博士后研究。目前，在清华大学教授“数字图像处理”和“计算机视觉”课程，研究方向为计算机视觉与模式识别。发表学术论文80余篇。

Catalogue

第1章概述
1.1 什么是计算机视觉？
1.2 简史
1.3 本书概述
1.4 课程大纲样例
1.5 标记法说明
1.6 扩展阅读

第2章图像形成
2.1 几何基元和变换
2.1.1 几何基元
2.1.2 2D变换
2.1.3 3D变换
2.1.4 3D旋转
2.1.5 3D到2D投影
2.1.6 镜头畸变
2.2 光度测定学的图像形成
2.2.1 照明
2.2.2 反射和阴影
2.2.3 光学
2.3 数字摄像机
2.3.1 采样与混叠
2.3.2 色彩
2.3.3 压缩
2.4 补充阅读
2.5 习题

第3章图像处理
3.1 点算子
3.1.1 像素变换
3.1.2 彩色变换
3.1.3 合成与抠图
3.1.4 直方图均衡化
3.1.5 应用：色调调整
3.2 线性滤波
3.2.1 可分离的滤波
3.2.2 线性滤波示例
3.2.3 带通和导向滤波器
3.3 更多的邻域算子
3.3.1 非线性滤波
3.3.2 形态学
3.3.3 距离变换
3.3.4 连通量
3.4 傅里叶变换
3.4.1 傅里叶变换对
3.4.2 二维傅里叶变换
3.4.3 维纳滤波
3.4.4 应用：锐化，模糊和去噪
3.5 金字塔与小波
3.5.1 插值
3.5.2 降采样
3.5.3 多分辨率表达
3.5.4 小波
3.5.5 应用：图像融合
3.6 几何变换
3.6.1 参数化变换
3.6.2 基于网格的卷绕
3.6.3 应用：基于特征的变形
3.7 全局优化
3.7.1 正则化
3.7.2 马尔科夫随机场
3.7.3 应用：图像的恢复
3.8 补充阅读
3.9 习题

第4章特征检测与匹配
4.1 点和块
4.1.1 特征检测器
4.1.2 特征描述子
4.1.3 特征匹配
4.1.4 特征跟踪
4.1.5 应用：表演驱动的动画
4.2 边缘
4.2.1 边缘检测
4.2.2 边缘连接
4.2.3 应用：边缘编辑和增强
4.3 线条
4.3.1 逐次近似
4.3.2 Hough变换
4.3.3 消失点
4.3.4 应用：矩形检测
4.4 扩展阅读
4.5 习题

第5章分割
5.1 活动轮廓
5.1.1 蛇行
5.1.2 动态蛇行和CONDENSATION
5.1.3 剪刀
5.1.4 水平集
5.1.5 应用：轮廓跟踪和转描机
5.2 分裂与归并
5.2.1 分水岭
5.2.2 区域分裂（区分式聚类）
5.2.3 区域归并（凝聚式聚类）
5.2.4 基于图的分割
5.2.5 概率聚集
5.3 均值移位和模态发现
5.3.1 k-均值和高斯混合
5.3.2 均值移位
5.4 规范图割
5.5 图割和基于能量的方法
5.6 补充阅读
5.7 习题

第6章基于特征的配准
6.1 基于2D和3D特征的配准
6.1.1 使用最小二乘的2D配准
6.1.2 应用：全景图
6.1.3 迭代算法
6.1.4 鲁棒最小二乘和RANSAC
6.1.5 3D配准
6.2 姿态估计
6.2.1 线性算法
6.2.2 迭代算法
6.2.3 应用：增强现实
6.3 几何内参数标定
6.3.1 标定模式
6.3.2 消失点
6.3.3 应用：单视图测量学
6.3.4 旋转运动
6.3.5 径向畸变
6.4 补充阅读
6.5 习题

第7章由运动到结构
7.1 三角测量
7.2 二视图由运动到结构
7.2.1 投影（未标定的）重建
7.2.2 自标定
7.2.3 应用：视图变形
7.3 因子分解
7.3.1 透视与投影因子分解
7.3.2 应用：稀疏3D模型提取
……
第8章稠密运动估计
第9章图像拼接
第10章计算摄影学
第11章立体视觉对应
第12章 3D重建
第13章基于图像的绘制
第14章识别
第15章结语
附录A 线性代数与数值方法
附录B 贝叶斯建模与推断
附录C 补充材料
词汇表

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