计算机视觉：模型、学习和推理 / 计算机科学丛书
定价：￥119.00
作者： [英]西蒙 J.D.普林斯著；苗启广，刘凯，孔韦韦等译
译者：苗启广、刘凯等 译；
出版时间：2017-06-29
出版社：机械工业出版社
- 机械工业出版社
- 9787111516828
- 1-8
- 47229829-8
- 16开
- 2017-06-29
- 652
- 计算机通信类
- 本科
作者简介
内容简介
本书是一本从机器学习视角讲解计算机视觉的非常好的教材。全书图文并茂、语言浅显易懂，算法描述由浅入深，即使是数学背景不强的学生也能轻松理解和掌握。作者展示了如何使用训练数据来学习观察到的图像数据和我们希望预测的现实世界现象之间的联系，以及如何如何研究这些联系来从新的图像数据中作出新的推理。本书要求最少的前导知识，从介绍概率和模型的基础知识开始，接着给出让学生能够实现和修改来构建有用的视觉系统的实际示例。适合作为计算机视觉和机器学习的高年级本科生或研究生的教材，书中详细的方法演示和示例对于计算机视觉领域的专业人员也非常有用。
目录
Table of Contents
Part I. Probability:
1. Introduction to probability
2. Common probability distributions
3. Fitting probability models
4. The normal distribution
Part II. Machine Learning for Machine Vision:
5. Learning and inference in vision
6. Modeling complex data densities
7. Regression models
8. Classification models
Part III. Connecting Local Models:
9. Graphical models
10. Models for chains and trees
11. Models for grids
Part IV. Preprocessing:
12. Image preprocessing and feature extraction
Part V. Models for Geometry:
13. The pinhole camera
14. Models for transformations
15. Multiple cameras
Part VI. Models for Vision:
16. Models for style and identity
17. Temporal models
18. Models for visual words
Part VII. Appendices:
A. Optimization
B. Linear algebra
C. Algorithms.
Part I. Probability:
1. Introduction to probability
2. Common probability distributions
3. Fitting probability models
4. The normal distribution
Part II. Machine Learning for Machine Vision:
5. Learning and inference in vision
6. Modeling complex data densities
7. Regression models
8. Classification models
Part III. Connecting Local Models:
9. Graphical models
10. Models for chains and trees
11. Models for grids
Part IV. Preprocessing:
12. Image preprocessing and feature extraction
Part V. Models for Geometry:
13. The pinhole camera
14. Models for transformations
15. Multiple cameras
Part VI. Models for Vision:
16. Models for style and identity
17. Temporal models
18. Models for visual words
Part VII. Appendices:
A. Optimization
B. Linear algebra
C. Algorithms.
