Computer Vision: A Modern Approach 2/e
售價
$
1,250
- 一般書籍
- ISBN：9780273764144
- 作者：David A. Forsyth, Jean Ponce
- 版次：2
- 年份：2012
- 出版商：Pearson Education
- 頁數/規格：791頁/平裝單色
書籍介紹
本書特色
目錄
Description
This textbook provides the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications. Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods.
This textbook provides the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications. Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods.
Features
New to This Edition
- Broad coverage—Coverage of a wide range of topics allows customization to fit instructor, student, and course needs.
Allows instructors to select the most relevant topics for their students and encourages students to enrich their coursework by reading information on other computer vision topics. - Most comprehensive and up-to-date text on computer vision—Includes essential topics that either reflect practical significance or are of theoretical importance.
Provides students with the most coherent synthesis of current views and teaches them successful techniques for building applications. - Depth of the material accessible to various levels of students—Topics are discussed in substantial and increasing depth.
While the first half of each chapter is accessible to undergraduates, a good grasp of each chapter provides students with a professional level of skill and knowledge. - Application surveys—Describe numerous important application areas such as image based rendering and digital libraries.
Teaches students about practical use of techniques and helps them gain insight into the demands of applications. - Many important algorithms broken down and illustrated in pseudo code.
Enables students to build working systems easily as they can understand the construction of the final application. - Excellent pedagogy throughout the text—Includes numerous worked examples, exercises, programming assignments, and extensive illustrations.
Provides students with ample opportunity to apply the concepts in the text.
New to This Edition
- Every topic has been updated based on current research and trends in computer vision.
- Cleaner, clearer treatment of geometric computer vision, including camera calibration, stereopsis and structure from motion
- More application chapters, including image retrieval, human centric vision
- New section on object recognition, including a summary of recent research developments placed it in the context of an application.
Table of Contents
I IMAGE FORMATION
1 Geometric Camera Models
2 Light and Shading
3 Color
II EARLY VISION: JUST ONE IMAGE
4 Linear Filters
5 Local Image Features
6 Texture
III EARLY VISION: MULTIPLE IMAGES
7 Stereopsis
8 Structure from Motion
IV MID-LEVEL VISION
9 Segmentation by Clustering
10 Grouping and Model Fitting
11 Tracking
V HIGH-LEVEL VISION
12 Registration
13 Smooth Surfaces and Their Outlines
14 Range Data
15 Learning to Classify
16 Classifying Images
17 Detecting Objects in Images
18 Topics in Object Recognition
VI APPLICATIONS AND TOPICS
19 Image-Based Modeling and Rendering
20 Looking at People
21 Image Search and Retrieval
VII BACKGROUND MATERIAL
22 Optimization Techniques
I IMAGE FORMATION
1 Geometric Camera Models
2 Light and Shading
3 Color
II EARLY VISION: JUST ONE IMAGE
4 Linear Filters
5 Local Image Features
6 Texture
III EARLY VISION: MULTIPLE IMAGES
7 Stereopsis
8 Structure from Motion
IV MID-LEVEL VISION
9 Segmentation by Clustering
10 Grouping and Model Fitting
11 Tracking
V HIGH-LEVEL VISION
12 Registration
13 Smooth Surfaces and Their Outlines
14 Range Data
15 Learning to Classify
16 Classifying Images
17 Detecting Objects in Images
18 Topics in Object Recognition
VI APPLICATIONS AND TOPICS
19 Image-Based Modeling and Rendering
20 Looking at People
21 Image Search and Retrieval
VII BACKGROUND MATERIAL
22 Optimization Techniques
