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简介
机器视觉(或称计算机视觉)领域的研究博大精深，而且日新月异，对子具体视觉应用系统的设计人员和用户来说，该从何着手呢?本书是机器视觉领域的一本入门教材，详细介绍了基本概念，并辅以必要的数学知识，用较大篇幅来讲解如何在实际应用中实现和使用视觉算法，同时强调了技术的工程层面。本书有意省略了机器视觉中某些没有充分实际应用的理论。
本书可以作为高校相关专业的教材，也适合希望应用机器视觉来解决实际问题的各类人员阅读。
ramesh jain创建了密歇根大学的人工智能实验室，目前是加利福尼亚大学圣迭戈分校(ucsd)电气和计算机工程。计算机科学和工程系的教授。他的研究方向是多媒体信息系统。图像数据库。机器视觉和智能系统。他是《ieee multimedia》杂志的主编，《machine vision and application》。《pattern recognition》和《image and vision computing》杂志编委会成员，还是ieee和aaai的特别会员，acm的会员。
rangachar kasturi 于得克萨斯技术大学获得博士学位之后到宾夕法尼亚州立大学执教。他的主要研究方向是文档图像分析(dia)。他是《ieee transactions on pattern analysis and machineintelligence》杂志的主编。
brian g．schunck于加州大学欧文分校获得学士学位，子麻省理工学院获硕士和博士学位。他目前是密歇根大学安阿伯分校电子工程与计算机科学系副教授，近年来一直从事机器视觉和图像处理系统的开发工作。
目录
preface
acknowledgments
introduction
1.1 machine vision
1.2 relationships to other fields
1.3 role of knowledge
1.4 image geometry
1.4.1 perspective projection
1.4.2 coordinate systems
1.5 sampling and quantization
1.6 image definitions
1.7 levels of computation
1.7.1 point level
1.7.2 local level
1.7.3 global level
1.7.4 object level
1.8 road map
2 binary image processing
2.1 thresholding
2.2 geometric properties
.2.2.1 size
2.2.2 position
2.2.3 orientation
2.3 projections
2.4 run-length encoding
2.5 binary algorithms
2.5.1 definitions
2.5.2 component labeling
2.5.3 size filter
2.5.4 euler number
2.5.5 region boundary
2.5.6 area and perimeter
2.5.7 compactness
2.5.8 distance measures
2.5.9 distance transforms
2.5.10 medial axis
2.5.11 thinning
2.5.12 expanding and shrinking
2.6 morphological operators
2.7 optical character recognition
3 regions
3.1 regions and edges
3.2 region segmentation
3.2.1 automatic thresholding
3.2.2 limitations of histogram methods
3.3 region representation
3.3.1 array representation
3.3.2 hierarchical representations
3.3.3 region characteristic-based representations
3.3.4 data structures for segmentation
3.4 split and merge
3.4.1 region merging
3.4.2 removing weak edges
3.4.3 region splitting
3.4.4 split and merge
3.5 region growing
4 image filtering
4.1 histogram modification
4.2 linear systems
4.3 linear filters
4.4 median filter
4.5 gaussian smoothing
4.5.1 rotational symmetry
4.5.2 fourier transform property
4.5.3 gaussian separability
4.5.4 cascading gaussians
4.5.5 designing gaussian filters
4.5.6 discrete ganssian filters
5 edge detection
5.1 gradient
5.2 steps in edge detection
5.2.1 roberts operator
5.2.2 sobel operator
5.2.3 prewitt operator
5.2.4 comparison
5.3 second derivative operators
5.3.1 laplacian operator
5.3.2 second directional derivative
5.4 laplacian of gaussian
5.5 image approximation
5.6 gaussian edge detection
5.6.1 canny edge detector
5.7 subpixel location estimation
5.8 edge detector performance
5.8.1 methods for evaluating performance
5.8.2 figure of merit
5.9 sequential methods
5.10 line detection
6 contours
6.1 geometry of curves
6.2 digital curves
6.2.1 chain codes
6.2.2 slope representation
6.2.3 slope density function
6.3 curve fitting
6.4 polyline representation
6.4.1 polyline splitting
6.4.2 segment merging
6.4.3 split and merge
6.4.4 hop-along algorithm
6.5 circular arcs
6.6 conic sections
6.7 spline curves
6.8 curve approximation
6.8.1 total regression
6.8.2 estimating corners
6.8.3 robust regression
6.8.4 hough transform
6.9 fourier descriptors
7 texture
7.1 introduction
7.2 statistical methods of texture analysis
7.3 structural analysis of ordered texture
7.4 model-based methods for texture analysis
7.5 shape from texture
8 optics
8.1 lens equation
8.2 image resolution
8.3 depth of field
8.4 view volume
8.5 exposure
9 shading
9.1 image irradiance
9.1.1 illumination
9.1.2 reflectance
9.2 surface orientation
9.3 the reflectance map
9.3.1 diffuse reflectance
9.4 shape from shading
9.5 photometric stereo
l0 color
10.1 color physics
10.2 color terminology
10.3 color perception
10.4 color processing
10.5 color constancy
10.6 discussion
11 depth
11.1 stereo imaging
11.1.1. cameras in arbitrary position and orientation
11.2 stereo matching
11.2.1 edge matching
11.2.2 region correlation
11.3 shape from x
11.4 range imaging
11.4.1 structured lighting
11.4.2 imaging radar
11.5 active vision
12 calibration
12.1 coordinate systems
12.2 rigid body transformations
12.2.1 rotation matrices
12.2.2 axis of rotation
12.2.3 unit quaternions
12.3 absolute orientation
12.4 relative orientation
12.5 rectification
12.6 depth from binocular stereo
12.7 absolute orientation with scale
12.8 exterior orientation
12.8.1 calibration example
12.9 interior orientation
12.10 camera calibration
12.10.1 simple method for camera calibration
12.10.2 affine method for camera calibration
12.10.3 nonlinear method for camera calibration
12.11 binocular stereo calibration
12.12 active triangulation
12.13 robust methods
12.14 conclusions
13 curves and surfaces
13.1 fields
13.2 geometry of curves
13.3 geometry of surfaces
13.3.1 planes
13.3.2 differential geometry
13.4 curve representations
13.4.1 cubic spline curves
13.5 surface representations
13.5.1 polygonal meshes
13.5.2 surface patches
13.5.3 tensor-product surfaces
13.6 surface interpolation
13.6.1 triangular mesh interpolation
13.6.2 bilinear interpolation
13.6.3 robust interpolation
13.7 surface approximation
13.7.1 regression splines
13.7.2 variational methods
13.7.3 weighted spline approximation
13.8 surface segmentation
13.8.1 initial segmentation
13.8.2 extending surface patches
13.9 surface registration
14 dynamic vision
14.1 change detection
14.1.1 difference pictures
14.1.2 static segmentation and matching
14.2 segmentation using motion
14.2.1 time-varying edge detection
14.2.2 stationary camera
14.3 motion correspondence
14.4 image flow
14.4.1 computing image flow
14.4.2 feature-based methods
14.4.3 gradient-based methods
14.4.4 variational methods for image flow
14.4.5 robust computation of image flow
14.4.6 information in image flow
14.5 segmentation using a moving camera
14.5.1 ego-motion complex log mapping
14.5.2 depth determination
14.6 tracking
14.6.1 deviation function for path coherence
14.6.2 path coherence function
14.6.3 path coherence in the presence of occlusion
14.6.4 modified greedy exchange algorithm
14.7 shape from motion
object recognition
15.1 system components
15.2 complexity of object recognition
15.3 object representation
15.3.1 observer-centered representations
15.3.2 object-centered representations
15.4 feature detection
15.5 recognition strategies
15.5.1 classification
15.5.2 matching
15.5.3 feature indexing
15.6 verification
15.6.1 template matching
15.6.2 morphological approach
15.6.3 symbolic
15.6.4 analogical methods
a mathematical concepts
a.1 analytic geometry
a.2 linear algebra
a.3 variational calculus
a.4 numerical methods
b statistical methods
b.1 measurement errors
b.2 error distributions
b.3 linear regression
b.4 nonlinear regression
c programming techniques
c.1 image descriptors
c.2 mapping operators
c.3 image file formats
bibliography
index
acknowledgments
introduction
1.1 machine vision
1.2 relationships to other fields
1.3 role of knowledge
1.4 image geometry
1.4.1 perspective projection
1.4.2 coordinate systems
1.5 sampling and quantization
1.6 image definitions
1.7 levels of computation
1.7.1 point level
1.7.2 local level
1.7.3 global level
1.7.4 object level
1.8 road map
2 binary image processing
2.1 thresholding
2.2 geometric properties
.2.2.1 size
2.2.2 position
2.2.3 orientation
2.3 projections
2.4 run-length encoding
2.5 binary algorithms
2.5.1 definitions
2.5.2 component labeling
2.5.3 size filter
2.5.4 euler number
2.5.5 region boundary
2.5.6 area and perimeter
2.5.7 compactness
2.5.8 distance measures
2.5.9 distance transforms
2.5.10 medial axis
2.5.11 thinning
2.5.12 expanding and shrinking
2.6 morphological operators
2.7 optical character recognition
3 regions
3.1 regions and edges
3.2 region segmentation
3.2.1 automatic thresholding
3.2.2 limitations of histogram methods
3.3 region representation
3.3.1 array representation
3.3.2 hierarchical representations
3.3.3 region characteristic-based representations
3.3.4 data structures for segmentation
3.4 split and merge
3.4.1 region merging
3.4.2 removing weak edges
3.4.3 region splitting
3.4.4 split and merge
3.5 region growing
4 image filtering
4.1 histogram modification
4.2 linear systems
4.3 linear filters
4.4 median filter
4.5 gaussian smoothing
4.5.1 rotational symmetry
4.5.2 fourier transform property
4.5.3 gaussian separability
4.5.4 cascading gaussians
4.5.5 designing gaussian filters
4.5.6 discrete ganssian filters
5 edge detection
5.1 gradient
5.2 steps in edge detection
5.2.1 roberts operator
5.2.2 sobel operator
5.2.3 prewitt operator
5.2.4 comparison
5.3 second derivative operators
5.3.1 laplacian operator
5.3.2 second directional derivative
5.4 laplacian of gaussian
5.5 image approximation
5.6 gaussian edge detection
5.6.1 canny edge detector
5.7 subpixel location estimation
5.8 edge detector performance
5.8.1 methods for evaluating performance
5.8.2 figure of merit
5.9 sequential methods
5.10 line detection
6 contours
6.1 geometry of curves
6.2 digital curves
6.2.1 chain codes
6.2.2 slope representation
6.2.3 slope density function
6.3 curve fitting
6.4 polyline representation
6.4.1 polyline splitting
6.4.2 segment merging
6.4.3 split and merge
6.4.4 hop-along algorithm
6.5 circular arcs
6.6 conic sections
6.7 spline curves
6.8 curve approximation
6.8.1 total regression
6.8.2 estimating corners
6.8.3 robust regression
6.8.4 hough transform
6.9 fourier descriptors
7 texture
7.1 introduction
7.2 statistical methods of texture analysis
7.3 structural analysis of ordered texture
7.4 model-based methods for texture analysis
7.5 shape from texture
8 optics
8.1 lens equation
8.2 image resolution
8.3 depth of field
8.4 view volume
8.5 exposure
9 shading
9.1 image irradiance
9.1.1 illumination
9.1.2 reflectance
9.2 surface orientation
9.3 the reflectance map
9.3.1 diffuse reflectance
9.4 shape from shading
9.5 photometric stereo
l0 color
10.1 color physics
10.2 color terminology
10.3 color perception
10.4 color processing
10.5 color constancy
10.6 discussion
11 depth
11.1 stereo imaging
11.1.1. cameras in arbitrary position and orientation
11.2 stereo matching
11.2.1 edge matching
11.2.2 region correlation
11.3 shape from x
11.4 range imaging
11.4.1 structured lighting
11.4.2 imaging radar
11.5 active vision
12 calibration
12.1 coordinate systems
12.2 rigid body transformations
12.2.1 rotation matrices
12.2.2 axis of rotation
12.2.3 unit quaternions
12.3 absolute orientation
12.4 relative orientation
12.5 rectification
12.6 depth from binocular stereo
12.7 absolute orientation with scale
12.8 exterior orientation
12.8.1 calibration example
12.9 interior orientation
12.10 camera calibration
12.10.1 simple method for camera calibration
12.10.2 affine method for camera calibration
12.10.3 nonlinear method for camera calibration
12.11 binocular stereo calibration
12.12 active triangulation
12.13 robust methods
12.14 conclusions
13 curves and surfaces
13.1 fields
13.2 geometry of curves
13.3 geometry of surfaces
13.3.1 planes
13.3.2 differential geometry
13.4 curve representations
13.4.1 cubic spline curves
13.5 surface representations
13.5.1 polygonal meshes
13.5.2 surface patches
13.5.3 tensor-product surfaces
13.6 surface interpolation
13.6.1 triangular mesh interpolation
13.6.2 bilinear interpolation
13.6.3 robust interpolation
13.7 surface approximation
13.7.1 regression splines
13.7.2 variational methods
13.7.3 weighted spline approximation
13.8 surface segmentation
13.8.1 initial segmentation
13.8.2 extending surface patches
13.9 surface registration
14 dynamic vision
14.1 change detection
14.1.1 difference pictures
14.1.2 static segmentation and matching
14.2 segmentation using motion
14.2.1 time-varying edge detection
14.2.2 stationary camera
14.3 motion correspondence
14.4 image flow
14.4.1 computing image flow
14.4.2 feature-based methods
14.4.3 gradient-based methods
14.4.4 variational methods for image flow
14.4.5 robust computation of image flow
14.4.6 information in image flow
14.5 segmentation using a moving camera
14.5.1 ego-motion complex log mapping
14.5.2 depth determination
14.6 tracking
14.6.1 deviation function for path coherence
14.6.2 path coherence function
14.6.3 path coherence in the presence of occlusion
14.6.4 modified greedy exchange algorithm
14.7 shape from motion
object recognition
15.1 system components
15.2 complexity of object recognition
15.3 object representation
15.3.1 observer-centered representations
15.3.2 object-centered representations
15.4 feature detection
15.5 recognition strategies
15.5.1 classification
15.5.2 matching
15.5.3 feature indexing
15.6 verification
15.6.1 template matching
15.6.2 morphological approach
15.6.3 symbolic
15.6.4 analogical methods
a mathematical concepts
a.1 analytic geometry
a.2 linear algebra
a.3 variational calculus
a.4 numerical methods
b statistical methods
b.1 measurement errors
b.2 error distributions
b.3 linear regression
b.4 nonlinear regression
c programming techniques
c.1 image descriptors
c.2 mapping operators
c.3 image file formats
bibliography
index
机器视觉.英文版
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