to the instructor

acknowledgements

1 introduction

1.1 concerning this book

1.2 concerning prerequisites

1.3 some terminology

1.4 organization of a machine vision system

1.5 the nature of images

1.6 images: operations and analysis

reference

review of mathematical principles

2.1 a brief review of probability

2.2 a review of linear algebra

2.3 introduction to function minimization

2.4 markov models

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3 writing programs to process images

3.1 image file system (ifs) software

3.2 basic programming structure for image processing

3.3 good programming styles

.3.4 example programs

3.5 makefiles

4 images: formation and representation

4.1 image representations

4.2 the digital image

4.3 describing image formation

4.4 the image as a surface

4.5 neighborhood relations

4.6 conclusion

4.7 vocabulary

topic 4a image representations

4a.1 a variation on sampling: hexagonal pixels

4a.2 other types of iconic representations

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5 linear operators and kernels

5.1 what is a linear operator?

5.2 application of kernel operators in digital images

5.3 derivative estimation by function fitting

5.4 vector representations of images

5.5 basis vectors for images

5.6 edge detection

5.7 a kernel as a sampled differentiable function

5.8 computing convolutions

5.9 scale space

5.10 quantifying the accuracy of an edge detector

5.11 so how do people do it?

5.12 conclusion

5.13 vocabulary

topic 5a edge detectors

5a.1 the canny edge detector

5a.2 improvements to edge detection

5a.3 inferring line segments from edge points

5a.4 space/frequency representations

5a.5 vocabulary

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6 image relaxation: restoration and feature extraction

6.1 relaxation

6.2 restoration

6.3 the map approach

6.4 mean field annealing

6.5 conclusion

6.6 vocabulary

topic 6a alternative and equivalent algorithms

6a.1 gnc: an alternative algorithm for noise removal

6a.2 variable conductance diffusion

6a.3 edge-oriented anisotropic diffusion

6a.4 a common description of image relaxation operators

6a.5 relationship to neural networks

6a.6 conclusion

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7 mathematical morphology

7.1 binary morphology

7.2 gray-scale morphology

7.3 the distance transform

7.4 conclusion

7.5 vocabulary

topic 7a morphology

7a.1 computing erosion and dilation efficiently

7a.2 morphological sampling theorem

7a.3 choosing a structuring element

7a.4 closing gaps in edges and surfaces

7a.5 vocabulary

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8 segmentation

8.1 segmentation: partitioning an image

8.2 segmentation by thresholding

8.3 connected component analysis

8.4 segmentation of curves

8.5 active contours (snakes)

8.6 segmentation of surfaces

8.7 evaluating the quality of a segmentation

8.8 conclusion

8.9 vocabulary

topic 8a segmentation

8a.1 texture segmentation

8a.2 segmentation of images using edges

8a.3 motion segmentation

8a.4 color segmentation

8a.5 segmentation using map methods

8a.6 human segmentation

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shape

9.1 linear transformations

9.2 transformation methods based on the covariance matrix

9.3 simple features

9.4 moments

9.5 chain codes

9.6 fourier descriptors

9.7 the medial axis

9.8 deformable templates

9.9 quadric surfaces

9.10 surface harmonic representations

9.11 superquadrics and hyperquadrics

9.12 generalized cylinders (gcs)

9.13 conclusion

9.14 vocabulary

topic 9a shape description

9a.1 finding the diameter of nonconvex regions

9a.2 inferring 3d shape from images

9a.3 motion analysis and tracking

9a.4 vocabulary

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10 consistent labeling

10.1 consistency

10.2 relaxation labeling

10.3 conclusion

10.4 vocabulary

topic 10a 3d interpretation of 2d line drawings

references

11 parametric transforms

11.1 the hough transform

11.2 reducing computational complexity

11.3 finding circles

11.4 the generalized hough transform

11.5 conclusion

11.6 vocabulary

topic 11a parametric transforms

11a.1 finding parabolae

11a.2 finding the peak

11a.3 the gauss map

11a.4 parametric consistency in stereopsis

11a.5 conclusion

11a.6 vocabulary

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12 graphs and graph-theoretic concepts

12.1 graphs

12.2 properties of graphs

12.3 implementing graph structures

12.4 the region adjacency graph

12.5 using graph-matching: the subgraph isomorphism problem

12.6 aspect graphs

12.7 conclusion

12.8 vocabulary

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13 image matching

13.1 matching iconic representations

13.2 matching simple features

13.3 graph matching

13.4 conclusion

13.5 vocabulary

topic 13a matching

13a.1 springs and templates revisited

13a.2 neural networks for object recognition

13a.3 image indexing

13a.4 matching geometric invariants

13a.5 conclusion

13a.6 vocabulary

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14 statistical pattern recognition

14.1 design of a classifier

14.2 bayes' rule and the maximum likelihood classifier

14.3 decision regions and the probability of error

14.4 conditional risk

14.5 the quadratic classifier

14.6 the minimax rule

14.7 nearest neighbor methods

14.8 conclusion

14.9 vocabulary

topic 14a statistical pattern recognition

14a.1 matching feature vectors using statistical methods

14a.2 support vector machines (svms)

14a.3 conclusion

14a.4 vocabulary

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15 clustering

15.1 distances between clusters

15.2 clustering algorithms

15.3 optimization methods in clustering

15.4 conclusion

15.5 vocabulary

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16 syntactic pattern recognition

16.1 terminology

16.2 types of grammars

16.3 shape recognition using grannnatical structure

16.4 conclusion

16.5 vocabulary

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17 applications

17.1 multispectral image analysis

17.2 optical character recognition (ocr)

17.3 automated/assisted diagnosis

17.4 inspection/quality control

17.5 security and intruder identification

17.6 robot vision

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18 automatic target recognition

18.1 the hierarchy of levels of atr

18.2 atr system components

18.3 evaluating performance of atr algorithms

18.4 machine vision issues unique to atr

18.5 atr algorithms

18.6 the hough transform in atr

18.7 morphological techniques in atr

18.8 chain codes in atr

18.9 conclusion

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author index

index