What makes this book unique is that besides information on image processing of objects to yield knowledge, the author has devoted a lot of thought to the measurement factor of image processing. This is of direct practical use in numerous sectors from industrial quality and robotics to medicine and biology.

Chapter 1: introduction
1.1 scene, image and modality
1.2 why use images?
1.3 computer vision, image processing and computer graphics
1.4 applications of image based measurement systems
1.5 organisation of the book
1.6 literature
Chapter 2: image formation
2.1 interaction between light and matter
2.1.1 radiometry and photometry
2.1.2 reflection models
2.2 projection
2.2.1 world and camera co-ordinates
2.2.2 pinhole camera model
2.2.3 homogeneous co-ordinates
2.2.4 point spread function
2.2.5 2-dimensional convolution
2.3 Fourier analysis
2.3.1 harmonic functions
2.3.2 the Fourier transform
2.4 image conditioning
2.4.1 front or rear illumination
2.4.2 specular or diffuse illumination
2.4.3 structured light
references
Chapter 3: image models
3.1 first order statistics
3.2 second order statistics
3.2.1 stochastic processes and 2-dimensional filtering
3.2.2 autocorrelation and power spectrum
3.3 discontinuities
references
Chapter 4: image acquisition
4.1 the ccd-camera
4.2 spatial sampling and reconstruction
4.2.1 impulse modulation
4.2.2 reconstruction
4.2.3 area sampling and presampling filtering
4.2.4 2-dimensional sampled stochastic processes
4.2.5 finiteness of the image plane
4.2.6 discrete Fourier transform
4.3 amplitude discretisation
references
Chapter 5: image operations
5.1 pixel-to-pixel operations
5.1.1 monadic operations
5.1.2 dyadic operations
5.2 linear operations
5.2.1 convolution
5.2.2 orthogonal transforms
5.2.3 differential operators
5.3 correlation techniques
5.4 morphological operations
5.4.1 definitions
5.4.2 basic operations
5.4.3 morphological operations
5.4.4 applications
5.4.5 grey scale morphology
references
Chapter 6: statistical pattern classification and parameter estimation
6.1 decision making and classification
6.1.1 Bayes classification
6.1.2 uniform cost function and minimum error rate
6.1.3 Gaussian random vectors
6.1.4 the 2-class case
6.1.5 rejection
6.2 parameter estimation
6.2.1 Bayes estimation
6.2.2 minimum variance estimation
6.2.3 map estimation
6.2.4 maximum likelihood estimation
6.2.5 least squares fitting and ml-estimation
6.2.6 simultaneous estimation and classification
6.2.7 properties of estimators
6.2.8 estimation of dynamically changing parameters
6.3 supervised learning
6.3.1 parametric learning
6.3.2 Parzen estimation
6.3.3 nearest neighbor classification
6.3.4 linear discriminant functions
6.3.5 generalised linear discriminant functions
6.4 performance measures
6.4.1 interclass and intraclass distance
6.4.2 Chernoff-Bhattacharyya distance
6.5 feature selection and extraction
6.5.1 performance and dimensionality
6.5.2 feature selection
6.5.3 linear feature extraction
references
Chapter 7: image analysis
7.1 image segmentation
7.1.1 pixel classification
7.1.2 region based segmentation
7.1.3 edge based segmentation
7.2 region properties
7.2.1 region parameters
7.2.2 contours
7.2.3 relational description
7.3 object recognition
7.3.1 object models
7.3.2 from object model to image model
7.3.3 matching techniques
7.4 estimation of 3-dimensional body parameters
references
Appendix A: topics selected from linear algebra and matrix theory
A.1 linear spaces
A.2 metric spaces
A.3 orthonormal systems and Fourier series
A.4 linear operators
A.5 vectors and matrices
A.6 trace and determinant
A.7 differentiation of vectors and matrices
A.8 diagonalisation of self-adjoint matrices
references
Appendix B: probability theory and stochastic processes
B.1 probability theory and random variables
B.2 bivariate random variables
B.3 random vectors
B.3.1 decorrelation
B.4 stochastic processes
B.4.1 stationarity and power spectrum
B.4.2 average, expectation and ergodicity
B.4.3 time-invariant linear systems
references
Bibliography
Permission source notes
Index

 

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