Ph.D. Thesis
[pdf version] [fractal analysis software]
preface
part 1: IMAGE PROCESSING TECHNIQUES
1.1. Basics of image formation
1.1.1. Image formation in SEM
1.1.2. Image formation in TEM
1.2. Image processing software
1.2.1. KS400
1.2.2. Iage processing/analysis software developed
1.2.3. Image processing with MathCAD and MatLab
1.3. Image storage and manipulation
1.3.1. Windows bitmap file format
1.3.2. Sun raster file format
1.4. Image enhancement
1.4.1. Gray level histogram modifications
1.4.2. Smoothing of noisy images
1.4.3. Sharpening
1.5. Image segmentation
1.5.1. Global thresholding using a correlation criterion
1.5.2. Local binarization using discrete convolution
1.5.3. Segmentation based on watershed transform
1.6. Processing of binary images
1.6.1. Image enhancement
Binary morphology
Shrink and swell filters
1.6.2. Contour following techniques
'Turtle' procedure
Crack following
Border following
1.6.3. Perimeter estimation by different yardsticks
1.6.4. Contour filling and object labeling
1.6.5. Watershed segmentation of touching objects
References
2.1. Functional approach
2.1.1. Contour functions
Cross-section functions for symmetric figures
Radius-vector functions
Support functions
Width function
Contour parametric and contour complex functions
Tangent-angle function
The intrinsic equation of the contour
Concluding remarks on contour functions
2.1.2. Application of contour functions to shape analysis
Invariant contour function parameters
Line moments and invariants
Approximation of contour functions by other simple functions
Fourier analysis of contour functions
Some other possible series expansions of contour functions
Multiscale shape analysis using continuous wavelet transform
Shape curvature scale space representation
2.2. Set theory approach
2.2.1. Simple geometrical shape parameters
2.2.2. Fractals in shape analysis
Definition of fractal dimension
References
3.1. Classification of individual fly ash and soil dust aerosol particles
3.1.1. Introduction
3.1.2. Types of shapes of aerosol particles
3.1.3. Fractal description of particle shapes: a brief overview
3.1.4. Experimental
3.1.5. Results and discussion
3.1.6. Conclusions
3.2. Differentiation between individual algae cells and their agglomerates
3.2.1. Introduction
3.2.2. Complex Fourier shape description
3.2.3. Classification algorithms
3.2.4. Experimental
3.2.5. Results and discussion
3.2.6. Conclusions
3.3. Classification of tabular grain silver halide microcrystals according to their shape
3.3.1. Introduction
3.3.2. Shape representation of the microcrystals
3.3.3. Reconstruction of the shape of overlapping microcrystals
Extraction of hexagonal and truncated triangular microcrystals
Extraction of triangular microcrystals
3.3.4. Classification of microcrystals via their shape descriptors
Nearest neighbor classification algorithms
Labeled samples and prototypes
3.3.5. Experimental
3.3.6. Results and discussion
3.3.7. Conclusions
3.4. On fractal dimension calculation
3.4.1. 'Hand and dividers' method: theory
3.4.2. 'Hand and dividers' method: practice
3.4.3. Problems associated with the 'hand and dividers' method
3.4.4. Analysis of the Richardson plot
3.5. Study of quasi-fractal many-particle systems and percolation networks
3.5.1. Introduction
3.5.2. Experimental
Samples and sample preparation and image acquisition
Image processing and analysis
3.5.3. Results and discussions
Ag colloids
Ag filament networks
3.5.4. Conclusions
References
SAMENVATTING
APPENDIX
A.1. Publications in refereed scientific journals and conference proceedings
A.2. Conference contributions
A.3. Research reports
Document is created by Volodymyr Kindratenko
Last modified: 08/22/99