Chapter 1, Introduction  Introduction to Digital Image Processing Low level digital image processing Image Acquisition Practical Experiments in basic image processing
	Chapter 2, The Digitized Image and its Properties  2.1 Basic Concepts

	Image functions
	The Dirac distribution and convolution
	The Fourier transform
	Images as a stochastic process
	Images as linear systems

2.2 Image digitization Sampling  Quantization Color images

2.3 Digital image properties Metric and topological properties of digital images Histograms Visual perception of the image Image quality Noise in images

Chapter 3, Data Structures for Image Analysis

	3.1 Levels of image data representation
	3.2 Traditional image data structures Matrices Chains Topological data structures Relational structures
	3.3 Hierarchical data structures Pyramids Quadtrees

Chapter 4, Image Pre-processing

	4.1 Pixel brightness transformation Position-dependent brightness correction  Grey scale transformation
	4.2 Geometric transformations Pixel co-ordinate transformations Brightness interpolation
	4.3 Local pre-processing Image Smoothing Edge detectors  Zero crossings of the second derivative  Scale in image processing Canny edge detection  Edges in multispectral images Other local pre-processing operators Adaptive neighborhood pre-processing
	4.4 Image restoration Image restoration as inverse convolution of the whole image Degradations that are easy to restore  Inverse filtration  Wiener filtration

	Chapter 5, Segmentation  5.1 Thresholding  Threshold detection methods  Multispectral thresholding Thresholding in hierarchical data structures 5.2 Edge-based segmentation Edge image thresholding Edge relaxation Border tracing Edge following as graph searching Edge following as dynamic programming Hough transforms Border detection using border location information Region construction from borders 5.3 Region growing segmentation Region merging Region splitting Splitting and merging 5.4 Matching Matching criteria Control strategies of matching 5.5 Advanced optimal border and surface detection approaches Simultaneous detection of border pairs Optimal surface detection
	Chapter 6, Shape Representation and Description  6.1 Region identification 6.2 Contour-based shape representation and description Chain codes Simple geometric shape representation Fourier transforms of borders Boundary description using segment sequences B-spline representation Other contour-based shape description approaches Shape invariants 6.3 Region-based shape representation and description Simple scalar region descriptors Moments Convex hull Graph representation based on region skeleton Region decomposition Region neighborhood graphs 6.4 Shape classes
	Chapter 11: Mathematical Morphology
	Chapter 12, Linear Discrete Image Transforms 12.1 Basic theory 12.2 The Fourier transform 12.3 Hadamard transform 12.4 Discrete cosine transform 12.5 Wavelets 12.6 Other discrete image transforms 12.7 Applications of discrete image transforms
	Chapter 13, Image Data Compression 13.1 Image data properties 13.2 Discrete image transforms in image data compression 13.3 Predictive compression methods 13.4 Vector quantization 13.5 Hierarchical and progressive compression methods 13.6 Comparison of compression methods 13.7 Other techniques 13.8 Coding 13.9 JPEG and MPEG image compression
	Chapter 14, Texture  14.1 Statistical texture description 14.2 Syntactic texture description 14.3 Hybrid texture description methods 14.4 Texture recognition method applications