1 day, morning 4 periods, afternoon 3 periods.
Chap 1: Introduction 2
Machine vision (MV). 3
Vision 4
MV applications. 5
Chap 2: Image acquisition and image properties. 2
Image acquisition. 2
Image sources. 3
Image properties. 8
Image 9
Camera image. 10
Imaging geometry. 11
Image sampling. 12
Beyond Nyquist 13
Image quantizing. 14
Information. 15
Image forming. 17
Signal distribution. 18
Histogram examples. 19
Signal to noise ratio. 20
Chap 3: Image correction and filtering. 2
Geometric corrections. 2
Geometric correction problem.. 3
Geometric correction procedure. 4
Geometric transform.. 5
Specific transformations. 6
Point operations. 8
Gray level mapping. 9
Changing contrast 10
Normalisation to maximum range. 11
Color mapping. 12
g(f) gamma. 13
Image math. 14
Example of image subtraction. 15
Spatial filtering. 16
Linear invariant systems. 17
Convolution with a two-dimensional (2D) kernel 19
Low pass or smoothing filters. 20
Separable kernel 21
High pass filters. 22
Exemples 24
Non-linear filtering. 25
Linear versus median filtering. 26
Chap 4: image matching. 2
Comparing images. 3
Template maching problem.. 4
Template maching procedure. 5
Similarity and dissimilarity measures. 6
Properties of similarity/dissimilarity measures. 7
Application. 8
Chap 5: thresholding and segmentation. 3
Thresholding. 3
Thresholding. 4
Examples 6
Example of thresholding a multidimensional color image. 7
Segmentation. 8
Neighborhood. 9
Path C 10
Region and connex region. 11
Segmentation and partition. 12
Homogeneity. 13
Contour and region. 14
Blob coloring. 15
Example 17
Segmentation of gray-level images. 18
Graph based region growing. 19
Split and merge. 20
Edge detection. 21
Edge of one dimensional f(x) 22
Edge of f(x,y) 23
Differential gradient (DG) approach. 24
Example 25
Template matching (TM) approach. 26
Optimal edge detection. 27
Hysteresis thresholding. 28
Exemple 29
Laplacian approach. 30
Laplacien of smooth image. 31
Example 33
Contour modeling: snakes, marching cubes & level set methods 34
Chap 6: Mathematical morphology. 3
Binary morphology. 3
Binary operations. 4
Transformation by a structuring element 5
Erosion 6
Dilatation 7
Erosion and dilatation examples. 8
Duality of erosion and dilation. 9
Illustration 10
Iterative erosion and dilatation. 11
Opening 12
Closing 13
Duality of opening and closing. 14
Properties of erosion, dilation, opening and closing. 15
Examples 16
Applications of opening and closing. 17
Application: contour detection. 18
Opening as a size filter 19
Conditional and geodesic dilation. 20
Geodesic opening as size filter 22
Iterative size filter 23
Classes of structuring elements. 24
Example anisotropic filtering. 25
Gray level morphology. 27
Erosion and dilation. 28
Examples 29
Opening and closing. 31
Rank Order Based Filters ROBF. 32
Chap 7: features. 2
Contour features. 2
Contour following. 3
Contour representation. 5
Contour as a continuous parametric function. 6
Contour as a discrete parametric function. 7
Curvilinear (s) versus polar (x) features. 9
Problems with the centroid feature approach. 11
Comparison. 12
Shape features. 13
Simple features. 14
Application. 18
Moments of a binary shape. 19
Equivalent ellipse. 21
Signatures 22
Features for gray-level images. 23
First order features. 24
Second order features. 25
Texture descriptors. 26
Application. 27
Particle shape analysis. 28
Representation in feature space. 30
More features. 32
HIPR2
http://www.dai.ed.ac.uk/HIPR2
is a collection of free web-based tutorial materials on the 50 most commonly used image processing operators. Each operator has an individual JAVA exercise program, plus there is a JAVA-based Khoros-like drag and drop workspace tableau. It works under Internet Explorer and Mozilla as well as Netscape (Java 1.2 or higher is needed). HIPR2 just had its 65,000th user.
Books on-line
http://homepages.inf.ed.ac.uk/rbf/CVonline/books.htm
Image Processing Fundamentals
http://www.ph.tn.tudelft.nl/Courses/FIP/noframes/fip.html