1. Brief overview of ideas In this introductory lecture I will show short explanations of basic image processing methods In next lectures we will go into detail and mathematics, but this overview here is sufficient to start using software in your projects.

2. SPATIAL OPERATIONS

11. FEATURE EXTRACTION

12. Feature extraction Feature extraction involves finding features of the segmented image. Usually performed on a binary image produced from a thresholding operation. Common features include: 1. Area. 2. Perimeter. 3. Center of mass. 4. Compactness.

17. PATTERN CLASSIFICATION

20. Problems for students 1.Explain what are spatial operations. Give examples and examples of programs for them. 2.Explain what is 2D convolution in detail, using matrices. 3.Convolution with non-arithmetic operations. 4.Convolution in filtering. 5.Use of convolution in spatial averaging. Why one needs spatial averaging? 6.Edge detection by convolution. 7.Spectral techniques – main ideas. 8.General characteristics of methods used in image analysis for robotics applications. 9.Give examples and algorithms for segmentation. 10.Blob labeling. 11.Feature extraction 12.Relation about feature extraction and pattern recognition. 13.How to extract area of an object 14.How to calculate perimeter of an object. 15.What is compactness feature and how to measure it. Give one example of application. 16.Discuss the problem of pattern classification using feature space.