Image Processing I

This lecture covers fundamental concepts in image processing, including preprocessing techniques such as histogram normalization and combining images through spatial averaging. It also delves into matching and detection methods like correlation and feature extraction, as well as segmentation approaches like variational thresholding. The instructor emphasizes the importance of tasks like contour detection and texture analysis, providing insights into the intricacies of graylevel histograms and linear contrast adjustments. Additionally, the lecture explores local normalization for compensating non-uniformities in images and spatial averaging methods for noise reduction and local statistics estimation. Practical examples and experiments are used to illustrate the concepts of template matching, correlation measures, and impulsive noise reduction.