Filtering in Computer Vision - Lecture 2

Explore key concepts in computer vision through this comprehensive lecture on filtering. Delve into various image types, including binary, gray level, and color images with RGB channels. Examine image histograms and different noise types such as Gaussian, uniform distribution, and salt and pepper. Learn about analytical and discrete derivatives, including finite differences and derivatives in 2D. Investigate correlation, convolution, and linear filtering techniques, with a focus on Gaussian filters and their properties. Compare Gaussian and averaging filters for noise reduction, and understand the principle of linearity in filtering. Gain practical knowledge of MATLAB functions for implementing these concepts in image processing applications.