Filtering in Computer Vision - Lecture 2

Explore filtering techniques in computer vision through this comprehensive lecture. Delve into various image types, including binary, grayscale, and color images, and understand their characteristics. Learn about image histograms and different types of noise, particularly Gaussian noise. Examine discrete derivatives, finite differences, and two-dimensional derivatives in image processing. Investigate correlation, convolution, and various filtering methods, with a focus on Gaussian filters and their properties. Compare blurring techniques and noise filtering approaches. Gain practical knowledge of MATLAB functions for image processing. Discover edge detection techniques, including the concept of edges, detecting discontinuities, and applying derivatives to images. Understand the relationship between derivatives and noise, and explore image smoothing methods. Study various edge detectors and their applications in computer vision.