Texture: Analysis and Classification

This lecture covers the concept of texture in images, distinguishing between structural and statistical textures, and the challenges in segmenting and detecting texture-based edges. It explains the use of textural metrics, including spectral and statistical metrics, and the application of the Discrete Fourier Transform for texture analysis. The lecture also delves into second-order measures for texture analysis, such as contrast and homogeneity, and the use of filter-based measures and Gabor filters. Additionally, it explores the role of Machine Learning in texture analysis, specifically through Convolutional Neural Networks, highlighting their effectiveness in characterizing and classifying textures.