Texture Analysis: Statistical and Structural Approaches

This lecture covers the concepts of texture in images, focusing on both statistical and structural properties. It begins by defining texture and discussing its homogeneity, emphasizing that statistical properties can be homogeneous even when pixel values are not. The instructor explains texture-based segmentation, aiming to assign similar texture values to individual pixels. The lecture further explores the differences between structural and statistical textures, highlighting the challenges in segmenting texels in real images. Various methods for creating textural images are presented, including the use of feature vectors and classification algorithms. The discussion then shifts to texture analysis techniques, including spectral metrics and statistical measures, such as co-occurrence matrices. The lecture concludes with an overview of machine learning applications in texture classification, particularly using Gabor filters and convolutional neural networks, demonstrating how these techniques can enhance texture analysis and classification tasks.