Introduces the basics of edge detection, including measuring contrast, gradient images, Fourier interpretation, Gaussian functions, Canny edge detector, and industrial applications.
Explores the analysis and classification of texture in images, emphasizing the role of Machine Learning techniques like Convolutional Neural Networks.
Explores techniques for delineation, including Hough transform, gradient orientation, and shape detection, emphasizing the importance of combining graph-based techniques and machine learning.
Covers edge and contour detection in images, including gradient-based methods, Laplacian operator, and more complex methods.
Covers the Fourier Transform, essential for analyzing stable LTI systems through complex exponentials.
