Covers edge and contour detection in images, including gradient-based methods, Laplacian operator, and more complex methods.
Introduces the basics of edge detection, including measuring contrast, gradient images, Fourier interpretation, Gaussian functions, Canny edge detector, and industrial applications.
Explores techniques for delineation, including Hough transform, gradient orientation, and shape detection, emphasizing the importance of combining graph-based techniques and machine learning.
Covers the basics of edge detection, including edge duality, discontinuities, descriptors, gradients, and magnitude measurement.
Delves into choosing a proper feature size for image analysis in life sciences, presenting a Rule Of Thumb for defining object size in pixels.
