Lecture
This lecture covers interactive delineation using image gradients, Hough transform for curve detection, voting schemes, generic algorithms for parameter quantization, iris and circle detection, gradient orientation, and ellipse detection. It also explores generalized Hough for shape finding, R-tables for displacement vectors, real-time Hough, and the transition from delineation to detection. The instructor discusses training and testing processes, pedestrian detection, limitations, and various techniques like dynamic programming and deformable models. The lecture delves into magnitude, orientation, minimum spanning trees, and the transformation from image to roads. Optional topics include path classification, tree finding, and the application of deep learning in delineation. The lecture concludes with insights on the importance of combining graph-based techniques, machine learning, and semi-automated tools in image analysis.