Lecture
This lecture covers the limitations of the Canny algorithm, different boundary types, classification versus regression metrics, deep learning concepts such as weighted-fusion layers and error propagation paths, comparisons between deep learning and Canny edge detection, deeper learning stages, convolutional neural networks, and the evolution of edge detection over 50 years. The instructor discusses the challenges faced by convolution operators, the need for arbitrary thresholds and scale sizes, and the transition from contours to objects in edge detection.