Delves into convolutional filters as an inductive bias for images in neural networks, emphasizing independence to translation and local feature detectors.
Explores techniques for delineation, including Hough transform, gradient orientation, and shape detection, emphasizing the importance of combining graph-based techniques and machine learning.
Delves into the mathematical foundations and importance of directional cues in image processing, exploring computational challenges and selectivity to orientation.