We explore the two defining eras of computer vision: how machines learn to interpret the visual world. We'll dive into Classical Computer Vision, a "human-guided" approach where experts meticulously design algorithms to detect explicit features like edges or corners, exemplified by techniques such as SIFT, SURF, and HOG. Then, we'll turn to the revolutionary Deep Learning paradigm, notably with Convolutional Neural Networks (CNNs), which are "data-driven" and learn to identify salient features directly from massive datasets, representing a profound shift from programming to training. We'll discuss this fundamental philosophical change from a deductive to an inductive approach, highlighting key trade-offs in data requirements, computational cost, and the crucial distinction between the transparent "white box" nature of classical algorithms and the often uninterpretable "black box" of deep learning models. Finally, we'll see how these paradigms translate into our daily lives, from SIFT-powered panorama stitching and HOG-based early pedestrian detection to CNNs driving facial recognition, autonomous vehicles, and medical image analysis, emphasizing that the choice between them is a strategic one, with a future likely dominated by intelligent hybrid models.

Please see https://tinyurl.com/SM-S1E3