Contourbased
Contour-based approaches refer to a set of techniques in computer vision and image processing that extract, analyze, and utilize geometric shapes defined by the outlines or edges of objects within images. Unlike pixel‑wise or region‑based methods, contour-based methods focus on the boundary information that often captures the essential spatial structure and form of an object, making them particularly useful when precise shape representation is required.
Early contour detection relied on edge detectors such as Sobel, Prewitt, or Canny operators, which compute gradients
Contour-based feature extraction can be combined with machine learning pipelines. For instance, Shape Context descriptors capture
Current research trends focus on integrating contour-based methods with deep learning. Neural networks can learn to