hullbased

Hullbased is an expression used in mathematics, computer science, and related fields to describe approaches that rely on the convex hull of a set of points, or more generally on a hull that encloses a geometric object. The convex hull of a finite point set is the smallest convex set that contains all the points; many hullbased methods use this boundary as a geometric primitive for analysis, recognition, or planning.

In computational geometry, hullbased algorithms focus on computing or manipulating convex hulls; classic algorithms include the Graham scan and Quickhull, with extensions to higher dimensions. In data analysis and machine learning, hullbased techniques model normal data as occupying or being bounded by a convex hull in feature space. Outliers or novel observations are detected if they fall outside this hull, or if their distance to the hull exceeds a threshold. Such methods are simple and interpretable but can be sensitive to outliers themselves and may fail to represent nonconvex class boundaries.

In computer vision and graphics, hullbased shapes use the convex hull as a coarse representation of object silhouettes or point clouds; hulls are also used in collision detection and bounding volume hierarchies. In robotics and path planning, hullbased representations simplify obstacle shapes or configuration spaces, enabling efficient collision checks.

Limitations include the inability to capture concavities precisely, high computational cost in high dimensions, and distortion of the hull by outliers. See also convex hull, computational geometry, outlier detection, one-class classification, and bounding volume.