Comparer des méthodes

	Détection de coins de Harris	Détection de blobs
Domaine	Vision par ordinateur	Vision par ordinateur
Famille	Machine learning	Machine learning
Année d'origine	1988	1998
Auteur d'origine	Chris Harris and Mike Stephens	Tony Lindeberg
Type	Interest point detector	Multi-scale feature detection
Source fondatrice	Harris, C., & Stephens, M. (1988). A combined corner and edge detector. Alvey Vision Conference, 147–152.	Lindeberg, T. (1998). Feature detection with automatic scale selection. International Journal of Computer Vision, 30(2), 79–116.
Alias	Harris Corner Detector, Harris-Stephens Detector, Plessey Operator	Connected component analysis, Region-based detection
Apparentées	5	5
Résumé	The Harris corner detector, introduced by Chris Harris and Mike Stephens in 1988, is a foundational method for identifying corners and interest points in digital images. Harris corners are points where two edges meet at a significant angle, making them stable and repeatable features for image analysis, matching, and 3D reconstruction.	Blob detection is a technique for identifying regions of interest (blobs)—connected, homogeneous areas that differ from their surroundings—at multiple scales. Introduced by Lindeberg in the context of scale-space theory, blob detection automatically finds and characterizes circular or elliptical objects without requiring a priori knowledge of their size.