Comparer des méthodes

	Détection de blobs	Segmentation par ligne de partage des eaux
Domaine	Vision par ordinateur	Vision par ordinateur
Famille	Machine learning	Machine learning
Année d'origine	1998	1979
Auteur d'origine	Tony Lindeberg	Serge Beucher and Christian Lantuéjoul
Type	Multi-scale feature detection	Morphological image segmentation
Source fondatrice	Lindeberg, T. (1998). Feature detection with automatic scale selection. International Journal of Computer Vision, 30(2), 79–116.	Meyer, F. (1994). Topographic distance and watershed lines. Signal Processing, 38(1), 113–125.
Alias	Connected component analysis, Region-based detection	Watershed transform, Water shedding segmentation
Apparentées	5	5
Résumé	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.	Watershed segmentation is a morphological image processing technique that automatically segments an image into distinct regions by treating image intensity as a topographic landscape where each object corresponds to a valley. Introduced by Beucher and Lantuéjoul in 1979 and refined by Meyer, the watershed algorithm is particularly effective for separating touching or overlapping objects.