Comparar métodos

	Detección de esquinas de Harris	Detección de Blobs
Campo	Visión por computador	Visión por computador
Familia	Machine learning	Machine learning
Año de origen	1988	1998
Autor original	Chris Harris and Mike Stephens	Tony Lindeberg
Tipo	Interest point detector	Multi-scale feature detection
Fuente seminal	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
Relacionados	5	5
Resumen	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.