Compara mètodes

	Mètode de flux òptic de Lucas-Kanade	Detecció de blobs
Camp	Visió per computador	Visió per computador
Família	Machine learning	Machine learning
Any d'origen	1981	1998
Autor original	Bruce Lucas and Takeo Kanade	Tony Lindeberg
Tipus	Optical flow and tracking	Multi-scale feature detection
Font seminal	Lucas, B. D., & Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. Proceedings of the Seventh International Joint Conference on Artificial Intelligence (IJCAI), 674–679.	Lindeberg, T. (1998). Feature detection with automatic scale selection. International Journal of Computer Vision, 30(2), 79–116.
Àlies	Lucas-Kanade method, Sparse optical flow	Connected component analysis, Region-based detection
Relacionats	5	5
Resum	The Lucas-Kanade method, introduced by Bruce Lucas and Takeo Kanade in 1981, is a foundational technique for estimating optical flow—the apparent motion of objects in image sequences. By computing pixel-level motion vectors, the Lucas-Kanade algorithm tracks feature displacements between consecutive frames, enabling object tracking, motion estimation, and video analysis.	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.