مقایسهٔ روش‌ها

	روش لوکاس-کاناده برای جریان نوری	نظریه مقیاس-فضا
حوزه	بینایی ماشین	بینایی ماشین
خانواده	Machine learning	Machine learning
سال پیدایش	1981	1983
پدیدآور	Bruce Lucas and Takeo Kanade	Andrew Witkin and Tony Lindeberg
نوع	Optical flow and tracking	Theoretical framework for multi-scale processing
منبع بنیادین	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. (1994). Scale-space theory: A basic tool for analyzing structures at different scales. Journal of Applied Statistics, 21(2), 225–270.
نام‌های دیگر	Lucas-Kanade method, Sparse optical flow	Multi-scale analysis, Gaussian scale-space
مرتبط	5	5
خلاصه	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.	Scale-space theory, developed by Witkin and Lindeberg, provides a principled mathematical framework for analyzing images at multiple scales simultaneously. By treating scale as an explicit dimension and using Gaussian blurring, scale-space theory enables detection and analysis of features at appropriate scales, solving the fundamental problem of 'which scale should I analyze at?'