Combinatorial Gradient Fields for 2D Images with Empirically Convergent Separatrices
This addresses a computational geometry problem for image analysis, offering an incremental improvement over existing algorithms with a probabilistic guarantee.
The paper tackles the problem of computing combinatorial gradient fields for 2D images by proposing a probabilistic method that ensures the Morse-Smale complex converges to its continuous counterpart as resolution increases, with numerical evaluation showing empirical convergence.
This paper proposes an efficient probabilistic method that computes combinatorial gradient fields for two dimensional image data. In contrast to existing algorithms, this approach yields a geometric Morse-Smale complex that converges almost surely to its continuous counterpart when the image resolution is increased. This approach is motivated using basic ideas from probability theory and builds upon an algorithm from discrete Morse theory with a strong mathematical foundation. While a formal proof is only hinted at, we do provide a thorough numerical evaluation of our method and compare it to established algorithms.