[IPOL announce] new article: Image Segmentation using Backward Stochastic Differential Equations

[announcements about the IPOL journal]

A new article is available in IPOL: https://www.ipol.im/pub/art/2026/636/

Dariusz Borkowski,

Image Segmentation using Backward Stochastic Differential Equations,

Image Processing On Line, 16 (2026), pp. 138–155.

https://doi.org/10.5201/ipol.2026.636


Abstract
We introduce a novel image segmentation algorithm based on the 
methodology of approximating solutions to backward stochastic 
differential equations (BSDEs). The segmentation method repeats the BSDE 
reconstruction process, with the parameters of these equations changing 
in subsequent steps. We are interested in a sequence of images driven by 
BSDE solutions. As the segmentation result, we define the limit of these 
images. By their nature, stochastic tools, particularly the Monte Carlo 
method, have high computational complexity. There are concerns about the 
running time of the proposed method, especially if we are considering a 
sequence of stochastic solutions. Experimental segmentation results show 
that it is possible to obtain results quickly and that the algorithm 
yields excellent results for images with intense noise.