1 research outputs found
An Experimental Comparison of Trust Region and Level Sets
High-order (non-linear) functionals have become very popular in segmentation,
stereo and other computer vision problems. Level sets is a well established
general gradient descent framework, which is directly applicable to
optimization of such functionals and widely used in practice. Recently, another
general optimization approach based on trust region methodology was proposed
for regional non-linear functionals. Our goal is a comprehensive experimental
comparison of these two frameworks in regard to practical efficiency,
robustness to parameters, and optimality. We experiment on a wide range of
problems with non-linear constraints on segment volume, appearance and shape.Comment: 8 pages, 6 figure