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Found 10 papers, 0 papers with code
Simulating CRF with CNN for CNN
After our CRF Simulator is trained, it can be directly incorporated as part of any larger CNN architecture, enabling a seamless end-to-end training.
K-convexity shape priors for segmentation
As shown in the paper, for many forms of convexity our regularization model is significantly more descriptive for any given k. Our shape prior is useful in practice, e. g. in biomedical applications, and its optimization is robust to local minima.
Adaptive and Move Making Auxiliary Cuts for Binary Pairwise Energies
First, unlike LSA-AUX which selects auxiliary functions based solely on the current solution, we propose to incorporate several additional criteria.
Efficient Squared Curvature
Curvature has received increasing attention as an important alternative to length based regularization in computer vision.
Submodularization for Binary Pairwise Energies
We propose a general optimization framework based on local submodular approximations (LSA).
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.
Submodularization for Quadratic Pseudo-Boolean Optimization
We propose a general optimization framework based on local submodular approximations (LSA).
Efficient Regularization of Squared Curvature
Curvature has received increased attention as an important alternative to length based regularization in computer vision.
Auxiliary Cuts for General Classes of Higher Order Functionals
From these general-form bounds, we state various non-linear problems as the optimization of auxiliary functionals by graph cuts.
Fast Trust Region for Segmentation
In this paper we propose a Fast Trust Region (FTR) approach for optimization of segmentation energies with nonlinear regional terms, which are known to be challenging for existing algorithms.
