Priors play an essential role in Bayesian theory, and they are frequently center stage in
scientific inference. In image processing, geometric priors became very popular in the past …

Convexity is a known important cue in human vision. We propose shape convexity as a new
high-order regularization constraint for binary image segmentation. In the context of discrete …

The simplicity of gradient descent (GD) made it the default method for training ever-deeper
and complex neural networks. Both loss functions and architectures are often explicitly tuned …

Many objects in real world have convex shapes. It is a difficult task to have representations
for convex shapes with good and fast numerical solutions. This paper proposes a method to …

Abstract Convexity is known as an important cue in human vision. We propose shape
convexity as a new high-order regularization constraint for binary image segmentation. In …

High-order and non-submodular pairwise energies are important for image segmentation,
surface matching, deconvolution, tracking and other computer vision problems. Minimization …

Many computer vision problems require optimization of binary non-submodular energies.
We propose a general optimization framework based on local submodular approximations …

Analytical and empirical studies have shown that self-similar traffic can have detrimental
impact on network performance including amplified queuing delay and packet loss ratio. On …

We propose a novel segmentation method based on energy minimization of higher-order
potentials. We introduce higher-order terms into the energy to incorporate prior knowledge …

Minimization of boundary curvature is a classic regularization technique for image
segmentation in the presence of noisy image data. Techniques for minimizing curvature …