Bilateral filters have wide spread use due to their edge-preserving properties. The common
use case is to manually choose a parametric filter type, usually a Gaussian filter. In this …

In this paper, we present a comprehensive survey of Markov Random Fields (MRFs) in
computer vision and image understanding, with respect to the modeling, the inference and …

We present a novel approach to video segmentation using multiple object proposals. The
problem is formulated as a minimization of a novel energy function defined over a fully …

In this paper we propose a CNN architecture for semantic image segmentation. We
introduce a new “bilateral inception” module that can be inserted in existing CNN …

As well known, fully convolutional network (FCN) becomes the state of the art for semantic
segmentation in deep learning. Currently, new hardware designs for deep learning have …

The mainstream methods for change detection in synthetic-aperture radar (SAR) images use
difference images to define the initial change regions. However, methods can suffer from …

Current and near-term implantable prosthetic vision systems offer the potential to restore
some visual function, but suffer from limited resolution and dynamic range of induced visual …

Mean-field variational inference is one of the most popular approaches to inference in
discrete random fields. Standard mean-field optimization is based on coordinate descent …

A promising, recently explored, alternative to ultra-high b-value diffusion weighted imaging
(UHB-DWI) is apparent ultra-high b-value diffusion-weighted image reconstruction (AUHB …

Abstract Conditional Random Fields (CRFs) are one of the core technologies in computer
vision, and have been applied on a wide variety of tasks. Conventional CRFs typically define …