Many recent techniques for low-level vision problems such as image denoising are formulated in terms of Markov random field (MRF) or conditional random field (CRF) models. Nonethel...
In this research we address the problem of classification and labeling of regions given a single static natural image. Natural images exhibit strong spatial dependencies, and mode...
We propose a novel approach for improving level set seg-
mentation methods by embedding the potential functions
from a discriminatively trained conditional random field
(CRF) in...
Dana Cobzas (University of Alberta), Mark Schmidt ...
We present a new method for classification with structured
latent variables. Our model is formulated using the
max-margin formalism in the discriminative learning literature.
We...
This paper presents a generative model based approach to man-made structure detection in 2D natural images. The proposed approach uses a causal multiscale random field suggested i...