In this paper, we study Markov Random Fields as spatial smoothing priors in fMRI detection. Relatively high noise in fMRI images presents a serious challenge for the detection algo...
We present the Higher Order Proxy Neighborhoods (HOPS) approach to modeling higher order neighborhoods in Markov Random Fields (MRFs). HOPS incorporates more context information i...
Object detection is challenging partly due to the limited discriminative power of local feature descriptors. We amend this limitation by incorporating spatial constraints among ne...
This paper presents Bayesian edge inference (BEI), a
single-frame super-resolution method explicitly grounded in
Bayesian inference that addresses issues common to existing
meth...
Bryan S. Morse, Dan Ventura, Kevin D. Seppi, Neil ...
We introduce a novel data-driven mean-shift belief propagation
(DDMSBP) method for non-Gaussian MRFs, which
often arise in computer vision applications. With the aid
of scale sp...