In this paper, we study the optimal way of distributing sensors in a random field to minimize the estimation distortion. We show that this problem is equivalent to certain proble...
In this paper, we present a new background estimation algorithm which effectively represents both background and foreground. The problem is formulated with a labeling problem over...
Markov Random Field, or MRF, models are a powerful tool for modeling images. While much progress has been made in algorithms for inference in MRFs, learning the parameters of an M...
We propose a framework for intensity-based registration of images by linear transformations, based on a discrete Markov Random Field (MRF) formulation. Here, the challenge arises ...
Darko Zikic, Ben Glocker, Oliver Kutter, Martin Gr...
As CMOS devices and operating voltages are scaled down, noise and defective devices will impact the reliability of digital circuits. Probabilistic computing compatible with CMOS o...
Kundan Nepal, R. Iris Bahar, Joseph L. Mundy, Will...