Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms sy...
Markov random field (MRF, CRF) models are popular in
computer vision. However, in order to be computationally
tractable they are limited to incorporate only local interactions
a...
Random Forests (RFs) have become commonplace
in many computer vision applications. Their
popularity is mainly driven by their high computational
efficiency during both training ...
Christian Leistner, Amir Saffari, Jakob Santner, H...
This paper presents a power grid analyzer based on a random walk technique. A linear-time algorithm is first demonstrated for DC analysis, and is then extended to perform transien...
Haifeng Qian, Sani R. Nassif, Sachin S. Sapatnekar
This paper presents a randomized scheduler for finding concurrency bugs. Like current stress-testing methods, it repeatedly runs a given test program with supplied inputs. Howeve...
Sebastian Burckhardt, Pravesh Kothari, Madanlal Mu...