IAPR Workshop on Machine Vision and Applications, pp. 455-458, 2000, Tokyo, Japan In this paper, we integrate the model-based tracking and local contexture (temporal and spatial) ...
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...
—This paper presents an efficient technique for the estimation of the routed wirelength during global placement using the wire density of the net. The proposed method identifie...
We propose to bridge the gap between Random Field (RF) formulations for joint categorization and segmentation (JCaS), which model local interactions among pixels and superpixels, ...
We present a novel Quadratic Program (QP) formulation for robust multi-model fitting of geometric structures in vision data. Our objective function enforces both the fidelity of...