Many diagrams contain compound objects composed of parts. We propose a recognition framework that learns parts in an unsupervised way, and requires training labels only for compou...
Abstract. Markov random fields are often used to model high dimensional distributions in a number of applied areas. A number of recent papers have studied the problem of reconstruc...
This paper gives an information theoretic approach for detecting Byzantine modifications in networks employing random linear network coding. Each exogenous source packet is augmen...
Tracey Ho, Ben Leong, Ralf Koetter, Muriel M&eacut...
Abstract--A random matrix model is introduced that probabilistically describes the spatial and temporal multipath propagation between a transmitting and receiving antenna array wit...
This paper presents a dynamic conditional random field (DCRF) model to integrate contextual constraints for object segmentation in image sequences. Spatial and temporal dependenci...