Embedded computing architectures can be designed to meet a variety of application specific requirements. However, optimized hardware can require compiler support to realize the po...
This paper presents a probabilistic framework for off-line multiple object tracking. At each timestep, a small set of deterministic candidates is generated which is guaranteed to ...
This paper describes a new method for automatic estimation of the contours of the femur and of the cranial cross-section in fetal ultrasound images. Our approach can be described ...
We deploy a novel Reinforcement Learning optimization technique based on afterstates learning to determine the gain that can be achieved by incorporating movement prediction inform...
We present in this paper a new learning problem called learning distributions from experts. In the case we study the experts are stochastic deterministic finite automata (sdfa). W...