Understanding the need Quantitative vs Qualitative MRI data: The vast majority of common clinical MRI protocols rely on qualitative images reflecting the weighted effect of different tissue parameters. These contrast parameters include relaxation times, principally T1, T2 and T2*, as well as structural or functional quantities such as diffusion and blood flow. The absolute level of the signal v...

In normal animals, microstimulation of the medullary reticular formation (MRF) has two effects on efferent neurons in the motor branch of the pudendal nerve (PudM). MRF microstimulation depresses motoneuron reflex discharges (RD) elicited by dorsal nerve of the penis (DNP) stimulation and produces long latency sympathetic fiber responses (SFR). The midthoracic spinal location of these descendin...

This paper presents a new method for estimating the optical flow field using the MRF modeling. In the MRF framework, the estimation problem amounts to the minimization of an energy function. We propose an Evolutionary Algorithm (EA) method to solve this minimization problem. It is based on a divide-and-conquer strategy which adequately uses the markovian property. Experimental results show the ...

Single-neuron recording and electrical microstimulation suggest three roles for the mesencephalic reticular formation (MRF) in oculomotor control: 1) saccade triggering, 2) computation of the horizontal component of saccade amplitude (a feed-forward function), and 3) feedback of an eye velocity signal from the paramedian zone of the pontine reticular formation (PPRF) to higher structures. These...

The development of knowledge graph construction has prompted more and more commercial engines to improve the retrieval performance by using knowledge graphs as the basic semantic web. Knowledge graph is often used for knowledge inference and entity search, however, the potential ability of its entities and properties for better improving search performance in query expansion remains to be furth...

The theory of learning under the uniform distribution is rich and deep, with connections to cryptography, computational complexity, and the analysis of boolean functions to name a few areas. This theory however is very limited due to the fact that the uniform distribution and the corresponding Fourier basis are rarely encountered as a statistical model. A family of distributions that vastly gen...

In this paper, we present an optimised learning algorithm for learning the parametric prior models for high-order Markov random fields (MRF) of colour images. Compared to the priors used by conventional low-order MRFs, the learned priors have richer expressive power and can capture the statistics of natural scenes. Our proposed optimal learning algorithm is achieved by simplifying the estimatio...

We propose Markov random fields (MRFs) as a probabilistic mathematical model for unifying approaches to multi-robot coordination or, more specifically, distributed action selection. The MRF model is well-suited to domains in which the joint probability over latent (action) and observed (perceived) variables can be factored into pairwise interactions between these variables. Specifically, these ...

This paper describes the real time implementation of a simple and robust motion detection algorithm based on Markov random field (MRF) modeling, MRF-based algorithms often require a significant amount of computations. The intrinsic parallel property of MRF modeling has led most of implementations toward parallel machines and neural networks, but none of these approaches offers an efficient solu...

We use a new \aura" framework to rewrite the non-linear energy function of a homogeneous anisotropic Markov/Gibbs random eld (MRF) as a linear sum of aura measures. The new formulation relates MRF's to co-occurrence matrices. It also provides a physical interpretation of MRF textures in terms of the mixing and separation of graylevel sets, and in terms of boundary maximization and minimization....