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...

Tumor is an uncontrolled growth of tissues in any part of the body. Tumors are of different types and they have different characteristics and treatments. Brain tumor is inherently serious and life-threatening because of its character in the limited space of the intracranial cavity (space formed inside the skull). Locating the tumor within MR (magnetic resonance) image of brain is integral part ...

Discontinuity-preserving Bayesian image restoration, based on Markov random fields (MRF), involves an intensity fleld, representing the image to be restored, and an edge (discontinuity) fleld. The usual strategy is to perform joint maximum a posteriori (MAP) estimation of the intensity and discontinuity fields, this requiring the specification of Bayesian priors. Departing from this approach, w...

Developments in Markov Chain Monte Carlo procedures have made it possible to perform fully Bayesian image seg-mentation. By this we mean that all the parameters are treated identically, be they the segmentation labels, the class parameters or the Markov Random Field prior parameters. We perform the analysis by sampling from the posterior distribution of all the parameters. Sampling from the MRF...

We are interested in speeding up approximate inference in Markov Random Fields (MRFs). We present a new method that uses gates—binary random variables that determine which factors of the MRF to use. Which gates are open depends on the observed evidence; when many gates are closed, the MRF takes on a sparser and faster structure that omits “unnecessary” factors. We train parameters that control ...

This paper proposes a Markov random field (MRF) model-based method for unsupervised segmentation of multispectral images consisting of multiple textures. To model such textured images, a hierarchical MRF is used with two layers, the first layer representing an unobservable region image and the second layer representing multiple textures which cover each region. This method uses the Expectation ...

Estimation of the extent and spread of wildland fires is an important application of high spatial resolution multispectral images. This work addresses an unsupervised statistical segmentation algorithm to map fire extent, fire front location, just burned area and smoke region based on a statistical model. The results are useful information for a fire propagation model to predict fire behavior. ...

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...

Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...

This paper presents an optimisation technique to select automatically a set of control parameters for a Markov Random Field applied to stereo matching. The method is based on the Reactive Tabu Search strategy, and requires to define a suitable fitness function that measures the performance of the MRF stereo algorithm with a given parameters set. This approach have been made possible by the rece...