Disclaimer This document describes work undertaken as part of a programme of study at the Indian Institute of Remote Sensing and International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute. Abstract Remote sensing technologies provide a un...

Motivation: In a series of recent papers, Tess, a computer program based on the concept of hidden Markov random field, has been proposed to infer the number and locations of panmictic population units from the genotypes and spatial locations of these individuals. The method seems to be of broad appeal as it is conceptually much simpler than other competing methods and it has been reported by it...

3 Graph Analysis 6 3.1 Analysis Based on Spectral Graph Theory . . . . . . . . . . . . . 7 3.2 Analysis Based on Random Field Theory . . . . . . . . . . . . . . 9 3.2.1 Markov Random Fields . . . . . . . . . . . . . . . . . . . . 9 3.2.2 Conditional Random Fields . . . . . . . . . . . . . . . . . 10 3.2.3 Gaussian Random Fields . . . . . . . . . . . . . . . . . . . 11 3.3 Analysis Based onMatri...

Hidden Markov random fields (HMRFs) are conventionally assumed to be homogeneous in the sense that the potential functions are invariant across different sites. However in some biological applications, it is desirable to make HMRFs heterogeneous, especially when there exists some background knowledge about how the potential functions vary. We formally define heterogeneous HMRFs and propose an E...

Markov Random Fields (MRFs) are a popular and wellmotivated model for many medical image processing tasks such as segmentation. Discriminative Random Fields (DRFs), a discriminative alternative to the traditionally generative MRFs, allow tractable computation with less restrictive simplifying assumptions, and achieve better performance in many tasks. In this paper, we investigate the tumor segm...

Relational learning analyzes the probabilistic constraints between the attributes of entities and relationships. We extend the expressiveness of relational models by introducing for each entity (or object) an infinite-state latent variable as part of a Dirichlet process (DP) mixture model. It can be viewed as a relational generalization of hidden Markov random field. The information propagates ...

Removing noise from original image is still a challenging problem for researchers. There have been several published algorithm and each approach has its assumptions, advantages and disadvantages. Markov Random Field is ndimensional random process defined on a on a discrete lattice. Markov Random Field is a new branch of probability theory that promises to be important both in theory and applica...

Removing noise from original image is still a challenging problem for researchers. There have been several published algorithm and each approach has its assumptions, advantages and disadvantages. Markov Random Field is n-dimensional random process defined on a on a discrete lattice. Markov Random Field is a new branch of probability theory that promises to be important both in theory and applic...

A hidden Markov random fields may arise where a Markov random field – a spatial arrangement of correlated discrete states – is corrupted by some observational noise process. We assume that the number of hidden or latent states is known and wish to perform inference on all unknown parameters. The main challenge in such cases is to calculate the likehood of the hidden states, which could be compu...

Due to the enormous quantity of radar images acquired by satellites and through shuttle missions, there is an evident need for efficient automatic analysis tools. This paper describes unsupervised classification of radar images in the framework of hidden Markov models and generalized mixture estimation. Hidden Markov chain models, applied to a Hilbert–Peano scan of the image, constitute a fast ...