Generating connected textured fractal patterns using Markov random fields
نویسندگان
چکیده
منابع مشابه
Unsupervised Segmentation of Textured Images Using Binomial Markov Random Fields
We show how scanned images can be segmented on the basis of their stochastic nature without a priori knowledge of the number of regions or their model parameters. The region distribution is modelled by a Mesh Markov Random Field while each region is filled with textures modelled by a Binomial Markov Random Field (BMRF). A hierarchical fuzzy clustering method is used to estimate the BMRF paramet...
متن کاملMarkov connected component fields
A new class of Gibbsian models with potentials associated to the connected components or homogeneous parts of images is introduced. For these models the neighbourhood of a pixel is not fixed as for Markov random fields, but given by the components which are adjacent to the pixel. The relationship to Markov random fields and marked point processes is explored and spatial Markov properties are es...
متن کاملMarkov Random Fields and Conditional Random Fields
Markov chains provided us with a way to model 1D objects such as contours probabilistically, in a way that led to nice, tractable computations. We now consider 2D Markov models. These are more powerful, but not as easy to compute with. In addition we will consider two additional issues. First, we will consider adding observations to our models. These observations are conditioned on the value of...
متن کاملUnmixing hyperspectral images using Markov random fields
This paper proposes a new spectral unmixing strategy based on the normal compositional model that exploits the spatial correlations between the image pixels. The pure materials (referred to as endmembers) contained in the image are assumed to be available (they can be obtained by using an appropriate endmember extraction algorithm), while the corresponding fractions (referred to as abundances) ...
متن کاملBayesian image classification using Markov random fields
In this paper, we present three optimisation techniques, Deterministic Pseudo-Annealing (DPA), Game Strategy Approach (GSA), and Modified Metropolis Dynamics (MMD), in order to carry out image classification using a Markov random field model. For the first approach (DPA), the a posteriori probability of a tentative labelling is generalised to a continuous labelling. The merit function thus defi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 1991
ISSN: 0162-8828
DOI: 10.1109/34.85673