In urban areas, the presence of shadow in high resolution satellite images is caused a serious problem for the full exploitation of images. To solve the shadow problem in high resolution satellite images, this paper propose a new shadow detection and reconstruction algorithm. Mainly three stages are using in this paper shadow detection stage, training stage and shadow reconstruction stage. The ...

The present report describes a novel method of reconstructing three-dimensional (3-D) scenes that include block-like objects observed in monocular images. The objects are represented by 3-D rigid models with geometrical parameters. The fidelity of these object models to the images and the consistency of relations between the object models are formulated using the Markov Random Field (MRF) model...

In this paper we develop a supervised classification approach for medium and high resolution multichannel synthetic aperture radar (SAR) amplitude images. The proposed technique combines finite mixture modeling for probability density function estimation, copulas for multivariate distribution modeling and a Markov random field (MRF) approach to Bayesian classification. The novelty of this resea...

We present a fully automated technique to segment lesions from multimodal brain MRIs of patients with Multiple Sclerosis. We describe an adapted Markov Random Field that uses intensity at every voxel, its neighbourhood intensity difference information and neighbouring voxel class information to infer voxel labels at every voxel. We test our technique on 25 real, clinical MS volumes evaluated by...

Image segmentation is an essential processing step for many image analysis applications. We propose a novel pixon-based adaptive scale method for image segmentation. The key idea of our approach is that a pixon-based image model is combined with a Markov random field (MRF) model under a Bayesian framework. We introduce a new pixon scheme that is more suitable for image segmentation than the "fu...

We present a segmentation method for fetal brain tissues of T2w MR images, based on the well known Expectation Maximization Markov Random Field (EM-MRF) scheme. Our main contribution is an intensity model composed of 7 Gaussian distribution designed to deal with the large intensity variability of fetal brain tissues. The second main contribution is a 3-steps MRF model that introduces both local...

We describe a learning procedure for a generative model that contains a hidden Markov Random Field (MRF) which has directed connections to the observable variables. The learning procedure uses a variational approximation for the posterior distribution over the hidden variables. Despite the intractable partition function of the MRF, the weights on the directed connections and the variational app...

This paper describes a method for building a compact online Markov random field (MRF) recognizer for large handwritten Japanese character set using structured dictionary representation and vector quantization (VQ) technique. The method splits character patterns into radicals, whose models by MRF are shared by different character classes such that a character model is constructed from the consti...

In this paper, we propose a new algorithm for image segtnentation based on the Markov Random Field (MRF) and the Ant Colony Optimization (ACO) metaheuristic. The underlying idea is to take advantage from the ACO nietaheuristic characteristics and the MRF theory to develop a novel ngents-based approach to segment an image. The proposed algorithm is based on a population of simple agents which co...

In this paper we propose a fully connected energy function for Markov Random Field (MRF) modeling which is inspired by Spin-Glass Theory (SGT). Two major tasks in MRF modeling are how to define the neighborhood system for irregular sites and how to choose the energy function for a proper encoding of constraints. The proposed energy function offers two major advantages that makes it possible to ...