A supervised classification method based on AdaBoost posterior probability and Markov Random Fields (MRF) model with Linear Targets Prior (LTP) is proposed in this paper. Firstly in contrast with most existing regions (superpixels) based models, this approach captures contiguous image regions called superpixels from ratio response maps of original images. Secondly, Adaboost classifier is employ...

Markov logic is a widely used tool in statistical relational learning, which uses a weighted first-order logic knowledge base to specify a Markov random field (MRF) or a conditional random field (CRF). In many applications, a Markov logic network (MLN) is trained in one domain, but used in a different one. This paper focuses on dynamic Markov logic networks, where the size of the discretized ti...

Recent stereo algorithms have achieved impressive results by modelling the disparity image as a Markov Random Field (MRF). An important component of an MRF-based approach is the inference algorithm used to find the most likely setting of each node in the MRF. Algorithms have been proposed which use Graph Cuts or Belief Propagation for inference. These stereo algorithms differ in both the infere...

We propose a new segmentation or clustering model that combines Markov Random Field (MRF) and Normalized Cut (NC) objectives. Both NC and MRF models are widely used in machine learning and computer vision, but they were not combined before due to significant differences in the corresponding optimization, e.g. spectral relaxation and combinatorial max-flow techniques. On the one hand, we show th...

We propose an automated method for the detection of buildings from a single airborne color optical image using a dedicated Markov Random Field (MRF) model, which describes both geometric and photometric attributes of the 3-D objects of interest. The paper presents the basic principles and some preliminary results of our approach.

Learning Markov random field (MRF) models is notoriously hard due to the presence of a global normalization factor. In this paper we present a new framework for learning MRF models based on the contrastive free energy (CF) objective function. In this scheme the parameters are updated in an attempt to match the average statistics of the data distribution and a distribution which is (partially or...

A method is developed that improves the accuracy of super-resolution range maps over interpolation by fusing actively illuminated HDR camera imagery with LIDAR data in dark subterranean environments. The key approach is shape recovery from estimation of the illumination function and integration in a Markov Random Field (MRF) framework. A virtual reconstruction using data collected from the Bruc...

Our noncausal, nonparametric, multiscale, Markov random field (MRF) model is capable of synthesizing and capturing the characteristics of a wide variety of textures, from the highly structured to the stochastic. We use a multiscale synthesis algorithm incorporating local annealing to obtain larger realizations of texture visually indistinguishable from the training texture.

Recent advances in multi-atlas based algorithms address many of the previous limitations in model-based and probabilistic segmentation methods. However, at the label fusion stage, a majority of algorithms focus primarily on optimizing weight-maps associated with the atlas library based on a theoretical objective function that approximates the segmentation error. In contrast, we propose a novel ...

We have examined Virtual Generalizing Random Access Memory Weightless Neural Networks (VG-RAM WNN) as platform for depth map inference from static monocular images. For that, we have designed, implemented and compared the performance of VG-RAM WNN systems against that of depth estimation systems based on Markov Random Field (MRF) models. While not surpassing the performance of such systems, our...