An unsupervised multiresolution conditional random field (CRF) approach to texture segmentation problems is introduced. This approach involves local and long-range information in the CRF neighbourhood to determine the classes of image blocks. Like most Markov random field (MRF) approaches, the proposed method treats the image as an array of random variables and attempts to assign an optimal cla...

We introduced an unsupervised texture segmentation method , the selectionist relaxation, relying on a Markov Random Field (MRF) texture description and a genetic algorithm based relaxation scheme. It has been shown elsewhere that this method is convenient for achieving a parallel and reliable estimation of MRF parameters and consequently a correct image segmentation. Nevertheless, these results...

In this paper, we propose an unsupervised color image classification algorithm based on a Markov random field (MRF) model. In the MRF model, we use the CIE-luv color metric because it is close to human perception when computing color differences. On the other hand, intensity and chroma information is separated in this space. Without parameter estimation, our model would not be useful in real-li...

Finite mixture model (FMM) with Gaussian distribution has been widely used in many image processing and pattern recognition tasks. This paper presents a new Student's-t mixture model (SMM) based on Markov random field (MRF) and weighted mean template. In this model, the Student's-t distribution is considered as an alternative to the Gaussian distribution due to the former is heavily tailed than...

In this paper we extend the work of (Louw and Nicolls, 2007) which proposed a novel Markov Random Field formulation for resolving sparse features correspondences in image pairs. The MRF terms can include cliques of variable sizes, and the energies are minimized using Loopy Belief Propagation. In this paper, an improved MRF topology is developed which uses a variant of the previously developed K...

We propose a systematic approach, based on probabilistic graphical model, to infer missing observations in Wireless Sensor Networks (WSNs) for sustaining environmental monitoring. This enables us to effectively address two critical challenges in WSNs: (a) energyefficient data gathering through planned energy-saving sleep cycles and (b) sensor-node failure tolerance in harsh environments. In our...

This paper studies the problem of detecting unknown objects within indoor environments in an active and natural manner. The visual saliency scheme utilizing both color and depth cues is proposed to arouse the interests of the machine system for detecting unknown objects at salient positions in a 3D scene. The 3D points at the salient positions are selected as seed points for generating object h...

Due to the speckle effect of coherent imaging the detection of lines in SAR scenes is considerably more difficult than in optical images. A new approach to detect lines in noisy images using a Markov random field (MRF) model and Bayesian classification is proposed. The unobservable object classes of single pixels are assumed to fulfill the Markov condition, i.e. to depend on the object classes ...

Color image segmentation is an important topic in the image processing field. MRF-MAP is often adopted in the unsupervised segmentation methods, but their performance are far behind recent interactive segmentation tools supervised by user inputs. Furthermore, the existing related unsupervised methods also suffer from the low efficiency, and high risk of being trapped in the local optima, becaus...

In this paper wk have used two fuzzy clustering algorithms, namely Fuzzy C-Means (FCM) and Gustafson Kessel Clustering (GKC) for unsupervised change detection in multitemporal remote sensing images. In conventional FCM & GKC no spatio-contextual information is taken into account and thus the result is not so much robust to noise/outliers. By incorporation of local neighborhood informationthe pe...