Markov random field

About: Markov random field is a research topic. Over the lifetime, 5669 publications have been published within this topic receiving 179568 citations. The topic is also known as: MRF.

Detecting Carried Objects from Sequences of Walking Pedestrians

Abstract: This paper proposes a method for detecting objects carried by pedestrians, such as backpacks and suitcases, from video sequences. In common with earlier work [14], [16] on the same problem, the method produces a representation of motion and shape (known as a temporal template) that has some immunity to noise in foreground segmentations and phase of the walking cycle. Our key novelty is for carried objects to be revealed by comparing the temporal templates against view-specific exemplars generated offline for unencumbered pedestrians. A likelihood map of protrusions, obtained from this match, is combined in a Markov random field for spatial continuity, from which we obtain a segmentation of carried objects using the MAP solution. We also compare the previously used method of periodicity analysis to distinguish carried objects from other protrusions with using prior probabilities for carried-object locations relative to the silhouette. We have reimplemented the earlier state-of-the-art method [14] and demonstrate a substantial improvement in performance for the new method on the PETS2006 data set. The carried-object detector is also tested on another outdoor data set. Although developed for a specific problem, the method could be applied to the detection of irregularities in appearance for other categories of object that move in a periodic fashion.

Models and Algorithms for Detection and Tracking of Coordinated Groups

Abstract: In this paper, we describe models and algorithms for detection and tracking of group and individual targets We develop two novel group dynamical models, within a continuous time setting, that aim to mimic behavioural properties of groups We also describe two possible ways of modeling interactions between closely spaced targets using Markov Random Field (MRF) and repulsive forces These can be combined together with a group structure transition model to create realistic evolving group models We use a Markov Chain Monte Carlo (MCMC)-Particles Algorithm to perform sequential inference Computer simulations demonstrate the ability of the algorithm to detect and track targets within groups, as well as infer the correct group structure over time

Spatial kernel K-harmonic means clustering for multi-spectral image segmentation

Abstract: The problem of image segmentation using intensity clustering approaches has been addressed in the literature. Grouping pixels of similar intensity to form clusters in an image have been tackled using a number of methods, such as the K-means (KM) algorithm. The K-harmonic means (KHM) was proposed to overcome the sensitivity of KM to centre initialisation. The use of a spatial kernel-based KHM (SKKHM) algorithm on the problem of image segmentation has been investigated. Instead of the original Euclidean intensity distance, a robust kernel-based KHM metric is employed to reduce the effect of outliers and noise. Spatial image information is also incorporated in the proposed clustering scheme, derived from Markov random field modelling. An extension of the proposed algorithm to multi-spectral imaging applications is also presented. Experimental results for both single-channel and multi-channel images demonstrate the robust performance of the proposed SKKHM algorithm.

Multimodal motion estimation and segmentation using Markov random fields

Abstract: A multimodal approach to the problem of velocity estimation is presented. It combines the advantages of the feature-based and gradient-based methods by making them cooperate in a single global motion estimator. The theoretical framework is based on global Bayesian decision associated with Markov random field models. The proposed approach addresses, in parallel, the problem of velocity estimation and segmentation. Results on synthetic as well as on real-world image sequences are presented. Accurate motion measurement and detection of motion discontinuities with a surprisingly good quality have been obtained. >

Spectral–Spatial Classification of Hyperspectral Image Based on Low-Rank Decomposition

Abstract: Spectral–spatial classification methods have been proven to be effective in hyperspectral image (HSI) classification. However, most of the methods make use of the correlation in a small neighborhood. In this paper, a novel low-rank decomposition spectral–spatial method (LRDSS) is proposed. LRDSS incorporates the global and local correlation where the global correlation is introduced by discovering the low-dimensional structure in the high-dimensional data, and local correlation is modeled by Markov Random Field (MRF). Specifically, all pixels’ spectrums in a homogeneous area are assumed to have low-dimensional structure. Low rankness is a fine property to characterize the low-dimensional structure and robust principal component analysis (RPCA) is used to extract the low-rank data. Then, the spectral information is obtained by the probabilistic support vector machine (SVM) classifier applied on the low-rank data. Moreover, the MRF models local correlation by encouraging neighboring pixels taking the same label. The maximum a posterior classification is computed by min-cut-based optimization algorithm. The experimental results suggest that LRDSS outperforms the other spectral–spatial classification methods investigated in this paper in terms of classification accuracies.