Spatio-temporal Markov Random Field Research Articles

In vitro Diagnosis of Exercise Related Skin Trauma based on Spatiotemporal Image Segmentation

In vitro diagnosis is a kind of product and service that can get clinical diagnosis information by detecting human samples (blood, body fluid, tissue, etc.) outside human body and then judge disease or body’s functioning. This paper proposes an in vitro diagnosis method based on spatiotemporal image segmentation. Firstly, the image sequence segmentation method based on spatiotemporal Markov random field is used to segment computed tomography image, then the computed tomography image of exercise related skin trauma is segmented. According to the segmented computed tomography image, the in vitro diagnosis of exercise-related skin trauma is realized by the detection method based on the superpixel spatiotemporal characteristics. The results show that the proposed method can effectively detect skin trauma in vitro and has good segmentation effect on skin computed tomography image, as well as high accuracy in trauma detection. It can also be used in physical examination, chronic disease management and severe disease monitoring. Compared with similar detection methods, this method has significant application value.

Threat-Event Detection for Distributed Networks Based on Spatiotemporal Markov Random Field

Distributed threat-events are one of the main challenges faced in computer networks. Although a lot of research has been conducted for these issues, the situation has not been significantly improved. Different from existing victim-centric approaches, in this article we propose a new network-centric approach for the detection of distributed threat-events. The distributed network is treated as a holistic system that consists of spatially interconnected network elements. Network events are detected by the dynamic behavior analysis of the distributed networks. We develop a model consisting of two-layer random fields to describe the time-varying traffic forwarding behavior of the distributed networks. The bottom layer describes the interaction and influence of the network elements under the action of network events. Markovianity is adopted to characterize the spatiotemporal context of each network element’s behavior patterns. The top layer describes each network element’s traffic features driven by the underlying behavior patterns. A Gaussian mixture model is used to capture the statistical features of the network traffic for each behavior pattern. We derive algorithms for parameter estimation and event detection. Numerical experiments using real datasets and different network scenarios are presented to validate the proposed approach. Performance-related issues and comparison with related works are discussed.

Traffic Video Image Segmentation Model Based on Bayesian and Spatio-Temporal Markov Random Field

Traffic video image is a kind of dynamic image and its background and foreground is changed at any time, which results in the occlusion. In this case, using the general method is more difficult to get accurate image segmentation. A segmentation algorithm based on Bayesian and Spatio-Temporal Markov Random Field is put forward, which respectively build the energy function model of observation field and label field to motion sequence image with Markov property, then according to Bayesian' rule, use the interaction of label field and observation field, that is the relationship of label field’s prior probability and observation field’s likelihood probability, get the maximum posterior probability of label field’s estimation parameter, use the ICM model to extract the motion object, consequently the process of segmentation is finished. Finally, the segmentation methods of ST - MRF and the Bayesian combined with ST - MRF were analyzed. Experimental results: the segmentation time in Bayesian combined with ST-MRF algorithm is shorter than in ST-MRF, and the computing workload is small, especially in the heavy traffic dynamic scenes the method also can achieve better segmentation effect.

Lung diaphragm tracking in CBCT images using spatio-temporal MRF

In EBRT in order to monitor the intra fraction motion of thoracic and abdominal tumors, one of the standard approaches is to use the lung diaphragm apex as an internal marker. However, tracking the position of the apex from image based observations is a challenging problem, as it undergoes both position and shape variation. The purpose of this paper is to propose an alternative method for tracking the ipsi-lateral hemidiaphragm apex (IHDA) position on Cone Beam Computed Tomography (CBCT) projection images. A hierarchical method is proposed to track the IHDA position across the frames. The diaphragm state is modeled as a spatio-temporal Markov Random Field (MRF). The likelihood function is derived from the votes based on 4D-Hough space. The optimal state of the diaphragm is obtained by solving the associated energy minimization problem using graph-cuts. A heterogeneous GPU implementation is provided for the method using CUDA framework and the performance is compared with that of CPU implementation. The method was tested using 15 clinical CBCT images. The results demonstrate that the MRF formulation outperforms the full search method in terms of accuracy. The GPU based heterogeneous implementation of the proposed algorithm takes about 25s, which is 16% improvement over the existing benchmark. The proposed MRF formulation considers all the possible combinations from the 4D-Hough space and therefore results in better tracking accuracy. The GPU based implementation exploits the inherent parallelism in our algorithm to accelerate the performance thereby increasing the viability of the approach for clinical use.

Water detection through spatio-temporal invariant descriptors

In this work, we aim to segment and detect water in videos. Water detection is beneficial for appllications such as video search, outdoor surveillance, and systems such as unmanned ground vehicles and unmanned aerial vehicles. The specific problem, however, is less discussed compared to general texture recognition. Here, we analyze several motion properties of water. First, we describe a video pre-processing step, to increase invariance against water reflections and water colours. Second, we investigate the temporal and spatial properties of water and derive corresponding local descriptors. The descriptors are used to locally classify the presence of water and a binary water detection mask is generated through spatio-temporal Markov Random Field regularization of the local classifications. Third, we introduce the Video Water Database, containing several hours of water and non-water videos, to validate our algorithm. Experimental evaluation on the Video Water Database and the DynTex database indicates the effectiveness of the proposed algorithm, outperforming multiple algorithms for dynamic texture recognition and material recognition.

Accurate and real-time depth video acquisition using Kinect–stereo camera fusion

This paper presents a Kinect–stereo camera fusion system that significantly improves the accuracy of depth map acquisition. The typical Kinect depth map suffers from missing depth values and errors, resulting from a single Kinect input. To ameliorate such problems, the proposed system couples a Kinect with a stereo RGB camera to provide an additional disparity map. Kinect depth map and the disparity map are efficiently fused in real time by exploiting a spatiotemporal Markov random field framework on a graphics processing unit. An efficient temporal data cost is proposed to maintain the temporal coherency between frames. We demonstrate the performance of the proposed system on challenging real-world examples. Experimental results confirm that the proposed system is robust and accurate in depth video acquisition.

3D-MRF BASED VIDEO TRACKING IN THE COMPRESSED DOMAIN

Object tracking is an interesting and needed procedure for many real time applications. But it is a challenging one, because of the presence of challenging sequences with abrupt motion, drastic illumination change, large pose variation, occlusion, cluttered background and also the camera shake. This paper presents a novel method of object tracking by using the algorithms spatiotemporal Markov random field (STMRF) and online discriminative feature selection (ODFS), which overcome the above mentioned problems and provide a better tracking process. This method is also capable of tracking multiple objects in video sequence even in the presence of an object interactions and occlusions that achieves better results with real time performance.

Video Object Tracking in the Compressed Domain Using Spatio-Temporal Markov Random Fields

Despite the recent progress in both pixel-domain and compressed-domain video object tracking, the need for a tracking framework with both reasonable accuracy and reasonable complexity still exists. This paper presents a method for tracking moving objects in H.264/AVC-compressed video sequences using a spatio-temporal Markov random field (ST-MRF) model. An ST-MRF model naturally integrates the spatial and temporal aspects of the object's motion. Built upon such a model, the proposed method works in the compressed domain and uses only the motion vectors (MVs) and block coding modes from the compressed bitstream to perform tracking. First, the MVs are preprocessed through intracoded block motion approximation and global motion compensation. At each frame, the decision of whether a particular block belongs to the object being tracked is made with the help of the ST-MRF model, which is updated from frame to frame in order to follow the changes in the object's motion. The proposed method is tested on a number of standard sequences, and the results demonstrate its advantages over some of the recent state-of-the-art methods.

Priority Belief Propagation-Based Inpainting Prediction With Tensor Voting Projected Structure in Video Compression

This paper presents a new video compression framework over H.264/AVC scheme that integrates our proposed structured priority belief propagation (BP)-based inpainting prediction (IP) to exploit the intrinsic nonlocal and geometric regularity in video samples. Unlike the existing edge-based inpainting adopted in lossy image coding, the optimal predictor could maintain the pixel-wise fidelity and the robust error resilience without any assistant information. Beyond the local prediction limitation of traditional intra and inter-modes, the priority BP with regularized structure priors of a spatio-temporal Markov random field is imposed on the predictor in an adaptive and more convergent sense. Specifically, the structured sparsity of the predicted macroblock region is inferred by tensor voting projected from the co-located decoded regions. In turn, the priority and visiting order of nodes are assigned according to the sets of updated beliefs as the propagation of messages. Through relatively few iterations of forward and backward process, the sparse inference of priority BP would ensure a stable marginal belief distribution on the structure and texture through updating local messages and beliefs. Within the optimal mode selection on rate-distortion optimization (RDO), the IP-mode with structured priority BP outperforms the existing vision-based approaches, and specially achieves a better objective rate-distortion performance besides visual quality. The IP-mode with structured priority BP can be applied to both I and P frames to generate low entropy residue, e.g., homogeneous visual patterns, and the computation complexity is also competitive with one iteration of sparse inference. Moreover, it behaves more resilient with an intrinsic probabilistic inference than the intra and inter-modes.

Moving object segmentation technology based on DA-STMRF model

In order to avoid the drawback of over-smoothness in the conventional Spatiotemporal Markov Random Field(MRF) model,a moving object segmentation technology based on Discontinuity Adaptive-Spatiotemporal Markov Random Field(DA-STMRF) model was proposed.Initial labels were derived after frame difference images were converted to binary images,and the AND-label was obtained with the AND-operation on the two initial labels.After constructing corresponding energy functions,we used metroplis sampler algorithm to optimize the label field.Segmentation experiments on several image sequences show that the algorithm proposed is effective.

An occlusion‐robust tracking algorithm based on a spatiotemporal Markov random field model

AbstractThe tracking of vehicles on images is important as a baseline technology for applying ITS image processing to the detection of abnormal phenomena such as accidents. However, up to now one of the most difficult problems in vehicle tracking has been occlusion, which creates a difficult environment for realizing stable tracking. This has been particularly true in our research, because our objects of study are large intersections in which an average of 20 vehicles are present at the same time, of various sizes and shapes, and so on. The various motions of these vehicles also generate various conditions under which occlusion can occur. In tracking under these circumstances, it is necessary to use a paradigm different from those used previously, which assume a model of rectilinear motion, largely vacant intersections, predictions based on straight‐line trajectories, and specific vehicle shapes. To solve this problem, we developed an algorithm that makes use of the Markov random field (MRF) model, generalized beyond a single image to include temporal sequences of images, and evaluated its degree of accuracy based on spatiotemporal correlation of textures from one image to the one immediately following it and the connection of loci followed by the moving bodies. The results were further optimized by stochastic relaxation of the energy distributions exhibited by images from this space‐time MRF, which makes the tracking robust against various forms of occlusion. We applied this space‐time MRF to image data taken over about 25 minutes, tracking 3214 vehicles moving through an intersection under conditions of heavy congestion. Our results showed that the tracking was successful with a 99% probability when the vehicles being tracked generated no occlusions, while for the 541 cars that did cause occlusions the probability of successfully tracking multiple cars and distinguishing the separate vehicles through the occlusions was 95%. Because this algorithm uses information obtained from black‐and‐white images only, and can be implemented without any prior hypotheses about the vehicle's shape etc., it is useful over long periods of time and under a broad range of conditions. For this reason, we anticipate that it will be effective for detecting accidents and other anomalies in traffic at intersections. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(9): 73–86, 2003; Published online in Wiley InterScience (www. interscience.wiley.com). DOI 10.1002/ecjc.10110

Traffic monitoring and accident detection at intersections

We have developed an algorithm, referred to as spatio-temporal Markov random field, for traffic images at intersections. This algorithm models a tracking problem by determining the state of each pixel in an image and its transit, and how such states transit along both the x-y image axes as well as the time axes. Our algorithm is sufficiently robust to segment and track occluded vehicles at a high success rate of 93%-96%. This success has led to the development of an extendable robust event recognition system based on the hidden Markov model (HMM). The system learns various event behavior patterns of each vehicle in the HMM chains and then, using the output from the tracking system, identifies current event chains. The current system can recognize bumping, passing, and jamming. However, by including other event patterns in the training set, the system can be extended to recognize those other events, e.g., illegal U-turns or reckless driving. We have implemented this system, evaluated it using the tracking results, and demonstrated its effectiveness.

FMRI Signal Restoration Using a Spatio-Temporal Markov Random Field Preserving Transitions

In fMRI studies, Gaussian filtering is usually applied to improve the detection of activated areas. Such lowpass filtering enhances the signal to noise ratio. However, undesirable secondary effects are a bias on the signal shape and a blurring in the spatial domain. Neighboring activated areas may be merged and the high resolution of the fMRI data compromised. In the temporal domain, activation and deactivation slopes are also blurred. We propose an alternative to Gaussian filtering by restoring the signal using a spatiotemporal Markov Random Field which preserves the shape of the transitions. We define some interaction between neighboring voxels which allows us to reduce the noise while preserving the signal characteristics. An energy function is defined as the sum of the interaction potentials and is minimized using a simulated annealing algorithm. The shape of the hemodynamic response is preserved leading to a better characterization of its properties. We demonstrate the use of this approach by applying it to simulated data and to data obtained from a typical fMRI study.