Real-Time Automatic Kinematic Model Building for Optical Motion Capture Using a Markov Random Field

2007 ◽  
pp. 69-78 ◽  
Author(s):  
Stjepan Rajko ◽  
Gang Qian
Keyword(s):  
Random Field ◽  
Real Time ◽  
Motion Capture ◽  
Markov Random Field ◽  
Model Building ◽  
Kinematic Model ◽  
Markov Random ◽  
Optical Motion ◽  
Optical Motion Capture

scholarly journals Traffic Updates: Saying a Lot While Revealing a Little

2019 ◽  
Vol 33 ◽  
pp. 986-995
Author(s):  
John Krumm ◽  
Eric Horvitz
Keyword(s):  
Random Field ◽  
Real Time ◽  
Markov Random Field ◽  
Road Network ◽  
Probabilistic Method ◽  
Vehicle Occupant ◽  
Traffic Conditions ◽  
Potential Value ◽  
Markov Random ◽  
Traffic Speed

Taking speed reports from vehicles is a proven, inexpensive way to infer traffic conditions. However, due to concerns about privacy and bandwidth, not every vehicle occupant may want to transmit data about their location and speed in real time. We show how to drastically reduce the number of transmissions in two ways, both based on a Markov random field for modeling traffic speed and flow. First, we show that a only a small number of vehicles need to report from each location. We give a simple, probabilistic method that lets a group of vehicles decide on which subset will transmit a report, preserving privacy by coordinating without any communication. The second approach computes the potential value of any location’s speed report, emphasizing those reports that will most affect the overall speed inferences, and omitting those that contribute little value. Both methods significantly reduce the amount of communication necessary for accurate speed inferences on a road network.

scholarly journals Multimodal Brain-Tumor Segmentation Based on Dirichlet Process Mixture Model with Anisotropic Diffusion and Markov Random Field Prior

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yisu Lu ◽  
Jun Jiang ◽  
Wei Yang ◽  
Qianjin Feng ◽  
Wufan Chen
Keyword(s):  
Brain Tumor ◽  
Random Field ◽  
Real Time ◽  
Markov Random Field ◽  
Dirichlet Process ◽  
Anisotropic Diffusion ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Number Of Clusters ◽  
Markov Random

Brain-tumor segmentation is an important clinical requirement for brain-tumor diagnosis and radiotherapy planning. It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. Because the classical MDP segmentation cannot be applied for real-time diagnosis, a new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain-tumor images, we developed the algorithm to segment multimodal brain-tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated using 32 multimodal MR glioma image sequences, and the segmentation results are compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance and has a great potential for practical real-time clinical use.

Markerless human motion capture by Markov random field and dynamic graph cuts with color constraints

2009 ◽  
Vol 52 (2) ◽  
pp. 252-259 ◽  
Author(s):  
Jia Li ◽  
ChengKai Wan ◽  
DianYong Zhang ◽  
ZhenJiang Miao ◽  
BaoZong Yuan
Keyword(s):  
Random Field ◽  
Motion Capture ◽  
Markov Random Field ◽  
Graph Cuts ◽  
Human Motion ◽  
Dynamic Graph ◽  
Human Motion Capture ◽  
Markov Random

Toward accurate real-time marker labeling for live optical motion capture

2017 ◽  
Vol 33 (6-8) ◽  
pp. 993-1003 ◽  
Author(s):  
Shihong Xia ◽  
Le Su ◽  
Xinyu Fei ◽  
Han Wang
Keyword(s):  
Real Time ◽  
Motion Capture ◽  
Time Marker ◽  
Optical Motion ◽  
Optical Motion Capture

scholarly journals Smart Commuter Line (KRL) using IoT and SOA in Indonesia

2020 ◽  
Vol 8 (5) ◽  
pp. 485-488
Keyword(s):  
Random Field ◽  
Real Time ◽  
Markov Random Field ◽  
Public Transportation ◽  
Time Management ◽  
Enterprise System ◽  
Line System ◽  
Markov Random ◽  
Passenger Satisfaction ◽  
The One

Commuter line (KRL) one of the better public transportation for commuter people in Indonesia especially in Jabodetabek (Jakarta, Bogor, Depok, Tangerang, Bekasi). Passenger satisfaction is the one of the service quality factor in KRL. There is no real time passenger information in both train and station, the situation make an unpredictable activity for passenger that want to use or wait the commuter line (KRL). Most of passenger cannot enter the train because the crowded passenger. KRL management cannot manage train capacity and train time management to meet passenger needs. This paper proposed a smart commuter line system to provide real time passenger information using IoT by count people using Markov Random Field framework and integrate all KRL enterprise system using SOA to support data integration.

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