Decision fusion algorithm for target tracking in forward-looking infrared imagery

2004 ◽  
Author(s):  
Amer Dawoud ◽  
Mohammad S. Alam ◽  
Abdullah Bal ◽  
Chey H. Loo
Keyword(s):  
Target Tracking ◽  
Decision Fusion ◽  
Infrared Imagery ◽  
Fusion Algorithm ◽  
Forward Looking

Target tracking in airborne forward looking infrared imagery

2003 ◽  
Vol 21 (7) ◽  
pp. 623-635 ◽  
Author(s):  
Alper Yilmaz ◽  
Khurram Shafique ◽  
Mubarak Shah
Keyword(s):  
Target Tracking ◽  
Infrared Imagery ◽  
Forward Looking

scholarly journals A muscle synergies-based movements detection approach for recognition of the wrist movements

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Aida Masoumdoost ◽  
Reza Saadatyar ◽  
Hamid Reza Kobravi
Keyword(s):  
Decision Fusion ◽  
Classification Performance ◽  
Muscle Synergy ◽  
Fusion Algorithm ◽  
Main Research ◽  
Main Challenge ◽  
Detection Approach ◽  
Average Accuracy ◽  
Bayesian Fuzzy Clustering ◽  
Research Challenge

Abstract Myoelectric signals are regarded as the control signal for prosthetic limbs. But, the main research challenge is reliable and repeatable movement detection using electromyography. In this study, the analysis of the muscle synergy pattern has been considered as a key idea to cope with this main challenge. The main objective of this research was to provide an analytical tool to recognize six wrist movements through electromyography (EMG) based on analysis of the muscle synergy patterns. In order to design such a system‚ the synergy patterns of the wrist muscles have been extracted and utilized to identify wrist movements. Also, different decision fusion algorithms were used to increase the reliability of the synergy pattern classification. The classification performance was evaluated while no data subject was enrolled. In terms of the achieved performance, using a multi-layer perceptron (MLP) neural network as the fusion algorithm turned out to be the best combination. The classification average accuracy, obtained in an offline manner, was about 99.78 ± 0.45%. While the classification average cross-validation accuracy, obtained in an offline manner, using Bayesian fusion, and Bayesian fuzzy clustering (BFC) fusion algorithm were 99.33 ± 0.80% and 96.43 ± 1.08%, respectively.

Automated vehicle detection in forward-looking infrared imagery

2004 ◽  
Vol 43 (2) ◽  
pp. 333 ◽  
Author(s):  
Sandor Der ◽  
Alex Chan ◽  
Nasser Nasrabadi ◽  
Heesung Kwon
Keyword(s):  
Vehicle Detection ◽  
Infrared Imagery ◽  
Automated Vehicle ◽  
Forward Looking

scholarly journals Joint Target Tracking, Recognition and Segmentation for Infrared Imagery Using a Shape Manifold-Based Level Set

Sensors ◽  
2014 ◽  
Vol 14 (6) ◽  
pp. 10124-10145 ◽  
Author(s):  
Jiulu Gong ◽  
Guoliang Fan ◽  
Liangjiang Yu ◽  
Joseph Havlicek ◽  
Derong Chen ◽  
...  
Keyword(s):  
Target Tracking ◽  
Level Set ◽  
Infrared Imagery

Wavelet-RX anomaly detection for dual-band forward-looking infrared imagery

2010 ◽  
Vol 49 (24) ◽  
pp. 4621 ◽  
Author(s):  
Asif Mehmood ◽  
Nasser M. Nasrabadi
Keyword(s):  
Anomaly Detection ◽  
Dual Band ◽  
Infrared Imagery ◽  
Forward Looking
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