Decision fusion algorithm for target tracking in infrared imagery

2005 ◽  
Vol 44 (2) ◽  
pp. 026401 ◽  
Author(s):  
A. Dawoud
Keyword(s):  
Target Tracking ◽  
Decision Fusion ◽  
Infrared Imagery ◽  
Fusion Algorithm

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

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.

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

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 AUTOMATIC VEHICLE RECOGNITION FOR URBAN TRAFFIC MANAGEMENT

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 741-744
Author(s):  
M. Mohammadi ◽  
F. Tabib Mahmoudi ◽  
M. Hedayatifard
Keyword(s):  
Land Use ◽  
Spatial Resolution ◽  
Traffic Management ◽  
Decision Fusion ◽  
Image Resolution ◽  
Urban Traffic ◽  
Aerial Images ◽  
Fusion Algorithm ◽  
Vehicle Recognition ◽  
Very High

Abstract. Automatic vehicle recognition has an important role for many applications such as supervision, traffic management and rescue tasks. The ability of online supervision on the distribution of vehicles in urban environments prevents traffic, which in turn reduces air pollution and noise. However, this is extremely challenging due to the small size of vehicles, their different types and orientations, and the visual similarity to some other objects in very high resolution images. In this paper, an automatic vehicle recognition algorithm is proposed based on very high spatial resolution aerial images. In the first step of the proposed method, by generating the image pyramid, the candidate regions of the vehicles are recognized. Then, performing reverse pyramid, decision level fusion of the vehicle candidates and the land use/cover classification results of the original image resolution are performed in order to modify recognized vehicle regions. For evaluating the performance of the proposed method in this study, Ultracam aerial imagery with spatial resolution of 11 cm and 3 spectral bands have been used. Comparing the obtained vehicle recognition results from the proposed decision fusion algorithm with some manually selected vehicle regions confirm the accuracy of about %80. Moreover, the %78.87 and 0.71 are respectively the values for overall accuracy and Kappa coefficient of the obtained land use/cover classification map from decision fusion algorithm.

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