Tracking of crossing targets with forward looking infrared imaging sensors

2003 ◽  
Author(s):  
H.M. Shertukde ◽  
Y. Bar-Shalom
Keyword(s):  
Infrared Imaging ◽  
Imaging Sensors ◽  
Forward Looking

scholarly journals Infrared Imaging Sensors: An Integrated Flexible All‐Nanowire Infrared Sensing System with Record Photosensitivity (Adv. Mater. 16/2020)

2020 ◽  
Vol 32 (16) ◽  
pp. 2070126
Author(s):  
Wenhao Ran ◽  
Lili Wang ◽  
Shufang Zhao ◽  
Depeng Wang ◽  
Ruiyang Yin ◽  
...  
Keyword(s):  
Infrared Imaging ◽  
Infrared Sensing ◽  
Sensing System ◽  
Imaging Sensors

scholarly journals Substrate-Based Near-Infrared Imaging Sensors Enable Fluorescence Lifetime Contrast via Built-in Dynamic Fluorescence Quenching Elements

ACS Sensors ◽  
2016 ◽  
Vol 1 (4) ◽  
pp. 427-436 ◽  
Author(s):  
Anand T. N. Kumar ◽  
William L. Rice ◽  
Jessica C. López ◽  
Suresh Gupta ◽  
Craig J. Goergen ◽  
...  
Keyword(s):  
Fluorescence Quenching ◽  
Fluorescence Lifetime ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Near Infrared Imaging ◽  
Imaging Sensors ◽  
Dynamic Fluorescence

scholarly journals Forward-looking infrared imaging predicts ultimate burn depth in a porcine vertical injury progression model

Burns ◽  
2016 ◽  
Vol 42 (2) ◽  
pp. 397-404 ◽  
Author(s):  
Joseph Miccio ◽  
Shruti Parikh ◽  
Xavier Marinaro ◽  
Atulya Prasad ◽  
Steven McClain ◽  
...  
Keyword(s):  
Infrared Imaging ◽  
Progression Model ◽  
Burn Depth ◽  
Injury Progression ◽  
Forward Looking

scholarly journals Discriminative Fusion Correlation Learning for Visible and Infrared Tracking

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Xiao Yun ◽  
Yanjing Sun ◽  
Xuanxuan Yang ◽  
Nannan Lu
Keyword(s):  
Infrared Imaging ◽  
Visible Spectrum ◽  
Background Information ◽  
Single Type ◽  
Computationally Efficient ◽  
Model Learning ◽  
Improve Model ◽  
Correlation Learning ◽  
Imaging Sensors ◽  
Infrared Tracking

Discriminative correlation filter- (DCF-) based trackers are computationally efficient and achieve excellent tracking in challenging applications. However, most of them suffer low accuracy and robustness due to the lack of diversity information extracted from a single type of spectral image (visible spectrum). Fusion of visible and infrared imaging sensors, one of the typical multisensor cooperation, provides complementarily useful features and consistently helps recognize the target from the background efficiently in visual tracking. Therefore, this paper proposes a discriminative fusion correlation learning model to improve DCF-based tracking performance by efficiently combining multiple features from visible and infrared images. Fusion learning filters are extracted via late fusion with early estimation, in which the performances of the filters are weighted to improve the flexibility of fusion. Moreover, the proposed discriminative filter selection model considers the surrounding background information in order to increase the discriminability of the template filters so as to improve model learning. Extensive experiments showed that the proposed method achieves superior performances in challenging visible and infrared tracking tasks.

Forward-looking infrared imaging systems characterization requirements and algorithms

1994 ◽  
Author(s):  
M. Ali Atashroo ◽  
Clay Brice ◽  
Timothy Young ◽  
Jamie Binkley
Keyword(s):  
Infrared Imaging ◽  
Imaging Systems ◽  
Forward Looking

Teledyne Imaging Sensors: infrared imaging technologies for astronomy and civil space

2008 ◽  
Author(s):  
James W. Beletic ◽  
Richard Blank ◽  
David Gulbransen ◽  
Donald Lee ◽  
Markus Loose ◽  
...  
Keyword(s):  
Infrared Imaging ◽  
Imaging Technologies ◽  
Imaging Sensors
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