Zobrazeno 1 - 10
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pro vyhledávání: '"David Gribben"'
Publikováno v:
IEEE Access, Vol 8, Pp 191997-192008 (2020)
Recognizing normal and anomalous events in long and complex videos with multiple sub-activities has received considerable attention in recent years. This task is more challenging than traditional action recognition in short and relatively homogeneous
Externí odkaz:
https://doaj.org/article/31e6d576e4e34a47af03ca5b8cb27130
Publikováno v:
Remote Sensing, Vol 13, Iss 16, p 3257 (2021)
To apply powerful deep-learning-based algorithms for object detection and classification in infrared videos, it is necessary to have more training data in order to build high-performance models. However, in many surveillance applications, one can hav
Externí odkaz:
https://doaj.org/article/eb138c0cacbc44e6b5ac924b96251c53
Publikováno v:
Remote Sensing, Vol 12, Iss 23, p 3880 (2020)
Accurate vegetation detection is important for many applications, such as crop yield estimation, land cover land use monitoring, urban growth monitoring, drought monitoring, etc. Popular conventional approaches to vegetation detection incorporate the
Externí odkaz:
https://doaj.org/article/cb906a5bca5f487cb571d46490ca93a1
Publikováno v:
Journal of Imaging, Vol 6, Iss 6, p 40 (2020)
Compressive video measurements can save bandwidth and data storage. However, conventional approaches to target detection require the compressive measurements to be reconstructed before any detectors are applied. This is not only time consuming but al
Externí odkaz:
https://doaj.org/article/599beb6075784c76b66806e95f48e9b4
Publikováno v:
Remote Sensing, Vol 12, Iss 9, p 1392 (2020)
Hyperspectral (HS) data have found a wide range of applications in recent years. Researchers observed that more spectral information helps land cover classification performance in many cases. However, in some practical applications, HS data may not b
Externí odkaz:
https://doaj.org/article/cddd789cd6ac4565a47fe96e07614f43
Autor:
Chiman Kwan, David Gribben
Publikováno v:
Signal & Image Processing : An International Journal. 12:01-16
It is challenging to detect vehicles in long range and low quality infrared videos using deep learning techniques such as You Only Look Once (YOLO) mainly due to small target size. This is because small targets do not have detailed texture informatio
Publikováno v:
Signal & Image Processing : An International Journal. 12:33-45
Long range infrared videos such as the Defense Systems Information Analysis Center (DSIAC) videos usually do not have high resolution. In recent years, there are significant advancement in video super-resolution algorithms. Here, we summarize our stu
Autor:
David Gribben, Chiman Kwan
Publikováno v:
Signal & Image Processing : An International Journal. 12:23-38
In our earlier target detection and classification papers, we used 8-bit infrared videos in the Defense Systems Information Analysis Center(DSIAC) video dataset. In this paper, we focus on how we can improve the target detection and classification re
Publikováno v:
IEEE Access, Vol 8, Pp 191997-192008 (2020)
Recognizing normal and anomalous events in long and complex videos with multiple sub-activities has received considerable attention in recent years. This task is more challenging than traditional action recognition in short and relatively homogeneous
Autor:
David Gribben, Mohammad Shahab Uddin, Kazi Aminul Islam, Reshad Ul Hoque, Jiang Li, Chiman Kwan
Publikováno v:
Remote Sensing
Volume 13
Issue 16
Pages: 3257
Remote Sensing, Vol 13, Iss 3257, p 3257 (2021)
Volume 13
Issue 16
Pages: 3257
Remote Sensing, Vol 13, Iss 3257, p 3257 (2021)
To apply powerful deep-learning-based algorithms for object detection and classification in infrared videos, it is necessary to have more training data in order to build high-performance models. However, in many surveillance applications, one can hav