Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Qingdi Wei"'
Publikováno v:
IEEE Access, Vol 8, Pp 599-610 (2020)
Accurate land use classification results play an important role in scientific research and production practice. The existing neural network structure contains many pooling layers. Although the model incorporated spatial location information when extr
Externí odkaz:
https://doaj.org/article/0c64dd885b984c3ca5f4a76200d52c91
Publikováno v:
2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP).
Despite the effectiveness in extracting information from images, the existing neural network model is not suitable for extracting cultivated land information from high resolution remote sensing images (HRRSIs). In this paper, three effective improvem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0a4f429413807b778bd162652b94f356
https://doi.org/10.1109/icsidp47821.2019.9173010
https://doi.org/10.1109/icsidp47821.2019.9173010
Autor:
Shouyi Wang, Shuai Gao, Yan Chen, Qingdi Wei, Xiaoxia Yang, Fan Yu, Dejuan Song, Chengming Zhang
When extract building from high resolution remote sensing image with meter/sub-meter accuracy, the shade of trees and interference of roads are the main factors of reducing the extraction accuracy. Proposed a Bayesian Convolutional Neural Networks(BC
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e219b88ece1c209b98b905304edd0945
Action recognition is one of the most active research fields in computer vision. This chapter first reviews the action recognition methods in literature from two aspects: action representation and recognition strategy. Then, a novel method for classi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e99bb0fe20fa19100f7aa9cc3029cb8a
https://doi.org/10.4018/978-1-60566-900-7.ch012
https://doi.org/10.4018/978-1-60566-900-7.ch012
Publikováno v:
Advances in Visual Computing ISBN: 9783642105197
ISVC (2)
ISVC (2)
Human activity recognition is attracting a lot of attention in the computer vision domain. In this paper we present a novel human activity recognition method based on $\Re$ transform and Fourier Mellin Transform (FMT). Firstly, we convert the origina
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b48268897da8ef7cfbdfdf9aa6bc214f
https://doi.org/10.1007/978-3-642-10520-3_60
https://doi.org/10.1007/978-3-642-10520-3_60
Publikováno v:
Advances in Visual Computing ISBN: 9783642105197
ISVC (2)
ISVC (2)
Group action recognition is a challenging task in computer vision due to the large complexity induced by multiple motion patterns. This paper aims at analyzing group actions in video clips containing several activities. We combine the probability sum
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5454385970916b7df7095dd29ee10931
https://doi.org/10.1007/978-3-642-10520-3_72
https://doi.org/10.1007/978-3-642-10520-3_72
Publikováno v:
ICPR
Object tracking is one of the most important tasks in computer vision. The unscented particle filter algorithm has been extensively used to tackle this problem and achieved a great success, because it uses the UKF (unscented Kalman filter) to generat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::498e243e896d61b62f48511009fb8a5c
https://doi.org/10.1109/icpr.2008.4761153
https://doi.org/10.1109/icpr.2008.4761153
Publikováno v:
ICPR
Group action recognition in soccer videos is a challenging problem due to the difficulties of group action representation and camera motion estimation. This paper presents a novel approach for recognizing group action with a moving camera. In our app
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1c49ed0287497d0a89dc2e052afb229a
https://doi.org/10.1109/icpr.2008.4761001
https://doi.org/10.1109/icpr.2008.4761001
Publikováno v:
ICIP (6)
Action recognition is one of the most active research fields in computer vision. In this paper, we propose a novel method for classifying human actions in a series of image sequences containing certain actions. Human action in image sequences can be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c47508b3b29472ff0a7bfd7299d0f863
https://doi.org/10.1109/icip.2007.4379539
https://doi.org/10.1109/icip.2007.4379539
Publikováno v:
2010 17th IEEE International Conference on Image Processing (ICIP); 2010, p3805-3808, 4p