Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Zhai, Menghua"'
Autor:
Pan, Zhanlei, Zhang, Zhenggui, Li, Junhong, Zhang, Yaopeng, Zhai, Menghua, Zhao, Wenqi, Wang, Lizhi, Li, Ao, Wang, Kunfeng, Wang, Zhanbiao
Publikováno v:
In Sustainable Production and Consumption November 2024 51:315-326
Autor:
Li, Xin, Zhang, Zhenggui, Pan, Zhanlei, Sun, Guilan, Li, Pengcheng, Chen, Jing, Wang, Lizhi, Wang, Kunfeng, Li, Ao, Li, Junhong, Zhang, Yaopeng, Zhai, Menghua, Zhao, Wenqi, Wang, Jian, Wang, Zhanbiao
Publikováno v:
In European Journal of Agronomy January 2025 162
Autor:
Zhai, Menghua, Wei, Xuewen, Pan, Zhanlei, Xu, Qinqing, Qin, Dulin, Li, Junhong, Zhang, Jie, Wang, Lizhi, Wang, Kunfeng, Duan, Xueyan, Zhang, Yaopeng, Zhao, Wenqi, Li, Ao, Zhang, Zhenggui, Wang, Zhanbiao
Publikováno v:
In Industrial Crops & Products 15 December 2024 222 Part 4
Autor:
Li, Jin, Lou, Shanwei, Gong, Jingyun, Liang, Jing, Zhang, Jungao, Zhou, Xiaoyun, Li, Jie, Wang, Li, Zhai, Menghua, Duan, Liusheng, Lei, Bin
Publikováno v:
In Plant Physiology and Biochemistry August 2024 213
Autor:
Zhai, Menghua, Salem, Tawfiq, Greenwell, Connor, Workman, Scott, Pless, Robert, Jacobs, Nathan
We propose to implicitly learn to extract geo-temporal image features, which are mid-level features related to when and where an image was captured, by explicitly optimizing for a set of location and time estimation tasks. To train our method, we tak
Externí odkaz:
http://arxiv.org/abs/1909.07499
Given a single RGB image of a complex outdoor road scene in the perspective view, we address the novel problem of estimating an occlusion-reasoned semantic scene layout in the top-view. This challenging problem not only requires an accurate understan
Externí odkaz:
http://arxiv.org/abs/1803.10870
We propose a novel convolutional neural network architecture for estimating geospatial functions such as population density, land cover, or land use. In our approach, we combine overhead and ground-level images in an end-to-end trainable neural netwo
Externí odkaz:
http://arxiv.org/abs/1708.03035
We introduce a novel strategy for learning to extract semantically meaningful features from aerial imagery. Instead of manually labeling the aerial imagery, we propose to predict (noisy) semantic features automatically extracted from co-located groun
Externí odkaz:
http://arxiv.org/abs/1612.02709
We propose a novel method for detecting horizontal vanishing points and the zenith vanishing point in man-made environments. The dominant trend in existing methods is to first find candidate vanishing points, then remove outliers by enforcing mutual
Externí odkaz:
http://arxiv.org/abs/1608.05684
The horizon line is an important contextual attribute for a wide variety of image understanding tasks. As such, many methods have been proposed to estimate its location from a single image. These methods typically require the image to contain specifi
Externí odkaz:
http://arxiv.org/abs/1604.02129