5 citations · 9 across the 3 of their papers we have counts for
3 papers
cs.CV2019
Using Orthophoto for Building Boundary Sharpening in the Digital Surface Model
Xiaohu Lu, Rongjun Qin, Xu Huang
Nowadays dense stereo matching has become one of the dominant tools in 3D reconstruction of urban regions for its low cost and high flexibility in generating dense 3D points. Howev…
cs.CV2019★ 4 cited
A Comparison of Stereo-Matching Cost between Convolutional Neural Network and Census for Satellite Images
Bihe Chen, Rongjun Qin, Xu Huang +2
Stereo dense image matching can be categorized to low-level feature based matching and deep feature based matching according to their matching cost metrics. Census has been proofed…
cs.CV2017★ 5 cited
Learning to Refine Object Contours with a Top-Down Fully Convolutional Encoder-Decoder Network
Yahui Liu, Jian Yao, Li Li +2
We develop a novel deep contour detection algorithm with a top-down fully convolutional encoder-decoder network. Our proposed method, named TD-CEDN, solves two important issues in…