Mumford-Shah Model Based Man-Made Objects Detection from Aerial Images

2005 ◽  
pp. 386-395 ◽  
Author(s):  
Guo Cao ◽  
Xin Yang ◽  
Dake Zhou
Keyword(s):  
Aerial Images ◽  
Model Based ◽  
Objects Detection

Model-Based 3D Reconstruction of Buildings from Multiple Aerial Images

Annals of GIS ◽  
2002 ◽  
Vol 8 (1) ◽  
pp. 16-23
Author(s):  
Yi-Hsing Tseng ◽  
Sendo Wang
Keyword(s):  
3D Reconstruction ◽  
Aerial Images ◽  
Model Based

scholarly journals Axis Learning for Orientated Objects Detection in Aerial Images

2020 ◽  
Vol 12 (6) ◽  
pp. 908 ◽  
Author(s):  
Zhifeng Xiao ◽  
Linjun Qian ◽  
Weiping Shao ◽  
Xiaowei Tan ◽  
Kai Wang
Keyword(s):  
Computational Complexity ◽  
Aspect Ratio ◽  
Object Detection ◽  
Aerial Images ◽  
Aerial Image ◽  
Large Aspect Ratio ◽  
Computationally Efficient ◽  
Feature Maps ◽  
One Stage ◽  
Objects Detection

Orientated object detection in aerial images is still a challenging task due to the bird’s eye view and the various scales and arbitrary angles of objects in aerial images. Most current methods for orientated object detection are anchor-based, which require considerable pre-defined anchors and are time consuming. In this article, we propose a new one-stage anchor-free method to detect orientated objects in per-pixel prediction fashion with less computational complexity. Arbitrary orientated objects are detected by predicting the axis of the object, which is the line connecting the head and tail of the object, and the width of the object is vertical to the axis. By predicting objects at the pixel level of feature maps directly, the method avoids setting a number of hyperparameters related to anchor and is computationally efficient. Besides, a new aspect-ratio-aware orientation centerness method is proposed to better weigh positive pixel points, in order to guide the network to learn discriminative features from a complex background, which brings improvements for large aspect ratio object detection. The method is tested on two common aerial image datasets, achieving better performance compared with most one-stage orientated methods and many two-stage anchor-based methods with a simpler procedure and lower computational complexity.

Model-Based Matching of Line Segments in Aerial Images

1985 ◽  
Author(s):  
Z Chen ◽  
K H. Tsai ◽  
K R. Lu ◽  
Y. S. Ou
Keyword(s):  
Aerial Images ◽  
Line Segments ◽  
Model Based
Sign in / Sign up

Export Citation Format

Share Document