Trefoil Aberration Measurement of Lithographic Projection Optics Based on Linewidth Asymmetry of the Aerial Image

2019 ◽  
Vol 18 (1) ◽  
pp. 373-379
Author(s):  
Qiongyan Yuan ◽  
Xiangzhao Wang ◽  
Zicheng Qiu
Keyword(s):  
Aerial Image ◽  
Projection Optics

Trefoil aberration measurement of lithographic projection optics based on linewidth asymmetry of the aerial image

Optik ◽  
2010 ◽  
Vol 121 (19) ◽  
pp. 1739-1742 ◽  
Author(s):  
Qiongyan Yuan ◽  
Xiangzhao Wang ◽  
Zicheng Qiu
Keyword(s):  
Aerial Image ◽  
Projection Optics

scholarly journals A nonlinear measurement method of polarization aberration in immersion projection optics by spectrum analysis of aerial image

2018 ◽  
Vol 26 (25) ◽  
pp. 32743 ◽  
Author(s):  
Enze Li ◽  
Yanqiu Li ◽  
Naiyuan Sheng ◽  
Tie Li ◽  
Yiyu Sun ◽  
...  
Keyword(s):  
Spectrum Analysis ◽  
Measurement Method ◽  
Aerial Image ◽  
Projection Optics

scholarly journals Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

2019 ◽  
Vol 11 (10) ◽  
pp. 1157 ◽  
Author(s):  
Jorge Fuentes-Pacheco ◽  
Juan Torres-Olivares ◽  
Edgar Roman-Rangel ◽  
Salvador Cervantes ◽  
Porfirio Juarez-Lopez ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Image Data ◽  
Ground Truth ◽  
Aerial Images ◽  
Aerial Image ◽  
Data Set ◽  
Visual Appearance ◽  
Aerial Robots ◽  
Lighting Conditions ◽  
Convolutional Encoder

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

Sign in / Sign up

Export Citation Format

Share Document