Exposure latitude analysis for dense line and space patterns by using diffused aerial image model

2000 ◽  
Author(s):  
Chang-Nam Ahn ◽  
Hee-Bom Kim ◽  
Ki-Ho Baik
Keyword(s):  
Aerial Image ◽  
Image Model ◽  
Dense Line

Accuracy of diffused aerial image model for full-chip-level optical proximity correction

2000 ◽  
Author(s):  
Jee-Suk Hong ◽  
Hee-Bom Kim ◽  
Hyoung-Soon Yune ◽  
Chang-Nam Ahn ◽  
Young-Mo Koo ◽  
...  
Keyword(s):  
Aerial Image ◽  
Image Model ◽  
Optical Proximity Correction

Aerial image model and application to aberration measurement

2010 ◽  
Author(s):  
Anatoly Y. Bourov ◽  
Liang Li ◽  
Zhiyong Yang ◽  
Fan Wang ◽  
Lifeng Duan
Keyword(s):  
Aerial Image ◽  
Image Model

Aerial image of 3D phase-shifted reticle: 3D fast aerial image model

1994 ◽  
Author(s):  
Eytan Barouch ◽  
Uwe Hollerbach ◽  
Steven A. Orszag
Keyword(s):  
Aerial Image ◽  
Image Model

Computational Micrograph Registration with Sieve Processes

1996 ◽  
Vol 54 ◽  
pp. 440-441
Author(s):  
P.J. Phillips ◽  
J. Huang ◽  
S. M. Dunn
Keyword(s):  
Planar Graph ◽  
Efficient Algorithm ◽  
Spatial Organization ◽  
Spatial Arrangement ◽  
Stereo Image ◽  
Image Model ◽  
Geometric Invariants ◽  
Point Set

In this paper we present an efficient algorithm for automatically finding the correspondence between pairs of stereo micrographs, the key step in forming a stereo image. The computation burden in this problem is solving for the optimal mapping and transformation between the two micrographs. In this paper, we present a sieve algorithm for efficiently estimating the transformation and correspondence.In a sieve algorithm, a sequence of stages gradually reduce the number of transformations and correspondences that need to be examined, i.e., the analogy of sieving through the set of mappings with gradually finer meshes until the answer is found. The set of sieves is derived from an image model, here a planar graph that encodes the spatial organization of the features. In the sieve algorithm, the graph represents the spatial arrangement of objects in the image. The algorithm for finding the correspondence restricts its attention to the graph, with the correspondence being found by a combination of graph matchings, point set matching and geometric invariants.

Explanation of Color Lines Based on a Simple Color Image Model

2018 ◽  
Vol 2018 (16) ◽  
pp. 296-1-296-5
Author(s):  
Megan M. Fuller ◽  
Jae S. Lim
Keyword(s):  
Color Image ◽  
Image Model

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.

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