Measuring geometric parameters using image processing and diffractive optics methods

2002 ◽  
Author(s):  
Victor A. Soifer ◽  
Victor V. Kotlyar ◽  
Svetlana N. Khonina ◽  
Alexander G. Khramov ◽  
N. Y. Ilyasova
Keyword(s):  
Image Processing ◽  
Geometric Parameters ◽  
Diffractive Optics

Image processing and geometric parameters extracted from sliced image of porous biomaterial

2007 ◽  
Vol 18 (2) ◽  
pp. 187-213 ◽  
Author(s):  
Z. Yangi ◽  
X.F. Peng ◽  
C.P. Chu ◽  
D.J. Lee
Keyword(s):  
Image Processing ◽  
Geometric Parameters

Use of maps to extract geometric parameters of cities based image processing

2016 ◽  
Author(s):  
N David Hernandez-Abreu ◽  
Ismael Quevedo-Medina ◽  
Carlos M. Travieso ◽  
Malay Kishore Dutta ◽  
Anushikha Singh
Keyword(s):  
Image Processing ◽  
Geometric Parameters

scholarly journals Discriminative Power of Geometric Parameters of Different Cultivars of Sour Cherry Pits Determined Using Machine Learning

Agriculture ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1212
Author(s):  
Ewa Ropelewska ◽  
Kadir Sabanci ◽  
Muhammet Fatih Aslan
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Sour Cherry ◽  
Geometric Parameters ◽  
Discriminative Power ◽  
Shape Factors ◽  
Practical Applications ◽  
Discriminative Models ◽  
Linear Dimensions

The aim of this study was to develop models based on linear dimensions or shape factors, and the sets of combined linear dimensions and shape factors for discrimination of sour cherry pits of different cultivars (‘Debreceni botermo’, ‘Łutówka’, ‘Nefris’, ‘Kelleris’). The geometric parameters were calculated using image processing. The pits of different sour cherry cultivars statistically significantly differed in terms of selected dimensions and shape factors. The discriminative models built based on linear dimensions produced average accuracies of up to 95% for distinguishing the pit cultivars in the case of ‘Nefris’ vs. ‘Kelleris’ and 72% for all four cultivars. The average accuracies for the discriminative models built based on shape factors were up to 95% for the ‘Nefris’ and ‘Kelleris’ pits and 73% for four cultivars. The models combining the linear dimensions and shape factors produced accuracies reaching 96% for the ‘Nefris’ vs. ‘Kelleris’ pits and 75% for all cultivars. The geometric parameters with high discriminative power may be used for distinguishing different cultivars of sour cherry pits. It can be of great importance for practical applications. It may allow avoiding the adulteration and mixing of different cultivars.

Optimization of liquid crystal displays behavior in optical image processing and in diffractive optics

2001 ◽  
Author(s):  
Juan Campos ◽  
Andres Marquez ◽  
Josep Nicolas ◽  
Ignacio Soriano Moreno ◽  
Claudio C. Iemmi ◽  
...  
Keyword(s):  
Image Processing ◽  
Liquid Crystal ◽  
Liquid Crystal Displays ◽  
Optical Image ◽  
Diffractive Optics ◽  
Optical Image Processing

Image processing method for multicore fiber geometric parameters

2016 ◽  
Vol 29 ◽  
pp. 84-89 ◽  
Author(s):  
Chuanbiao Zhang ◽  
Tigang Ning ◽  
Jing Li ◽  
Chao Li ◽  
Shaoshuo Ma
Keyword(s):  
Image Processing ◽  
Processing Method ◽  
Geometric Parameters ◽  
Image Processing Method ◽  
Multicore Fiber

Research on FAE Cloud Image Processing Method Based on Background Subtraction and Region Growing

2018 ◽  
Vol 32 (07) ◽  
pp. 1854019
Author(s):  
Fang Wang ◽  
Yi-Zhao Li ◽  
Li-Ping Li ◽  
De-Ren Kong
Keyword(s):  
Image Processing ◽  
High Precision ◽  
Background Subtraction ◽  
Region Growing ◽  
Processing Method ◽  
Geometric Parameters ◽  
Subtraction Method ◽  
Image Processing Method ◽  
Integrate Area

After the first initiation, the Fuel Air Explosive (FAE) cloud formed through fuel explosion dispersal and it will generate tremendous damaging power after being detonated the second time. As the damaging power is closely related to the determination of reinitiation time, it is of great significance to study the growth principle of FAE cloud by means of analyzing FAE cloud images. Combining with background subtraction and region growing, an improved region growing image processing method was proposed, in which the seeds of region growing abstracted through background subtraction method and the growing criterion was modified. With this method, the integrate area of cloud can be obtained for extracting geometric parameters. Experiments were carried out on both cloudy and sunny days, and image overlap score was used to quantitatively evaluate the accuracy of images segmentation. The result indicated that this image processing method has advantages of high precision and robustness. In addition, the computation burden is reduced significantly compared with traditional region growing method.

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