Detecting Foreground Components in Grey Level Images for Shift Invariant and Topology Preserving Pyramids

2004 ◽  
pp. 57-64 ◽  
Author(s):  
Giuliana Ramella ◽  
Gabriella Sanniti di Baja
Keyword(s):  
Grey Level ◽  
And Topology

Geometry and Topology

2005 ◽  
Author(s):  
Miles Reid ◽  
Balazs Szendroi
Keyword(s):  
And Topology

Diffraction in crystals and topology

1991 ◽  
Vol 1 (8) ◽  
pp. 1187-1193 ◽  
Author(s):  
V. E. Dmitrienko
Keyword(s):  
And Topology

NEW PHASES OF QUANTUM MATTER AND TOPOLOGY

2016 ◽  
pp. 63-73
Author(s):  
I.V. Krive ◽  
◽  
S.I. Shevchenko ◽  
Keyword(s):  
Quantum Matter ◽  
And Topology

Characterization of Pancreas at Diabetic Patients in CT images using Texture Analysis

2017 ◽  
Vol 7 (7) ◽  
pp. 8
Author(s):  
Mona E. Elbashier ◽  
Suhaib Alameen ◽  
Caroline Edward Ayad ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Size Distribution ◽  
Texture Analysis ◽  
Classification Accuracy ◽  
Ct Images ◽  
Diabetic Patients ◽  
Gray Level ◽  
Grey Level ◽  
Run Length ◽  
Interactive Data

This study concern to characterize the pancreas areato head, body and tail using Gray Level Run Length Matrix (GLRLM) and extract classification features from CT images. The GLRLM techniques included eleven’s features. To find the gray level distribution in CT images it complements the GLRLM features extracted from CT images with runs of gray level in pixels and estimate the size distribution of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level distribution of images. The results show that the Gray Level Run Length Matrix and  features give classification accuracy of pancreashead 89.2%, body 93.6 and the tail classification accuracy 93.5%. The overall classification accuracy of pancreas area 92.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate pancreas area names.

Topography and topology optimization of diesel engine components for light-weight design in the automotive industry

2019 ◽  
Vol 61 (1) ◽  
pp. 27-34 ◽  
Author(s):  
Ali Rıza Yıldız ◽  
Ulaş Aytaç Kılıçarpa ◽  
Emre Demirci ◽  
Mesut Doğan
Keyword(s):  
Topology Optimization ◽  
Diesel Engine ◽  
Automotive Industry ◽  
Light Weight ◽  
And Topology ◽  
Weight Design ◽  
Engine Components
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