Optimizing Resolution for Feature Extraction in Robotic Motion Learning

Author(s):  
M. Kato ◽  
Y. Kobayashi ◽  
S. Hosoe
2007 ◽  
Vol 25 (5) ◽  
pp. 770-778 ◽  
Author(s):  
Yuichi Kobayashi ◽  
Masato Kato ◽  
Shigeyuki Hosoe

2018 ◽  
Vol 8 (2) ◽  
pp. 241 ◽  
Author(s):  
Rachael Burns ◽  
Myounghoon Jeon ◽  
Chung Park

Author(s):  
Daisuke Uragami ◽  
Tatsuji Takahashi ◽  
Hisham Alsubeheen ◽  
Akinori Sekiguchi ◽  
Yoshiki Matsuo

Author(s):  
J.P. Fallon ◽  
P.J. Gregory ◽  
C.J. Taylor

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.


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