scholarly journals Feature Extraction and Grouping for Robot Vision Tasks

10.5772/4643 ◽  
2005 ◽  
Author(s):  
Miguel Cazorla ◽  
Francisco Escolano
Keyword(s):  
Feature Extraction ◽  
Robot Vision

Robot Vision System Based on Image Model Feature Extraction

2014 ◽  
Vol 602-605 ◽  
pp. 1485-1489
Author(s):  
Yun Zhao
Keyword(s):  
Feature Extraction ◽  
Diversity Training ◽  
Vision System ◽  
Robot Vision ◽  
Iteration Process ◽  
Extraction Process ◽  
Image Feature ◽  
Image Model ◽  
Localization Property ◽  
Wavelet Image

Algorithm encryption of wavelet image which has excellent localization property in time domain and frequency domain can locate graphic information having different directional characteristics into any precision level in certain extent. It has feature matched with human visual characteristics because of the disappearance of blocking effect and the noise. The article first analyzes the wavelet transformation principle of image model in feature extraction process and the second iteration process. Dynamic image feature extraction model can be established using this method in robot vision system. The result of envelope is more realistic and the effect is obvious. The application of the diversity training to project and contrast, the comparison precision is high and relative error is low.

An Improved Quantitative Image Analyser

1977 ◽  
Vol 35 ◽  
pp. 226-227
Author(s):  
J.P. Fallon ◽  
P.J. Gregory ◽  
C.J. Taylor
Keyword(s):  
Feature Extraction ◽  
Quantitative Analysis ◽  
Frequency Content ◽  
General Sense ◽  
Physical Measurements ◽  
Quantitative Image ◽  
Image Analyser ◽  
Automatic Methods ◽  
Quantitative Results

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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