Feature extraction using constrained maximum variance mapping

2008 ◽  
Vol 41 (11) ◽  
pp. 3287-3294 ◽  
Author(s):  
Bo Li ◽  
De-Shuang Huang ◽  
Chao Wang ◽  
Kun-Hong Liu
2011 ◽  
Vol 21 (8) ◽  
pp. 1827-1833 ◽  
Author(s):  
Jin Liu ◽  
Bo Li ◽  
Wen-Sheng Zhang

2014 ◽  
Vol 633-634 ◽  
pp. 372-376
Author(s):  
Bing Xiang Liu ◽  
Feng Qin Wang ◽  
Cheng Le Yu

This paper presents a method for inter-class ceramic crack detection threshold than the maximum variance within the class-based segmentation. Ceramics crack detection methods are mainly obtained by ceramic image data, image pre-processing, image segmentation, feature extraction and object recognition constitutes five links. Experimental results show that the method can be detected quickly and accurately detect whether the standard ceramic.


Author(s):  
XI CHEN ◽  
JIASHU ZHANG

This paper, presents a novel unsupervised dimensionality reduction approach called variance difference embedding (VDE) for facial feature extraction. The proposed VDE method is derived from maximizing the difference between global variance and local variance, so it can draw the close samples closer and simultaneously making the mutually distant samples even more distant from each other. VDE utilizes the maximum variance difference criterion rather than the generalized Rayleigh quotient as a class separability measure, thereby avoiding the singularity problem when addressing the sample size problem. The results of the experiments conducted on ORL database, Yale database and a subset of PIE database indicate the effectiveness of the proposed VDE method on facial feature extraction and classification.


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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