class geometry
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2011 ◽  
Vol 105 (2) ◽  
pp. 126-132 ◽  
Author(s):  
Arnold E. Perham ◽  
Faustine L. Perham

The same rain that washes out the high school baseball team's game supplies data for a class geometry project.



2008 ◽  
Vol 2008 ◽  
pp. 1-15
Author(s):  
Miao Cheng ◽  
Bin Fang ◽  
Yuan Yan Tang ◽  
Jing Wen

Face recognition is a challenging problem in computer vision and pattern recognition. Recently, many local geometrical structure-based techiniques are presented to obtain the low-dimensional representation of face images with enhanced discriminatory power. However, these methods suffer from the small simple size (SSS) problem or the high computation complexity of high-dimensional data. To overcome these problems, we propose a novel local manifold structure learning method for face recognition, named direct neighborhood discriminant analysis (DNDA), which separates the nearby samples of interclass and preserves the local within-class geometry in two steps, respectively. In addition, the PCA preprocessing to reduce dimension to a large extent is not needed in DNDA avoiding loss of discriminative information. Experiments conducted on ORL, Yale, and UMIST face databases show the effectiveness of the proposed method.



1984 ◽  
Vol 19 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Thomas Ihringer


1982 ◽  
Vol 180 (2) ◽  
pp. 395-411 ◽  
Author(s):  
Thomas Ihringer


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