Fault classification method for inverter based on hybrid support vector machines and wavelet analysis

2011 ◽  
Vol 9 (4) ◽  
pp. 797-804 ◽  
Author(s):  
Zhi-kun Hu ◽  
Wei-hua Gui ◽  
Chun-hua Yang ◽  
Peng-cheng Deng ◽  
Steven X. Ding
Keyword(s):  
Support Vector Machines ◽  
Wavelet Analysis ◽  
Classification Method ◽  
Fault Classification ◽  
Support Vector ◽  
Vector Machines

scholarly journals Contact Lens Classification by Using Segmented Lens Boundary Features

2018 ◽  
Vol 11 (3) ◽  
pp. 1129
Author(s):  
Nur Ariffin Mohd Zin ◽  
Hishammuddin Asmuni ◽  
Haza Nuzly Abdul Hamed ◽  
Razib M. Othman ◽  
Shahreen Kasim ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Contact Lens ◽  
State Of The Art ◽  
Classification Method ◽  
Support Vector ◽  
Local Descriptors ◽  
Iris Image ◽  
Vector Machines ◽  
False Reject Rate ◽  
Better Than

Recent studies have shown that the wearing of soft lens may lead to performance degradation with the increase of false reject rate. However, detecting the presence of soft lens is a non-trivial task as its texture that almost indiscernible. In this work, we proposed a classification method to identify the existence of soft lens in iris image. Our proposed method starts with segmenting the lens boundary on top of the sclera region. Then, the segmented boundary is used as features and extracted by local descriptors. These features are then trained and classified using Support Vector Machines. This method was tested on Notre Dame Cosmetic Contact Lens 2013 database. Experiment showed that the proposed method performed better than state of the art methods.

Coupling Pairwise Support Vector Machines for Fault Classification

2003 ◽  
Vol 36 (5) ◽  
pp. 657-662
Author(s):  
Sanna Pöyhönen ◽  
Antero Arkkio ◽  
Heikki Hyötyniemi
Keyword(s):  
Support Vector Machines ◽  
Fault Classification ◽  
Support Vector ◽  
Vector Machines
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