A Classification Method of Traditional Decor Pattern Based on Support Vector Machines Approach and Gray Level Co-occurrence Matrix with Mean and F-Score

2019 ◽  
Author(s):  
Dhendra Marutho ◽  
Muljono
Keyword(s):  
Support Vector Machines ◽  
Classification Method ◽  
Support Vector ◽  
Gray Level ◽  
Vector Machines ◽  
Occurrence Matrix

Particulate matter characterization by gray level co-occurrence matrix based support vector machines

2012 ◽  
Vol 223-224 ◽  
pp. 94-103 ◽  
Author(s):  
K. Manivannan ◽  
P. Aggarwal ◽  
V. Devabhaktuni ◽  
A. Kumar ◽  
D. Nims ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Support Vector Machines ◽  
Support Vector ◽  
Gray Level ◽  
Vector Machines ◽  
Occurrence Matrix

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.

Detection of Closure Features in Gray-Level Images by Using Support Vector Machines

2005 ◽  
Vol 64 (11) ◽  
pp. 923-929
Author(s):  
Mario A. Ibarra-Manzano ◽  
J. Gabriel Avina-Cervantes ◽  
Dora L. Almanza-Ojeda ◽  
Jose Ruiz-Pinales
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Gray Level ◽  
Vector Machines ◽  
Gray Level Images

MULTIPLE DECISION TEMPLATES WITH ADAPTIVE FEATURES FOR FINGERPRINT CLASSIFICATION

2007 ◽  
Vol 21 (08) ◽  
pp. 1323-1338 ◽  
Author(s):  
JUN-KI MIN ◽  
SUNG-BAE CHO
Keyword(s):  
Support Vector Machines ◽  
Feature Vector ◽  
Experimental Results ◽  
Classification Method ◽  
Support Vector ◽  
Fingerprint Classification ◽  
Vector Machines ◽  
Fingerprint Database

This paper proposes a novel fingerprint classification method using multiple decision templates of Support Vector Machines (SVMs) with adaptive features. In order to overcome intra-class and inter-class ambiguities of fingerprints, the proposed method extracts a feature vector from an adaptively detected feature region and classifies the feature vector using SVMs. The outputs of the SVMs are then combined by multiple decision templates that make several per class. Experimental results on NIST4 fingerprint database revealed the effectiveness and validity of the proposed method for fingerprint classification.

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